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Article Contents

Introduction, what is fieldwork, purpose of fieldwork, physical safety, mental wellbeing and affect, ethical considerations, remote fieldwork, concluding thoughts, acknowledgments, funder information.

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Field Research: A Graduate Student's Guide

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Ezgi Irgil, Anne-Kathrin Kreft, Myunghee Lee, Charmaine N Willis, Kelebogile Zvobgo, Field Research: A Graduate Student's Guide, International Studies Review , Volume 23, Issue 4, December 2021, Pages 1495–1517, https://doi.org/10.1093/isr/viab023

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What is field research? Is it just for qualitative scholars? Must it be done in a foreign country? How much time in the field is “enough”? A lack of disciplinary consensus on what constitutes “field research” or “fieldwork” has left graduate students in political science underinformed and thus underequipped to leverage site-intensive research to address issues of interest and urgency across the subfields. Uneven training in Ph.D. programs has also left early-career researchers underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust, and related issues of funding, physical safety, mental health, research ethics, and crisis response. Based on the experience of five junior scholars, this paper offers answers to questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” This practical guide engages theory and praxis, in support of an epistemologically and methodologically pluralistic discipline.

¿Qué es la investigación de campo? ¿Es solo para académicos cualitativos? ¿Debe realizarse en un país extranjero? ¿Cuánto tiempo en el terreno es “suficiente”? La falta de consenso disciplinario con respecto a qué constituye la “investigación de campo” o el “trabajo de campo” ha causado que los estudiantes de posgrado en ciencias políticas estén poco informados y, por lo tanto, capacitados de manera insuficiente para aprovechar la investigación exhaustiva en el sitio con el objetivo de abordar los asuntos urgentes y de interés en los subcampos. La capacitación desigual en los programas de doctorado también ha provocado que los investigadores en las primeras etapas de su carrera estén poco preparados para la logística del trabajo de campo, desde desarrollar redes y estrategias de muestreo efectivas hasta generar la confianza de las personas que facilitan la información, y las cuestiones relacionadas con la financiación, la seguridad física, la salud mental, la ética de la investigación y la respuesta a las situaciones de crisis. Con base en la experiencia de cinco académicos novatos, este artículo ofrece respuestas a las preguntas que desconciertan a los estudiantes de posgrado, a menudo, sin el beneficio de las “lecciones aprendidas” de otras personas. Esta guía práctica incluye teoría y praxis, en apoyo de una disciplina pluralista desde el punto de vista epistemológico y metodológico.

En quoi consiste la recherche de terain ? Est-elle uniquement réservée aux chercheurs qualitatifs ? Doit-elle être effectuée dans un pays étranger ? Combien de temps faut-il passer sur le terrain pour que ce soit « suffisant » ? Le manque de consensus disciplinaire sur ce qui constitue une « recherche de terrain » ou un « travail de terrain » a laissé les étudiants diplômés en sciences politiques sous-informés et donc sous-équipés pour tirer parti des recherches de terrain intensives afin d'aborder les questions d'intérêt et d'urgence dans les sous-domaines. L'inégalité de formation des programmes de doctorat a mené à une préparation insuffisante des chercheurs en début de carrière à la logistique du travail de terrain, qu'il s'agisse du développement de réseaux et de stratégies d’échantillonnage efficaces, de l'acquisition de la confiance des personnes interrogées ou des questions de financement, de sécurité physique, de santé mentale, d’éthique de recherche et de réponse aux crises qui y sont associées. Cet article s'appuie sur l'expérience de cinq jeunes chercheurs pour proposer des réponses aux questions que les étudiants diplômés se posent, souvent sans bénéficier des « enseignements tirés » par les autres. Ce guide pratique engage théorie et pratique en soutien à une discipline épistémologiquement et méthodologiquement pluraliste.

Days before embarking on her first field research trip, a Ph.D. student worries about whether she will be able to collect the qualitative data that she needs for her dissertation. Despite sending dozens of emails, she has received only a handful of responses to her interview requests. She wonders if she will be able to gain more traction in-country. Meanwhile, in the midst of drafting her thesis proposal, an M.A. student speculates about the feasibility of his project, given a modest budget. Thousands of miles away from home, a postdoc is concerned about their safety, as protests erupt outside their window and state security forces descend into the streets.

These anecdotes provide a small glimpse into the concerns of early-career researchers undertaking significant projects with a field research component. Many of these fieldwork-related concerns arise from an unfortunate shortage in curricular offerings for qualitative and mixed-method research in political science graduate programs ( Emmons and Moravcsik 2020 ), 1 as well as the scarcity of instructional materials for qualitative and mixed-method research, relative to those available for quantitative research ( Elman, Kapiszewski, and Kirilova 2015 ; Kapiszewski, MacLean, and Read 2015 ; Mosley 2013 ). A recent survey among the leading United States Political Science programs in Comparative Politics and International Relations found that among graduate students who have carried out international fieldwork, 62 percent had not received any formal fieldwork training and only 20 percent felt very or mostly prepared for their fieldwork ( Schwartz and Cronin-Furman 2020 , 7–8). This shortfall in training and instruction means that many young researchers are underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust. In addition, there is a notable lack of preparation around issues of funding, physical safety, mental health, research ethics, and crisis response. This is troubling, as field research is highly valued and, in some parts of the field, it is all but expected, for instance in comparative politics.

Beyond subfield-specific expectations, research that leverages multiple types of data and methods, including fieldwork, is one of the ways that scholars throughout the discipline can more fully answer questions of interest and urgency. Indeed, multimethod work, a critical means by which scholars can parse and evaluate causal pathways, is on the rise ( Weller and Barnes 2016 ). The growing appearance of multimethod research in leading journals and university presses makes adequate training and preparation all the more significant ( Seawright 2016 ; Nexon 2019 ).

We are five political scientists interested in providing graduate students and other early-career researchers helpful resources for field research that we lacked when we first began our work. Each of us has recently completed or will soon complete a Ph.D. at a United States or Swedish university, though we come from many different national backgrounds. We have conducted field research in our home countries and abroad. From Colombia and Guatemala to the United States, from Europe to Turkey, and throughout East and Southeast Asia, we have spanned the globe to investigate civil society activism and transitional justice in post-violence societies, conflict-related sexual violence, social movements, authoritarianism and contentious politics, and the everyday politics and interactions between refugees and host-country citizens.

While some of us have studied in departments that offer strong training in field research methods, most of us have had to self-teach, learning through trial and error. Some of us have also been fortunate to participate in short courses and workshops hosted by universities such as the Consortium for Qualitative Research Methods and interdisciplinary institutions such as the Peace Research Institute Oslo. Recognizing that these opportunities are not available to or feasible for all, and hoping to ease the concerns of our more junior colleagues, we decided to compile our experiences and recommendations for first-time field researchers.

Our experiences in the field differ in several key respects, from the time we spent in the field to the locations we visited, and how we conducted our research. The diversity of our experiences, we hope, will help us reach and assist the broadest possible swath of graduate students interested in field research. Some of us have spent as little as ten days in a given country or as much as several months, in some instances visiting a given field site location just once and in other instances returning several times. At times, we have been able to plan weeks and months in advance. Other times, we have quickly arranged focus groups and impromptu interviews. Other times still, we have completed interviews virtually, when research participants were in remote locations or when we ourselves were unable to travel, of note during the coronavirus pandemic. We have worked in countries where we are fluent or have professional proficiency in the language, and in countries where we have relied on interpreters. We have worked in settings with precarious security as well as in locations that feel as comfortable as home. Our guide is not intended to be prescriptive or exhaustive. What we offer is a set of experience-based suggestions to be implemented as deemed relevant and appropriate by the researcher and their advisor(s).

In terms of the types of research and data sources and collection, we have conducted archival research, interviews, focus groups, and ethnographies with diplomats, bureaucrats, military personnel, ex-combatants, civil society advocates, survivors of political violence, refugees, and ordinary citizens. We have grappled with ethical dilemmas, chief among them how to get useful data for our research projects in ways that exceed the minimal standards of human subjects’ research evaluation panels. Relatedly, we have contemplated how to use our platforms to give back to the individuals and communities who have so generously lent us their time and knowledge, and shared with us their personal and sometimes harrowing stories.

Our target audience is first and foremost graduate students and early-career researchers who are interested in possibly conducting fieldwork but who either (1) do not know the full potential or value of fieldwork, (2) know the potential and value of fieldwork but think that it is excessively cost-prohibitive or otherwise infeasible, or (3) who have the interest, the will, and the means but not necessarily the know-how. We also hope that this resource will be of value to graduate programs, as they endeavor to better support students interested in or already conducting field research. Further, we target instructional faculty and graduate advisors (and other institutional gatekeepers like journal and book reviewers), to show that fieldwork does not have to be year-long, to give just one example. Instead, the length of time spent in the field is a function of the aims and scope of a given project. We also seek to formalize and normalize the idea of remote field research, whether conducted because of security concerns in conflict zones, for instance, or because of health and safety concerns, like the Covid-19 pandemic. Accordingly, researchers in the field for shorter stints or who conduct fieldwork remotely should not be penalized.

We note that several excellent resources on fieldwork such as the bibliography compiled by Advancing Conflict Research (2020) catalogue an impressive list of articles addressing questions such as ethics, safety, mental health, reflexivity, and methods. Further resources can be found about the positionality of the researcher in the field while engaging vulnerable communities, such as in the research field of migration ( Jacobsen and Landau 2003 ; Carling, Bivand Erdal, and Ezzati 2014 ; Nowicka and Cieslik 2014 ; Zapata-Barrero and Yalaz 2019 ). However, little has been written beyond conflict-affected contexts, fragile settings, and vulnerable communities. Moreover, as we consulted different texts and resources, we found no comprehensive guide to fieldwork explicitly written with graduate students in mind. It is this gap that we aim to fill.

In this paper, we address five general categories of questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” First, What is field research? Is it just for qualitative scholars? Must it be conducted in a foreign country? How much time in the field is “enough”? Second, What is the purpose of fieldwork? When does it make sense to travel to a field site to collect data? How can fieldwork data be used? Third, What are the nuts and bolts? How does one get ready and how can one optimize limited time and financial resources? Fourth, How does one conduct fieldwork safely? What should a researcher do to keep themselves, research assistants, and research subjects safe? What measures should they take to protect their mental health? Fifth, How does one conduct ethical, beneficent field research?

Finally, the Covid-19 pandemic has impressed upon the discipline the volatility of research projects centered around in-person fieldwork. Lockdowns and closed borders left researchers sequestered at home and unable to travel, forced others to cut short any trips already begun, and unexpectedly confined others still to their fieldwork sites. Other factors that may necessitate a (spontaneous) readjustment of planned field research include natural disasters, a deteriorating security situation in the field site, researcher illness, and unexpected changes in personal circumstances. We, therefore, conclude with a section on the promise and potential pitfalls of remote (or virtual) fieldwork. Throughout this guide, we engage theory and praxis to support an epistemologically and methodologically pluralistic discipline.

The concept of “fieldwork” is not well defined in political science. While several symposia discuss the “nuts and bolts” of conducting research in the field within the pages of political science journals, few ever define it ( Ortbals and Rincker 2009 ; Hsueh, Jensenius, and Newsome 2014 ). Defining the concept of fieldwork is important because assumptions about what it is and what it is not underpin any suggestions for conducting it. A lack of disciplinary consensus about what constitutes “fieldwork,” we believe, explains the lack of a unified definition. Below, we discuss three areas of current disagreement about what “fieldwork” is, including the purpose of fieldwork, where it occurs, and how long it should be. We follow this by offering our definition of fieldwork.

First, we find that many in the discipline view fieldwork as squarely in the domain of qualitative research, whether interpretivist or positivist. However, field research can also serve quantitative projects—for example, by providing crucial context, supporting triangulation, or illustrating causal mechanisms. For instance, Kreft (2019) elaborated her theory of women's civil society mobilization in response to conflict-related sexual violence based on interviews she carried out in Colombia. She then examined cross-national patterns through statistical analysis. Conversely, Willis's research on the United States military in East Asia began with quantitative data collection and analysis of protest events before turning to fieldwork to understand why protests occurred in some instances but not others. Researchers can also find quantifiable data in the field that is otherwise unavailable to them at home ( Read 2006 ; Chambers-Ju 2014 ; Jensenius 2014 ). Accordingly, fieldwork is not in the domain of any particular epistemology or methodology as its purpose is to acquire data for further information.

Second, comparative politics and international relations scholars often opine that fieldwork requires leaving the country in which one's institution is based. Instead, we propose that what matters most is the nature of the research project, not the locale. For instance, some of us in the international relations subfield have interviewed representatives of intergovernmental organizations (IGOs) and international nongovernmental organizations (INGOs), whose headquarters are generally located in Global North countries. For someone pursuing a Ph.D. in the United States and writing on transnational advocacy networks, interviews with INGO representatives in New York certainly count as fieldwork ( Zvobgo 2020 ). Similarly, a graduate student who returns to her home country to interview refugees and native citizens is conducting a field study as much as a researcher for whom the context is wholly foreign. Such interviews can provide necessary insights and information that would not have been gained otherwise—one of the key reasons researchers conduct fieldwork in the first place. In other instances, conducting any in-person research is simply not possible, due to financial constraints, safety concerns, or other reasons. For example, the Covid-19 pandemic has forced many researchers to shift their face-to-face research plans to remote data collection, either over the phone or virtually ( Howlett 2021 , 2). For some research projects, gathering data through remote methods may yield the same if not similar information than in-person research ( Howlett 2021 , 3–4). As Howlett (2021 , 11) notes, digital platforms may offer researchers the ability to “embed ourselves in other contexts from a distance” and glimpse into our subjects’ lives in ways similar to in-person research. By adopting a broader definition of fieldwork, researchers can be more flexible in getting access to data sources and interacting with research subjects.

Third, there is a tendency, especially among comparativists, to only count fieldwork that spans the better part of a year; even “surgical strike” field research entails one to three months, according to some scholars ( Ortbals and Rincker 2009 ; Weiss, Hicken, and Kuhonta 2017 ). The emphasis on spending as much time as possible in the field is likely due to ethnographic research traditions, reflected in classics such as James Scott's Weapons of the Weak , which entail year-long stints of research. However, we suggest that the appropriate amount of time in the field should be assessed on a project-by-project basis. Some studies require the researcher to be in the field for long periods; others do not. For example, Willis's research on the discourse around the United States’ military presence in overseas host communities has required months in the field. By contrast, Kreft only needed ten days in New York to carry out interviews with diplomats and United Nations staff, in a context with which she already had some familiarity from a prior internship. Likewise, Zvobgo spent a couple of weeks in her field research sites, conducting interviews with directors and managers of prominent human rights nongovernmental organizations. This population is not so large as to require a whole month or even a few months. This has also been the case for Irgil, as she had spent one month in the field site conducting interviews with ordinary citizens. The goal of the project was to acquire information on citizens’ perceptions of refugees. As we discuss in the next section, when deciding how long to spend in the field, scholars must consider the information their project requires and consider the practicalities of fieldwork, notably cost.

Thus, we highlight three essential points in fieldwork and offer a definition accordingly: fieldwork involves acquiring information, using any set of appropriate data collection techniques, for qualitative, quantitative, or experimental analysis through embedded research whose location and duration is dependent on the project. We argue that adopting such a definition of “fieldwork” is necessary to include the multitude of forms fieldwork can take, including remote methods, whose value and challenges the Covid-19 pandemic has impressed upon the discipline.

When does a researcher need to conduct fieldwork? Fieldwork can be effective for (1) data collection, (2) theory building, and (3) theory testing. First, when a researcher is interested in a research topic, yet they could not find an available and/or reliable data source for the topic, fieldwork could provide the researcher with plenty of options. Some research agendas can require researchers to visit archives to review historical documents. For example, Greitens (2016) visited national archives in the Philippines, South Korea, Taiwan, and the United States to find historical documents about the development of coercive institutions in past authoritarian governments for her book, Dictators and Their Secret Police . Also, newly declassified archival documents can open new possibilities for researchers to examine restricted topics. To illustrate, thanks to the newly released archival records of the Chinese Communist Party's communications, and exchange of visits with the European communist world, Sarotte (2012) was able to study the Party's decision to crack down on Tiananmen protesters, which had previously been deemed as an unstudiable topic due to the limited data.

Other research agendas can require researchers to conduct (semistructured) in-depth interviews to understand human behavior or a situation more closely, for example, by revealing the meanings of concepts for people and showing how people perceive the world. For example, O'Brien and Li (2005) conducted in-depth interviews with activists, elites, and villagers to understand how these actors interact with each other and what are the outcomes of the interaction in contentious movements in rural China. Through research, they revealed that protests have deeply influenced all these actors’ minds, a fact not directly observable without in-depth interviews.

Finally, data collection through fieldwork should not be confined to qualitative data ( Jensenius 2014 ). While some quantitative datasets can be easily compiled or accessed through use of the internet or contact with data-collection agencies, other datasets can only be built or obtained through relationships with “gatekeepers” such as government officials, and thus require researchers to visit the field ( Jensenius 2014 ). Researchers can even collect their own quantitative datasets by launching surveys or quantifying data contained in archives. In a nutshell, fieldwork will allow researchers to use different techniques to collect and access original/primary data sources, whether these are qualitative, quantitative, or experimental in nature, and regardless of the intended method of analysis. 2

But fieldwork is not just for data collection as such. Researchers can accomplish two other fundamental elements of the research process: theory building and theory testing. When a researcher finds a case where existing theories about a phenomenon do not provide plausible explanations, they can build a theory through fieldwork ( Geddes 2003 ). Lee's experience provides a good example. When studying the rise of a protest movement in South Korea for her dissertation, Lee applied commonly discussed social movement theories, grievances, political opportunity, resource mobilization, and repression, to explain the movement's eruption and found that these theories do not offer a convincing explanation for the protest movement. She then moved on to fieldwork and conducted interviews with the movement participants to understand their motivations. Finally, through those interviews, she offered an alternative theory that the protest participants’ collective identity shaped during the authoritarian past played a unifying factor and eventually led them to participate in the movement. Her example shows that theorization can take place through careful review and rigorous inference during fieldwork.

Moreover, researchers can test their theory through fieldwork. Quantitative observational data has limitations in revealing causal mechanisms ( Esarey 2017 ). Therefore, many political scientists turn their attention to conducting field experiments or lab-in-the-field experiments to reveal causality ( Druckman et al. 2006 ; Beath, Christia, and Enikolopov 2013 ; Finseraas and Kotsadam 2017 ), or to leveraging in-depth insights or historical records gained through qualitative or archival research in process-tracing ( Collier 2011 ; Ricks and Liu 2018 ). Surveys and survey experiments may also be useful tools to substantiate a theoretical story or test a theory ( Marston 2020 ). Of course, for most Ph.D. students, especially those not affiliated with more extensive research projects, some of these options will be financially prohibitive.

A central concern for graduate students, especially those working with a small budget and limited time, is optimizing time in the field and integrating remote work. We offer three pieces of advice: have a plan, build in flexibility, and be strategic, focusing on collecting data that are unavailable at home. We also discuss working with local translators or research assistants. Before we turn to these more practical issues arising during fieldwork, we address a no less important issue: funding.

The challenge of securing funds is often overlooked in discussions of what constitutes field research. Months- or year-long in-person research can be cost-prohibitive, something academic gatekeepers must consider when evaluating “what counts” and “what is enough.” Unlike their predecessors, many graduate students today have a significant amount of debt and little savings. 3 Additionally, if researchers are not able to procure funding, they have to pay out of pocket and possibly take on more debt. Not only is in-person fieldwork costly, but researchers may also have to forego working while they are in the field, making long stretches in the field infeasible for some.

For researchers whose fieldwork involves travelling to another location, procuring funding via grants, fellowships, or other sources is a necessity, regardless of how long one plans to be in the field. A good mantra for applying for research funding is “apply early and often” ( Kelsky 2015 , 110). Funding applications take a considerable amount of time to prepare, from writing research statements to requesting letters of recommendation. Even adapting one's materials for different applications takes time. Not only is the application process itself time-consuming, but the time between applying for and receiving funds, if successful, can be quite long, from several months to a year. For example, after defending her prospectus in May 2019, Willis began applying to funding sources for her dissertation, all of which had deadlines between June and September. She received notifications between November and January; however, funds from her successful applications were not available until March and April, almost a year later. 4 Accordingly, we recommend applying for funding as early as possible; this not only increases one's chances of hitting the ground running in the field, but the application process can also help clarify the goals and parameters of one's research.

Graduate students should also apply often for funding opportunities. There are different types of funding for fieldwork: some are larger, more competitive grants such as the National Science Foundation Political Science Doctoral Dissertation Improvement Grant in the United States, others, including sources through one's own institution, are smaller. Some countries, like Sweden, boast a plethora of smaller funding agencies that disburse grants of 20,000–30,0000 Swedish Kronor (approx. 2,500–3,500 U.S. dollars) to Ph.D. students in the social sciences. Listings of potential funding sources are often found on various websites including those belonging to universities, professional organizations (such as the American Political Science Association or the European Consortium for Political Research), and governmental institutions dealing with foreign affairs. Once you have identified fellowships and grants for which you and your project are a good match, we highly recommend soliciting information and advice from colleagues who have successfully applied for them. This can include asking them to share their applications with you, and if possible, to have them, another colleague or set of colleagues read through your project description and research plan (especially for bigger awards) to ensure that you have made the best possible case for why you should be selected. While both large and small pots of funding are worth applying for, many researchers end up funding their fieldwork through several small grants or fellowships. One small award may not be sufficient to fund the entirety of one's fieldwork, but several may. For example, Willis's fieldwork in Japan and South Korea was supported through fellowships within each country. Similarly, Irgil was able to conduct her fieldwork abroad through two different and relatively smaller grants by applying to them each year.

Of course, situations vary in different countries with respect to what kinds of grants from what kinds of funders are available. An essential part of preparing for fieldwork is researching the funding landscape well in advance, even as early as the start of the Ph.D. We encourage first-time field researchers to be aware that universities and departments may themselves not be aware of the full range of possible funds available, so it is always a good idea to do your own research and watch research-related social media channels. The amount of funding needed thereby depends on the nature of one's project and how long one intends to be in the field. As we elaborate in the next section, scholars should think carefully about their project goals, the data required to meet those goals, and the requisite time to attain them. For some projects, even a couple of weeks in the field is sufficient to get the needed information.

Preparing to Enter “the field”

It is important to prepare for the field as much as possible. What kind of preparations do researchers need? For someone conducting interviews with NGO representatives, this might involve identifying the largest possible pool of potential respondents, securing their contact information, sending them study invitation letters, finding a mutually agreeable time to meet, and pulling together short biographies for each interviewee in order to use your time together most effectively. If you plan to travel to conduct interviews, you should reach out to potential respondents roughly four to six weeks prior to your arrival. For individuals who do not respond, you can follow up one to two weeks before you arrive and, if needed, once more when you are there. This is still no guarantee for success, of course. For Kreft, contacting potential interviewees in Colombia initially proved more challenging than anticipated, as many of the people she targeted did not respond to her emails. It turned out that many Colombians have a preference for communicating via phone or, in particular, WhatsApp. Some of those who responded to her emails sent in advance of her field trip asked her to simply be in touch once she was in the country, to set up appointments on short notice. This made planning and arranging her interview schedule more complicated. Therefore, a general piece of advice is to research your target population's preferred communication channels and mediums in the field site if email requests yield no or few responses.

In general, we note for the reader that contacting potential research participants should come after one has designed an interview questionnaire (plus an informed consent protocol) and sought and received, where applicable, approval from institutional review boards (IRBs) or other ethical review procedures in place (both at one's home institution/in the country of the home institution as well as in the country where one plans to conduct research if travelling abroad). The most obvious advantage of having the interview questionnaire in place and having secured all necessary institutional approvals before you start contacting potential interviewees is that you have a clearer idea of the universe of individuals you would like to interview, and for what purpose. Therefore, it is better to start sooner rather than later and be mindful of “high seasons,” when institutional and ethical review boards are receiving, processing, and making decisions on numerous proposals. It may take a few months for them to issue approvals.

On the subject of ethics and review panels, we encourage you to consider talking openly and honestly with your supervisors and/or funders about the situations where a written consent form may not be suitable and might need to be replaced with “verbal consent.” For instance, doing fieldwork in politically unstable contexts, highly scrutinized environments, or vulnerable communities, like refugees, might create obstacles for the interviewees as well as the researcher. The literature discusses the dilemma in offering the interviewees anonymity and requesting signed written consent in addition to the emphasis on total confidentiality ( Jacobsen and Landau 2003 ; Mackenzie, McDowell, and Pittaway 2007 ; Saunders, Kitzinger, and Kitzinger 2015 ). Therefore, in those situations, the researcher might need to take the initiative on how to act while doing the interviews as rigorously as possible. In her fieldwork, Irgil faced this situation as the political context of Turkey did not guarantee that there would not be any adverse consequences for interviewees on both sides of her story: citizens of Turkey and Syrian refugees. Consequently, she took hand-written notes and asked interviewees for their verbal consent in a safe interview atmosphere. This is something respondents greatly appreciated ( Irgil 2020 ).

Ethical considerations, of course, also affect the research design itself, with ramifications for fieldwork. When Kreft began developing her Ph.D. proposal to study women's political and civil society mobilization in response to conflict-related sexual violence, she initially aimed to recruit interviewees from the universe of victims of this violence, to examine variation among those who did and those who did not mobilize politically. As a result of deeper engagement with the literature on researching conflict-related sexual violence, conversations with senior colleagues who had interviewed victims, and critical self-reflection of her status as a researcher (with no background in psychology or social work), she decided to change focus and shift toward representatives of civil society organizations and victims’ associations. This constituted a major reconfiguration of her research design, from one geared toward identifying the factors that drive mobilization of victims toward using insights from interviews to understand better how those mobilize perceive and “make sense” of conflict-related sexual violence. Needless to say, this required alterations to research strategies and interview guides, including reassessing her planned fieldwork. Kreft's primary consideration was not to cause harm to her research participants, particularly in the form of re-traumatization. She opted to speak only with those women who on account of their work are used to speaking about conflict-related sexual violence. In no instance did she inquire about interviewees’ personal experiences with sexual violence, although several brought this up on their own during the interviews.

Finally, if you are conducting research in another country where you have less-than-professional fluency in the language, pre-fieldwork planning should include hiring a translator or research assistant, for example, through an online hiring platform like Upwork, or a local university. Your national embassy or consulate is another option; many diplomatic offices have lists of individuals who they have previously contracted. More generally, establishing contact with a local university can be beneficial, either in the form of a visiting researcher arrangement, which grants access to research groups and facilities like libraries or informally contacting individual researchers. The latter may have valuable insights into the local context, contacts to potential research participants, and they may even be able to recommend translators or research assistants. Kreft, for example, hired local research assistants recommended by researchers at a Bogotá-based university and remunerated them equivalent to the salary they would have received as graduate research assistants at the university, while also covering necessary travel expenses. Irgil, on the other hand, established contacts with native citizens and Syrian gatekeepers, who are shop owners in the area where she conducted her research because she had the opportunity to visit the fieldwork site multiple times.

Depending on the research agenda, researchers may visit national archives, local government offices, etc. Before visiting, researchers should contact these facilities and make sure the materials that they need are accessible. For example, Lee visited the Ronald Reagan Presidential Library Archives to find the United States’ strategic evaluations on South Korea's dictator in the 1980s. Before her visit, she contacted librarians in the archives, telling them her visit plans and her research purpose. Librarians made suggestions on which categories she should start to review based on her research goal, and thus she was able to make a list of categories of the materials she needed, saving her a lot of her time.

Accessibility of and access to certain facilities/libraries can differ depending on locations/countries and types of facilities. Facilities in authoritarian countries might not be easily accessible to foreign researchers. Within democratic countries, some facilities are more restrictive than others. Situations like the pandemic or national holidays can also restrict accessibility. Therefore, researchers are well advised to do preliminary research on whether a certain facility opens during the time they visit and is accessible to researchers regardless of their citizenship status. Moreover, researchers must contact the staff of facilities to know whether identity verification is needed and if so, what kind of documents (photo I.D. or passport) should be exhibited.

Adapting to the Reality of the Field

Researchers need to be flexible because you may meet people you did not make appointments with, come across opportunities you did not expect, or stumble upon new ideas about collecting data in the field. These happenings will enrich your field experience and will ultimately be beneficial for your research. Similarly, researchers should not be discouraged by interviews that do not go according to plan; they present an opportunity to pursue relevant people who can provide an alternative path to your work. Note that planning ahead does not preclude fortuitous encounters or epiphanies. Rather, it provides a structure for them to happen.

If your fieldwork entails travelling abroad, you will also be able to recruit more interviewees once you arrive at your research site. In fact, you may have greater success in-country; not everyone is willing to respond to a cold email from an unknown researcher in a foreign country. In Irgil's fieldwork, she contacted store owners that are known in the area and who know the community. This eased her process of introduction into the community and recruiting interviewees. For Zvobgo, she had fewer than a dozen interviews scheduled when she travelled to Guatemala to study civil society activism and transitional justice since the internal armed conflict. But she was able to recruit additional participants in-country. Interviewees with whom she built a rapport connected her to other NGOs, government offices, and the United Nations country office, sometimes even making the call and scheduling interviews for her. Through snowball sampling, she was able to triple the number of participants. Likewise, snowball sampling was central to Kreft's recruitment of interview partners. Several of her interviewees connected her to highly relevant individuals she would never have been able to identify and contact based on web searches alone.

While in the field, you may nonetheless encounter obstacles that necessitate adjustments to your original plans. Once Kreft had arrived in Colombia, for example, it transpired quickly that carrying out in-person interviews in more remote/rural areas was near impossible given her means, as these were not easily accessible by bus/coach, further complicated by a complex security situation. Instead, she adjusted her research design and shifted her focus to the big cities, where most of the major civil society organizations are based. She complemented the in-person interviews carried out there with a smaller number of phone interviews with civil society activists in rural areas, and she was also able to meet a few activists operating in rural or otherwise inaccessible areas as they were visiting the major cities. The resulting focus on urban settings changed the kinds of generalizations she was able to make based on her fieldwork data and produced a somewhat different study than initially anticipated.

This also has been the case for Irgil, despite her prior arrangements with the Syrian gatekeepers, which required adjustments as in the case of Kreft. Irgil acquired research clearance one year before, during the interviews with native citizens, conducting the interviews with Syrian refugees. She also had her questionnaire ready based on the previously collected data and the media search she had conducted for over a year before travelling to the field site. As she was able to visit the field site multiple times, two months before conducting interviews with Syrian refugees, she developed a schedule with the Syrian gatekeepers and informants. Yet, once she was in the field, influenced by Turkey's recent political events and the policy of increasing control over Syrian refugees, half of the previously agreed informants changed their minds or did not want to participate in interviews. As Irgil was following the policies and the news related to Syrian refugees in Turkey closely, this did not come as that big of a surprise but challenged the previously developed strategy to recruit interviewees. Thus, she changed the strategy of finding interviewees in the field site, such as asking people, almost one by one, whether they would like to participate in the interview. Eventually, she could not find willing Syrian women refugees as she had planned, which resulted in a male-dominant sample. As researchers encounter such situations, it is essential to remind oneself that not everything can go according to plan, that “different” does not equate to “worse,” but that it is important to consider what changes to fieldwork data collection and sampling imply for the study's overall findings and the contribution it makes to the literature.

We should note that conducting interviews is very taxing—especially when opportunities multiply, as in Zvobgo's case. Depending on the project, each interview can take an hour, if not two or more. Hence, you should make a reasonable schedule: we recommend no more than two interviews per day. You do not want to have to cut off an interview because you need to rush to another one, whether the interviews are in-person or remote. And you do not want to be too exhausted to have a robust engagement with your respondent who is generously lending you their time. Limiting the number of interviews per day is also important to ensure that you can write comprehensive and meaningful fieldnotes, which becomes even more essential where it is not possible to audio-record your interviews. Also, be sure to remember to eat, stay hydrated, and try to get enough sleep.

Finally, whether to provide gifts or payments to the subject also requires adapting to the reality of the field. You must think about payments beforehand when you apply for IRB approval (or whatever other ethical review processes may be in place) since these applications usually contain questions about payments. Obviously, the first step is to carefully evaluate whether the gifts and payments provided can harm the subject or are likely to unduly affect the responses they will give in response to your questions. If that is not the case, you have to make payment decisions based on your budget, field situation, and difficulties in recruitment. Usually, payment of respondents is more common in survey research, whereas it is less common in interviews and focus groups.

Nevertheless, payment practices vary depending on the field and the target group. In some cases, it may become a custom to provide small gifts or payments when interviewing a certain group. In other cases, interviewees might be offended if they are provided with money. Therefore, knowing past practices and field situations is important. For example, Lee provided small coffee gift cards to one group while she did not to the other based on previous practices of other researchers. That is, for a particular group, it has become a custom for interviewers to pay interviewees. Sometimes, you may want to reimburse your subject's interview costs such as travel expenses and provide beverages and snacks during the conduct of research, as Kreft did when conducting focus groups in Colombia. To express your gratitude to your respondents, you can prepare small gifts such as your university memorabilia (e.g., notebooks and pens). Since past practices about payments can affect your interactions and interviews with a target group, you want to seek advice from your colleagues and other researchers who had experiences interacting with the target group. If you cannot find researchers who have this knowledge, you can search for published works on the target population to find if the authors share their interview experiences. You may also consider contacting the authors for advice before your interviews.

Researching Strategically

Distinguishing between things that can only be done in person at a particular site and things that can be accomplished later at home is vital. Prioritize the former over the latter. Lee's fieldwork experience serves as a good example. She studied a conservative protest movement called the Taegeukgi Rally in South Korea. She planned to conduct interviews with the rally participants to examine their motivations for participating. But she only had one month in South Korea. So, she focused on things that could only be done in the field: she went to the rally sites, she observed how protests proceeded, which tactics and chants were used, and she met participants and had some casual conversations with them. Then, she used the contacts she made while attending the rallies to create a social network to solicit interviews from ordinary protesters, her target population. She was able to recruit twenty-five interviewees through good rapport with the people she met. The actual interviews proceeded via phone after she returned to the United States. In a nutshell, we advise you not to be obsessed with finishing interviews in the field. Sometimes, it is more beneficial to use your time in the field to build relationships and networks.

Working With Assistants and Translators

A final consideration on logistics is working with research assistants or translators; it affects how you can carry out interviews, focus groups, etc. To what extent constant back-and-forth translation is necessary or advisable depends on the researcher's skills in the interview language and considerations about time and efficiency. For example, Kreft soon realized that she was generally able to follow along quite well during her interviews in Colombia. In order to avoid precious time being lost to translation, she had her research assistant follow the interview guide Kreft had developed, and interjected follow-up questions in Spanish or English (then to be translated) as they arose.

Irgil's and Zvobgo's interviews went a little differently. Irgil's Syrian refugee interviewees in Turkey were native Arabic speakers, and Zvobgo's interviewees in Guatemala were native Spanish speakers. Both Irgil and Zvobgo worked with research assistants. In Irgil's case, her assistant was a Syrian man, who was outside of the area. Meanwhile, Zvobgo's assistant was an undergraduate from her home institution with a Spanish language background. Irgil and Zvobgo began preparing their assistants a couple of months before entering the field, over Skype for Irgil and in-person for Zvobgo. They offered their assistants readings and other resources to provide them with the necessary background to work well. Both Irgil and Zvobgo's research assistants joined them in the interviews and actually did most of the speaking, introducing the principal investigator, explaining the research, and then asking the questions. In Zvobgo's case, interviewee responses were relayed via a professional interpreter whom she had also hired. After every interview, Irgil and Zvobgo and their respective assistants discussed the answers of the interviewees, potential improvements in phrasing, and elaborated on their hand-written interview notes. As a backup, Zvobgo, with the consent of her respondents, had accompanying audio recordings.

Researchers may carry out fieldwork in a country that is considerably less safe than what they are used to, a setting affected by conflict violence or high crime rates, for instance. Feelings of insecurity can be compounded by linguistic barriers, cultural particularities, and being far away from friends and family. Insecurity is also often gendered, differentially affecting women and raising the specter of unwanted sexual advances, street harassment, or even sexual assault ( Gifford and Hall-Clifford 2008 ; Mügge 2013 ). In a recent survey of Political Science graduate students in the United States, about half of those who had done fieldwork internationally reported having encountered safety issues in the field, (54 percent female, 47 percent male), and only 21 percent agreed that their Ph.D. programs had prepared them to carry out their fieldwork safely ( Schwartz and Cronin-Furman 2020 , 8–9).

Preventative measures scholars may adopt in an unsafe context may involve, at their most fundamental, adjustments to everyday routines and habits, restricting one's movements temporally and spatially. Reliance on gatekeepers may also necessitate adopting new strategies, such as a less vehement and cold rejection of unwanted sexual advances than one ordinarily would exhibit, as Mügge (2013) illustratively discusses. At the same time, a competitive academic job market, imperatives to collect novel and useful data, and harmful discourses surrounding dangerous fieldwork also, problematically, shape incentives for junior researchers to relax their own standards of what constitutes acceptable risk ( Gallien 2021 ).

Others have carefully collected a range of safety precautions that field researchers in fragile or conflict-affected settings may take before and during fieldwork ( Hilhorst et al. 2016 ). Therefore, we are more concise in our discussion of recommendations, focusing on the specific situations of graduate students. Apart from ensuring that supervisors and university administrators have the researcher's contact information in the field (and possibly also that of a local contact person), researchers can register with their country's embassy or foreign office and any crisis monitoring and prevention systems it has in place. That way, they will be informed of any possible unfolding emergencies and the authorities have a record of them being in the country.

It may also be advisable to set up more individualized safety protocols with one or two trusted individuals, such as friends, supervisors, or colleagues at home or in the fieldwork setting itself. The latter option makes sense in particular if one has an official affiliation with a local institution for the duration of the fieldwork, which is often advisable. Still, we would also recommend establishing relationships with local researchers in the absence of a formal affiliation. To keep others informed of her whereabouts, Kreft, for instance, made arrangements with her supervisors to be in touch via email at regular intervals to report on progress and wellbeing. This kept her supervisors in the loop, while an interruption in communication would have alerted them early if something were wrong. In addition, she announced planned trips to other parts of the country and granted her supervisors and a colleague at her home institution emergency reading access to her digital calendar. To most of her interviews, she was moreover accompanied by her local research assistant/translator. If the nature of the research, ethical considerations, and the safety situation allow, it might also be possible to bring a local friend along to interviews as an “assistant,” purely for safety reasons. This option needs to be carefully considered already in the planning stage and should, particularly in settings of fragility or if carrying out research on politically exposed individuals, be noted in any ethical and institutional review processes where these are required. Adequate compensation for such an assistant should be ensured. It may also be advisable to put in place an emergency plan, that is, choose emergency contacts back home and “in the field,” know whom to contact if something happens, and know how to get to the nearest hospital or clinic.

We would be remiss if we did not mention that, when in an unfamiliar context, one's safety radar may be misguided, so it is essential to listen to people who know the context. For example, locals can give advice on which means of transport are safe and which are not, a question that is of the utmost importance when traveling to appointments. For example, Kreft was warned that in Colombia regular taxis are often unsafe, especially if waved down in the streets, and that to get to her interviews safely, she should rely on a ride-share service. In one instance, a Colombian friend suggested that when there was no alternative to a regular taxi, Kreft should book through the app and share the order details, including the taxi registration number or license plate, with a friend. Likewise, sharing one's cell phone location with a trusted friend while traveling or when one feels unsafe may be a viable option. Finally, it is prudent to heed the safety recommendations and travel advisories provided by state authorities and embassies to determine when and where it is safe to travel. Especially if researchers have a responsibility not only for themselves but also for research assistants and research participants, safety must be a top priority.

This does not mean that a researcher should be careless in a context they know either. Of course, conducting fieldwork in a context that is known to the researcher offers many advantages. However, one should be prepared to encounter unwanted events too. For instance, Irgil has conducted fieldwork in her country of origin in a city she knows very well. Therefore, access to the site, moving around the site, and blending in has not been a problem; she also has the advantage of speaking the native language. Yet, she took notes of the streets she walked in, as she often returned from the field site after dark and thought she might get confused after a tiring day. She also established a closer relationship with two or three store owners in different parts of the field site if she needed something urgent, like running out of battery. Above all, one should always be aware of one's surroundings and use common sense. If something feels unsafe, chances are it is.

Fieldwork may negatively affect the researcher's mental health and mental wellbeing regardless of where one's “field” is, whether related to concerns about crime and insecurity, linguistic barriers, social isolation, or the practicalities of identifying, contacting and interviewing research participants. Coping with these different sources of stress can be both mentally and physically exhausting. Then there are the things you may hear, see and learn during the research itself, such as gruesome accounts of violence and suffering conveyed in interviews or archival documents one peruses. Kreft and Zvobgo have spoken with women victims of conflict-related sexual violence, who sometimes displayed strong emotions of pain and anger during the interviews. Likewise, Irgil and Willis have spoken with members of other vulnerable populations such as refugees and former sex workers ( Willis 2020 ).

Prior accounts ( Wood 2006 ; Loyle and Simoni 2017 ; Skjelsbæk 2018 ; Hummel and El Kurd 2020 ; Williamson et al. 2020 ; Schulz and Kreft 2021 ) show that it is natural for sensitive research and fieldwork challenges to affect or even (vicariously) traumatize the researcher. By removing researchers from their regular routines and support networks, fieldwork may also exacerbate existing mental health conditions ( Hummel and El Kurd 2020 ). Nonetheless, mental wellbeing is rarely incorporated into fieldwork courses and guidelines, where these exist at all. But even if you know to anticipate some sort of reaction, you rarely know what that reaction will be until you experience it. When researching sensitive or difficult topics, for example, reactions can include sadness, frustration, anger, fear, helplessness, and flashbacks to personal experiences of violence ( Williamson et al. 2020 ). For example, Kreft responded with episodic feelings of depression and both mental and physical exhaustion. But curiously, these reactions emerged most strongly after she had returned from fieldwork and in particular as she spent extended periods analyzing her interview data, reliving some of the more emotional scenes during the interviews and being confronted with accounts of (sexual) violence against women in a concentrated fashion. This is a crucial reminder that fieldwork does not end when one returns home; the after-effects may linger. Likewise, Zvobgo was physically and mentally drained upon her return from the field. Both Kreft and Zvobgo were unable to concentrate for long periods of time and experienced lower-than-normal levels of productivity for weeks afterward, patterns that formal and informal conversations with other scholars confirm to be common ( Schulz and Kreft 2021 ). Furthermore, the boundaries between “field” and “home” are blurred when conducting remote fieldwork ( Howlett 2021 , 11).

Nor are these adverse reactions limited to cases where the researcher has carried out the interviews themselves. Accounts of violence, pain, and suffering transported in reports, secondary literature, or other sources can evoke similar emotional stress, as Kreft experienced when engaging in a concentrated fashion with additional accounts of conflict-related sexual violence in Colombia and with the feminist literature on sexual and gender-based violence in the comfort of her Swedish office. This could also be applicable to Irgil's fieldwork as she interviewed refugees whose traumas have come out during the interviews or recall specific events triggered by the questions. Likewise, Lee has reviewed primary and secondary materials on North Korean defectors in the national archives and these materials contain violent, intense, emotional narratives.

Fortunately, there are several strategies to cope with and manage such adverse consequences. In a candid and insightful piece, other researchers have discussed the usefulness of distractions, sharing with colleagues, counseling, exercise, and, probably less advisable in the long term, comfort eating and drinking ( Williamson et al. 2020 ; see also Loyle and Simoni 2017 ; Hummel and El Kurd 2020 ). Our experiences largely tally with their observations. In this section, we explore some of these in more detail.

First, in the face of adverse consequences on your mental wellbeing, whether in the field or after your return, it is essential to be patient and generous with yourself. Negative effects on the researcher's mental wellbeing can hit in unexpected ways and at unexpected times. Even if you think that certain reactions are disproportionate or unwarranted at that specific moment, they may simply have been building up over a long time. They are legitimate. Second, the importance of taking breaks and finding distractions, whether that is exercise, socializing with friends, reading a good book, or watching a new series, cannot be overstated. It is easy to fall into a mode of thinking that you constantly have to be productive while you are “in the field,” to maximize your time. But as with all other areas in life, balance is key and rest is necessary. Taking your mind off your research and the research questions you puzzle over is also a good way to more fully soak up and appreciate the context in which you find yourself, in the case of in-person fieldwork, and about which you ultimately write.

Third, we cannot stress enough the importance of investing in social relations. Before going on fieldwork, researchers may want to consult others who have done it before them. Try to find (junior) scholars who have done fieldwork on similar kinds of topics or in the same country or countries you are planning to visit. Utilizing colleagues’ contacts and forging connections using social media are valuable strategies to expand your networks (in fact, this very paper is the result of a social media conversation and several of the authors have never met in person). Having been in the same situation before, most field researchers are, in our experience, generous with their time and advice. Before embarking on her first trip to Colombia, Kreft contacted other researchers in her immediate and extended network and received useful advice on questions such as how to move around Bogotá, whom to speak to, and how to find a research assistant. After completing her fieldwork, she has passed on her experiences to others who contacted her before their first fieldwork trip. Informal networks are, in the absence of more formalized fieldwork preparation, your best friend.

In the field, seeking the company of locals and of other researchers who are also doing fieldwork alleviates anxiety and makes fieldwork more enjoyable. Exchanging experiences, advice and potential interviewee contacts with peers can be extremely beneficial and make the many challenges inherent in fieldwork (on difficult topics) seem more manageable. While researchers conducting remote fieldwork may be physically isolated from other researchers, even connecting with others doing remote fieldwork may be comforting. And even when there are no precise solutions to be found, it is heartening or even cathartic to meet others who are in the same boat and with whom you can talk through your experiences. When Kreft shared some of her fieldwork-related struggles with another researcher she had just met in Bogotá and realized that they were encountering very similar challenges, it was like a weight was lifted off her shoulders. Similarly, peer support can help with readjustment after the fieldwork trip, even if it serves only to reassure you that a post-fieldwork dip in productivity and mental wellbeing is entirely natural. Bear in mind that certain challenges are part of the fieldwork experience and that they do not result from inadequacy on the part of the researcher.

Finally, we would like to stress a point made by Inger Skjelsbæk (2018 , 509) and which has not received sufficient attention: as a discipline, we need to take the question of researcher mental wellbeing more seriously—not only in graduate education, fieldwork preparation, and at conferences, but also in reflecting on how it affects the research process itself: “When strong emotions arise, through reading about, coding, or talking to people who have been impacted by [conflict-related sexual violence] (as victims or perpetrators), it may create a feeling of being unprofessional, nonscientific, and too subjective.”

We contend that this is a challenge not only for research on sensitive issues but also for fieldwork more generally. To what extent is it possible, and desirable, to uphold the image of the objective researcher during fieldwork, when we are at our foundation human beings? And going even further, how do the (anticipated) effects of our research on our wellbeing, and the safety precautions we take ( Gifford and Hall-Clifford 2008 ), affect the kinds of questions we ask, the kinds of places we visit and with whom we speak? How do they affect the methods we use and how we interpret our findings? An honest discussion of affective responses to our research in methods sections seems utopian, as emotionality in the research process continues to be silenced and relegated to the personal, often in gendered ways, which in turn is considered unconnected to the objective and scientific research process ( Jamar and Chappuis 2016 ). But as Gifford and Hall-Clifford (2008 , 26) aptly put it: “Graduate education should acknowledge the reality that fieldwork is scholarly but also intimately personal,” and we contend that the two shape each other. Therefore, we encourage political science as a discipline to reflect on researcher wellbeing and affective responses to fieldwork more carefully, and we see the need for methods courses that embrace a more holistic notion of the subjectivity of the researcher.

Interacting with people in the field is one of the most challenging yet rewarding parts of the work that we do, especially in comparison to impersonal, often tedious wrangling and analysis of quantitative data. Field researchers often make personal connections with their interviewees. Consequently, maintaining boundaries can be a bit tricky. Here, we recommend being honest with everyone with whom you interact without overstating the abilities of a researcher. This appears as a challenge in the field, particularly when you empathize with people and when they share profound parts of their lives with you for your research in addition to being “human subjects” ( Fujii 2012 ). For instance, when Irgil interviewed native citizens about the changes in their neighborhood following the arrival of Syrian refugees, many interviewees questioned what she would offer them in return for their participation. Irgil responded that her primary contribution would be her published work. She also noted, however, that academic papers can take a year, sometimes longer, to go through the peer-reviewed process and, once published, many studies have a limited audience. The Syrian refugees posed similar questions. Irgil responded not only with honesty but also, given this population's vulnerable status, she provided them contact information for NGOs with which they could connect if they needed help or answers to specific questions.

For her part, Zvobgo was very upfront with her interviewees about her role as a researcher: she recognized that she is not someone who is on the frontlines of the fight for human rights and transitional justice like they are. All she could/can do is use her platform to amplify their stories, bringing attention to their vital work through her future peer-reviewed publications. She also committed to sending them copies of the work, as electronic journal articles are often inaccessible due to paywalls and university press books are very expensive, especially for nonprofits. Interviewees were very receptive; some were even moved by the degree of self-awareness and the commitment to do right by them. In some cases, this prompted them to share even more, because they knew that the researcher was really there to listen and learn. This is something that junior scholars, and all scholars really, should always remember. We enter the field to be taught. Likewise, Kreft circulated among her interviewees Spanish-language versions of an academic article and a policy brief based on the fieldwork she had carried out in Colombia.

As researchers from the Global North, we recognize a possible power differential between us and our research subjects, and certainly an imbalance in power between the countries where we have been trained and some of the countries where we have done and continue to do field research, particularly in politically dynamic contexts ( Knott 2019 ). This is why we are so concerned with being open and transparent with everyone with whom we come into contact in the field and why we are committed to giving back to those who so generously lend us their time and knowledge. Knott (2019 , 148) summarizes this as “Reflexive openness is a form of transparency that is methodologically and ethically superior to providing access to data in its raw form, at least for qualitative data.”

We also recognize that academics, including in the social sciences and especially those hailing from countries in the Global North, have a long and troubled history of exploiting their power over others for the sake of their research—including failing to be upfront about their research goals, misrepresenting the on-the-ground realities of their field research sites (including remote fieldwork), and publishing essentializing, paternalistic, and damaging views and analyses of the people there. No one should build their career on the backs of others, least of all in a field concerned with the possession and exercise of power. Thus, it is highly crucial to acknowledge the power hierarchies between the researcher and the interviewees, and to reflect on them both in the field and beyond the field upon return.

A major challenge to conducting fieldwork is when researchers’ carefully planned designs do not go as planned due to unforeseen events outside of our control, such as pandemics, natural disasters, deteriorating security situations in the field, or even the researcher falling ill. As the Covid-19 pandemic has made painfully clear, researchers may face situations where in-person research is simply not possible. In some cases, researchers may be barred entry to their fieldwork site; in others, the ethical implications of entering the field greatly outweigh the importance of fieldwork. Such barriers to conducting in-person research require us to reconsider conventional notions of what constitutes fieldwork. Researchers may need to shift their data collection methods, for example, conducting interviews remotely instead of in person. Even while researchers are in the field, they may still need to carry out part of their interviews or surveys virtually or by phone. For example, Kreft (2020) carried out a small number of interviews remotely while she was based in Bogotá, because some of the women's civil society activists with whom she intended to speak were based in parts of the country that were difficult and/or dangerous to access.

Remote field research, which we define as the collection of data over the internet or over the phone where in-person fieldwork is not possible due to security, health or other risks, comes with its own sets of challenges. For one, there may be certain populations that researchers cannot reach remotely due to a lack of internet connectivity or technology such as cellphones and computers. In such instances, there will be a sampling bias toward individuals and groups that do have these resources, a point worth noting when scholars interpret their research findings. In the case of virtual research, the risk of online surveillance, hacking, or wiretapping may also produce reluctance on the part of interviewees to discuss sensitive issues that may compromise their safety. Researchers need to carefully consider how the use of digital technology may increase the risk to research participants and what changes to the research design and any interview guides this necessitates. In general, it is imperative that researchers reflect on how they can ethically use digital technology in their fieldwork ( Van Baalen 2018 ). Remote interviews may also be challenging to arrange for researchers who have not made connections in person with people in their community of interest.

Some of the serendipitous happenings we discussed earlier may also be less likely and snowball sampling more difficult. For example, in phone or virtual interviews, it is harder to build good rapport and trust with interviewees as compared to face-to-face interviews. Accordingly, researchers should be more careful in communicating with interviewees and creating a comfortable interview environment. Especially when dealing with sensitive topics, researchers may have to make several phone calls and sometimes have to open themselves to establishing trust with interviewees. Also, researchers must be careful in protecting interviewees in phone or virtual interviews when they deal with sensitive topics of countries interviewees reside in.

The inability to physically visit one's community of interest may also encourage scholars to critically reflect on how much time in the field is essential to completing their research and to consider creative, alternative means for accessing information to complete their projects. While data collection techniques such as face-to-face interviews and archival work in the field may be ideal in normal times, there exist other data sources that can provide comparably useful information. For example, in her research on the role of framing in the United States base politics, Willis found that social media accounts and websites yielded information useful to her project. Many archives across the world have also been digitized. Researchers may also consider crowdsourcing data from the field among their networks, as fellow academics tend to collect much more data in the field than they ever use in their published works. They may also elect to hire someone, perhaps a graduate student, in a city or a country where they cannot travel and have the individual access, scan, and send archival materials. This final suggestion may prove generally useful to researchers with limited time and financial resources.

Remote qualitative data collection techniques, while they will likely never be “the gold-standard,” also pose several advantages. These techniques may help researchers avoid some of the issues mentioned previously. Remote interviews, for example, are less time-consuming in terms of travel to the interview site ( Archibald et al. 2019 ). The implication is that researchers may have less fatigue from conducting interviews and/or may be able to conduct more interviews. For example, while Willis had little energy to do anything else after an in-person interview (or two) in a given day, she had much more energy after completing remote interviews. Second, remote fieldwork also helps researchers avoid potentially dangerous situations in the field mentioned previously. Lastly, remote fieldwork generally presents fewer financial barriers than in-person research ( Archibald et al. 2019 ). In that sense, considering remote qualitative data collection, a type of “fieldwork” may make fieldwork more accessible to a greater number of scholars.

Many of the substantive, methodological and practical challenges that arise during fieldwork can be anticipated. Proper preparation can help you hit the ground running once you enter your fieldwork destination, whether in-person or virtually. Nonetheless, there is no such thing as being perfectly prepared for the field. Some things will simply be beyond your control, and especially as a newcomer to field research, and you should be prepared for things to not go as planned. New questions will arise, interview participants may cancel appointments, and you might not get the answers you expected. Be ready to make adjustments to research plans, interview guides, or questionnaires. And, be mindful of your affective reactions to the overall fieldwork situation and be gentle with yourself.

We recommend approaching fieldwork as a learning experience as much as, or perhaps even more than, a data collection effort. This also applies to your research topic. While it is prudent always to exercise a healthy amount of skepticism about what people tell you and why, the participants in your research will likely have unique perspectives and knowledge that will challenge yours. Be an attentive listener and remember that they are experts of their own experiences.

We encourage more institutions to offer courses that cover field research preparation and planning, practical advice on safety and wellbeing, and discussion of ethics. Specifically, we align with Schwartz and Cronin-Furman's (2020 , 3) contention “that treating fieldwork preparation as the methodology will improve individual scholars’ experiences and research.” In this article, we outline a set of issue areas in which we think formal preparation is necessary, but we note that our discussion is by no means exhaustive. Formal fieldwork preparation should also extend beyond what we have covered in this article, such as issues of data security and preparing for nonqualitative fieldwork methods. We also note that field research is one area that has yet to be comprehensively addressed in conversations on diversity and equity in the political science discipline and the broader academic profession. In a recent article, Brielle Harbin (2021) begins to fill this gap by sharing her experiences conducting in-person election surveys as a Black woman in a conservative and predominantly white region of the United States and the challenges that she encountered. Beyond race and gender, citizenship, immigration status, one's Ph.D. institution and distance to the field also affect who is able to do what type of field research, where, and for how long. Future research should explore these and related questions in greater detail because limits on who is able to conduct field research constrict the sociological imagination of our field.

While Emmons and Moravcsik (2020) focus on leading Political Science Ph.D. programs in the United States, these trends likely obtain, both in lower ranked institutions in the broader United States as well as in graduate education throughout North America and Europe.

As all the authors have carried out qualitative fieldwork, this is the primary focus of this guide. This does not, however, mean that we exclude quantitative or experimental data collection from our definition of fieldwork.

There is great variation in graduate students’ financial situations, even in the Global North. For example, while higher education is tax-funded in most countries in Europe and Ph.D. students in countries such as Sweden, Norway, Denmark, the Netherlands, and Switzerland receive a comparatively generous full-time salary, healthcare and contributions to pension schemes, Ph.D. programs in other contexts like the United States and the United Kingdom have (high) enrollment fees and rely on scholarships, stipends, or departmental duties like teaching to (partially) offset these, while again others, such as Germany, are commonly financed by part-time (50 percent) employment at the university with tasks substantively unrelated to the dissertation. These different preconditions leave many Ph.D. students struggling financially and even incurring debt, while others are in a more comfortable financial position. Likewise, Ph.D. programs around the globe differ in structure, such as required coursework, duration and supervision relationships. Naturally, all of these factors have a bearing on the extent to which fieldwork is feasible. We acknowledge unequal preconditions across institutions and contexts, and trust that those Ph.D. students interested in pursuing fieldwork are best able to assess the structural and institutional context in which they operate and what this implies for how, when, and how long to carry out fieldwork.

In our experience, this is not only the general cycle for graduate students in North America, but also in Europe and likely elsewhere.

For helpful advice and feedback on earlier drafts, we wish to thank the editors and reviewers at International Studies Review , and Cassandra Emmons. We are also grateful to our interlocuters in Argentina, Canada, Colombia, Germany, Guatemala, Japan, Kenya, Norway, the Philippines, Sierra Leone, South Korea, Spain, Sweden, Turkey, the United Kingdom, and the United States, without whom this reflection on fieldwork would not have been possible. All authors contributed equally to this manuscript.

This material is based upon work supported by the Forskraftstiftelsen Theodor Adelswärds Minne, Knut and Alice Wallenberg Foundation(KAW 2013.0178), National Science Foundation Graduate Research Fellowship Program(DGE-1418060), Southeast Asia Research Group (Pre-Dissertation Fellowship), University at Albany (Initiatives for Women and the Benevolent Association), University of Missouri (John D. Bies International Travel Award Program and Kinder Institute on Constitutional Democracy), University of Southern California (Provost Fellowship in the Social Sciences), Vetenskapsrådet(Diarienummer 2019-06298), Wilhelm och Martina Lundgrens Vetenskapsfond(2016-1102; 2018-2272), and William & Mary (Global Research Institute Pre-doctoral Fellowship).

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Qualitative study design: Field research

• Qualitative study design
• Phenomenology
• Grounded theory
• Ethnography
• Narrative inquiry
• Action research
• Case Studies

Field research

• Focus groups
• Observation
• Surveys & questionnaires
• Study Designs Home

To understand attitudes, practices, roles, organisations, groups, or behaviours in their natural setting

In a way you have probably done field research before – when you’ve been in a doctor’s waiting room, or on an aeroplane. Field research is at its core about observing and participating in social behaviour and trying to understand it. Qualitative field research takes these natural skills and curiosities and refines them to address and answer a research question The “field” is vast, consisting of numerous people, activities, events, and words. When undertaking field research, the researcher needs to determine the exact activities or practices that are of interest to the researcher to answer their research question. Instead of the more artificial environment of an interview or survey, field research lets researchers observe subtle communications, cues, or other events that they may not have anticipated or even measured otherwise.

Field research is often referred to interchangeably as “participant observation”. Participant observation is a type of field research where the researcher is an active participant in the everyday life, habits, or beliefs of the field alongside members. An example of this might be where a researcher goes into a hospital and works alongside hospital staff. A contrast to this is “direct observation”, a type of field research where the researcher observes members in the field but doesn’t actively participate. An example might be a researcher who sits at a hospital cafeteria and observes staff who may not realize they’re being studied.

You may be wondering what the difference is between ethnography and field research. The two terms are often used interchangeably, so it can be a really blurred line! Ethnography is about making sense of culture – it’s about making a detailed overview of the social group and organising your information. Field research is going out into the field – so describing “how” you’re going to conduct research. Ethnographical research can be field research (as in, you’re studying the culture of a hospital by observing within the hospital), or field research can be ethnographic (you’re observing staff in a hospital to see how staff handle crisis intervention). It’s a fine line between the two, and even experienced researchers can be unsure of the difference (or even use the terms interchangeably, depending on discipline), so when in doubt, it is best to talk to your supervisor or an experienced researcher in this discipline

Different studies may benefit from different degrees of researcher involvement. Ultimately, the researcher needs to be sensitive to the impact their presence might have on the data and on participants – and also aware of any ethical requirements around this study type, such as informed consent, duties to report (such as if the researcher observes criminal activities), and confidentiality and privacy of participants.

Observation, unstructured interviews

• Allows for observation in a natural setting
• Picks up on subtle cues
• Allows in depth exploration which contributes to a full appreciation of what’s being studied, including “whys” around human behaviour

Limitations

• Requires a high degree of sensitivity by the researcher to the impact of the research and their presence on participants and on the data
• Risk of reactivity, where research subjects may alter their behaviour from what it would have been normally as a result of being studied
• Ethical considerations involved in insider research
• Possible loss of objectivity

Example questions

How do student nurses integrate their training into care provision at end-of-life?

Example studies

• Barber-Parker, E. (2002). Integrating patient teaching into bedside patient care: a participant-observation study of hospital nurses.  Patient Education and Counselling, 48 ( 2): 107-113  
• Shikuku, D., Milimo, B., Ayebare, E., Gisore, P., & Gorrette, N. (2018). Practice and outcomes of neonatal resuscitation for newborns with birth asphyxia at Kakamega County General Hospital, Kenya: a direct observation study, BMC Pediatrics, 18 (1), doi: 10.1186/s12887-018-1127-6  

Babbie, E. (2008). The basics of social research (4th ed). Belmont: Thomson Wadsworth  

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What is Field Research: Definition, Methods, Examples and Advantages

What is Field Research?

Field research is defined as a qualitative method of data collection that aims to observe, interact and understand people while they are in a natural environment. For example, nature conservationists observe behavior of animals in their natural surroundings and the way they react to certain scenarios. In the same way, social scientists conducting field research may conduct interviews or observe people from a distance to understand how they behave in a social environment and how they react to situations around them.

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Field research encompasses a diverse range of social research methods including direct observation, limited participation, analysis of documents and other information, informal interviews, surveys etc. Although field research is generally characterized as qualitative research, it often involves multiple aspects of quantitative research in it.

Field research typically begins in a specific setting although the end objective of the study is to observe and analyze the specific behavior of a subject in that setting. The cause and effect of a certain behavior, though, is tough to analyze due to presence of multiple variables in a natural environment. Most of the data collection is based not entirely on cause and effect but mostly on correlation. While field research looks for correlation, the small sample size makes it difficult to establish a causal relationship between two or more variables.

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Methods of Field Research

Field research is typically conducted in 5 distinctive methods. They are:

• Direct Observation

In this method, the data is collected via an observational method or subjects in a natural environment. In this method, the behavior or outcome of situation is not interfered in any way by the researcher. The advantage of direct observation is that it offers contextual data on people management , situations, interactions and the surroundings. This method of field research is widely used in a public setting or environment but not in a private environment as it raises an ethical dilemma.

• Participant Observation

In this method of field research, the researcher is deeply involved in the research process, not just purely as an observer, but also as a participant. This method too is conducted in a natural environment but the only difference is the researcher gets involved in the discussions and can mould the direction of the discussions. In this method, researchers live in a comfortable environment with the participants of the research design , to make them comfortable and open up to in-depth discussions.

• Ethnography

Ethnography is an expanded observation of social research and social perspective and the cultural values of an  entire social setting. In ethnography, entire communities are observed objectively. For example,  if a researcher would like to understand how an Amazon tribe lives their life and operates, he/she may chose to observe them or live amongst them and silently observe their day-to-day behavior.

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• Qualitative Interviews

Qualitative interviews are close-ended questions that are asked directly to the research subjects. The qualitative interviews could be either informal and conversational, semi-structured, standardized and open-ended or a mix of all the above three. This provides a wealth of data to the researcher that they can sort through. This also helps collect relational data. This method of field research can use a mix of one-on-one interviews, focus groups and text analysis .

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A case study research is an in-depth analysis of a person, situation or event. This method may look difficult to operate, however, it is one of the simplest ways of conducting research as it involves a deep dive and thorough understanding the data collection methods and inferring the data.

Steps in Conducting Field Research

Due to the nature of field research, the magnitude of timelines and costs involved, field research can be very tough to plan, implement and measure. Some basic steps in the management of field research are:

• Build the Right Team: To be able to conduct field research, having the right team is important. The role of the researcher and any ancillary team members is very important and defining the tasks they have to carry out with defined relevant milestones is important. It is important that the upper management too is vested in the field research for its success.
• Recruiting People for the Study: The success of the field research depends on the people that the study is being conducted on. Using sampling methods , it is important to derive the people that will be a part of the study.
• Data Collection Methodology: As spoken in length about above, data collection methods for field research are varied. They could be a mix of surveys, interviews, case studies and observation. All these methods have to be chalked out and the milestones for each method too have to be chalked out at the outset. For example, in the case of a survey, the survey design is important that it is created and tested even before the research begins.
• Site Visit: A site visit is important to the success of the field research and it is always conducted outside of traditional locations and in the actual natural environment of the respondent/s. Hence, planning a site visit alongwith the methods of data collection is important.
• Data Analysis: Analysis of the data that is collected is important to validate the premise of the field research and  decide the outcome of the field research.
• Communicating Results: Once the data is analyzed, it is important to communicate the results to the stakeholders of the research so that it could be actioned upon.

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Field Research Notes

Keeping an ethnographic record is very important in conducting field research. Field notes make up one of the most important aspects of the ethnographic record. The process of field notes begins as the researcher is involved in the observational research process that is to be written down later.

Types of Field Research Notes

The four different kinds of field notes are:

• Job Notes: This method of taking notes is while the researcher is in the study. This could be in close proximity and in open sight with the subject in study. The notes here are short, concise and in condensed form that can be built on by the researcher later. Most researchers do not prefer this method though due to the fear of feeling that the respondent may not take them seriously.
• Field Notes Proper: These notes are to be expanded on immediately after the completion of events. The notes have to be detailed and the words have to be as close to possible as the subject being studied.
• Methodological Notes: These notes contain methods on the research methods used by the researcher, any new proposed research methods and the way to monitor their progress. Methodological notes can be kept with field notes or filed separately but they find their way to the end report of a study.
• Journals and Diaries: This method of field notes is an insight into the life of the researcher. This tracks all aspects of the researchers life and helps eliminate the Halo effect or any research bias that may have cropped up during the field research.

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Reasons to Conduct Field Research

Field research has been commonly used in the 20th century in the social sciences. But in general, it takes a lot of time to conduct and complete, is expensive and in a lot of cases invasive. So why then is this commonly used and is preferred by researchers to validate data? We look at 4 major reasons:

• Overcoming lack of data: Field research resolves the major issue of gaps in data. Very often, there is limited to no data about a topic in study, especially in a specific environment analysis . The research problem might be known or suspected but there is no way to validate this without primary research and data. Conducting field research helps not only plug-in gaps in data but collect supporting material and hence is a preferred research method of researchers.
• Understanding context of the study: In many cases, the data collected is adequate but field research is still conducted. This helps gain insight into the existing data. For example, if the data states that horses from a stable farm generally win races because the horses are pedigreed and the stable owner hires the best jockeys. But conducting field research can throw light into other factors that influence the success like quality of fodder and care provided and conducive weather conditions.
• Increasing the quality of data: Since this research method uses more than one tool to collect data, the data is of higher quality. Inferences can be made from the data collected and can be statistically analyzed via the triangulation of data.
• Collecting ancillary data: Field research puts the researchers in a position of localized thinking which opens them new lines of thinking. This can help collect data that the study didn’t account to collect.

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Examples of Field Research

Some examples of field research are:

• Decipher social metrics in a slum Purely by using observational methods and in-depth interviews, researchers can be part of a community to understand the social metrics and social hierarchy of a slum. This study can also understand the financial independence and day-to-day operational nuances of a slum. The analysis of this data can provide an insight into how different a slum is from structured societies.
• U nderstand the impact of sports on a child’s development This method of field research takes multiple years to conduct and the sample size can be very large. The data analysis of this research provides insights into how the kids of different geographical locations and backgrounds respond to sports and the impact of sports on their all round development.
• Study animal migration patterns Field research is used extensively to study flora and fauna. A major use case is scientists monitoring and studying animal migration patterns with the change of seasons. Field research helps collect data across years and that helps draw conclusions about how to safely expedite the safe passage of animals.

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Advantages of Field Research

The advantages of field research are:

• It is conducted in a real-world and natural environment where there is no tampering of variables and the environment is not doctored.
• Due to the study being conducted in a comfortable environment, data can be collected even about ancillary topics.
• The researcher gains a deep understanding into the research subjects due to the proximity to them and hence the research is extensive, thorough and accurate.

Disadvantages of Field Research

The disadvantages of field research are:

• The studies are expensive and time-consuming and can take years to complete.
• It is very difficult for the researcher to distance themselves from a bias in the research study.
• The notes have to be exactly what the researcher says but the nomenclature is very tough to follow.
• It is an interpretive method and this is subjective and entirely dependent on the ability of the researcher.
• In this method, it is impossible to control external variables and this constantly alters the nature of the research.

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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
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• Multiple Book Review Essay
• Reviewing Collected Works
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• About Informed Consent
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• Generative AI and Writing
• Acknowledgments

The purpose of a field report in the social sciences is to describe the deliberate observation of people, places, and/or events and to analyze what has been observed in order to identify and categorize common themes in relation to the research problem underpinning the study. The content represents the researcher's interpretation of meaning found in data that has been gathered during one or more observational events.

Flick, Uwe. The SAGE Handbook of Qualitative Data Collection . London: SAGE Publications, 2018; Lofland, John, David Snow, Leon Anderson, and Lyn H. Lofland. Analyzing Social Settings: A Guide to Qualitative Observation and Analysis. Long Grove, IL: Waveland Press, 2022; Baker, Lynda. "Observation: A Complex Research Method." Library Trends 55 (Summer 2006): 171-189.; Kellehear, Allan. The Unobtrusive Researcher: A Guide to Methods . New York: Routledge, 2020.

How to Approach Writing a Field Report

How to Begin

Field reports are most often assigned in disciplines of the applied social sciences [e.g., social work, anthropology, gerontology, criminal justice, education, law, the health care services] where it is important to build a bridge of relevancy between the theoretical concepts learned in the classroom and the practice of actually doing the work you are being taught to do. Field reports are also common in certain science disciplines [e.g., geology] but these reports are organized differently and serve a different purpose than what is described below.

Professors will assign a field report with the intention of improving your understanding of key theoretical concepts by applying methods of careful and structured observation of, and reflection about, people, places, or phenomena existing in their natural settings. Field reports facilitate the development of data collection techniques and observation skills and they help you to understand how theory applies to real world situations. Field reports are also an opportunity to obtain evidence through methods of observing professional practice that contribute to or challenge existing theories.

We are all observers of people, their interactions, places, and events; however, your responsibility when writing a field report is to conduct research based on data generated by the act of designing a specific study, deliberate observation, synthesis of key findings, and interpretation of their meaning.

When writing a field report you need to:

• Systematically observe and accurately record the varying aspects of a situation . Always approach your field study with a detailed protocol about what you will observe, where you should conduct your observations, and the method by which you will collect and record your data.
• Continuously analyze your observations . Always look for the meaning underlying the actions you observe. Ask yourself: What's going on here? What does this observed activity mean? What else does this relate to? Note that this is an on-going process of reflection and analysis taking place for the duration of your field research.
• Keep the report’s aims in mind while you are observing . Recording what you observe should not be done randomly or haphazardly; you must be focused and pay attention to details. Enter the observation site [i.e., "field"] with a clear plan about what you are intending to observe and record in relation to the research problem while, at the same time, being prepared to adapt to changing circumstances as they may arise.
• Consciously observe, record, and analyze what you hear and see in the context of a theoretical framework . This is what separates data gatherings from reporting. The theoretical framework guiding your field research should determine what, when, and how you observe and act as the foundation from which you interpret your findings in relation to the underlying assumptions embedded in the theoretical framework .

Techniques to Record Your Observations Although there is no limit to the type of data gathering techniques you can use, these are the most frequently used methods:

Note Taking This is the most common and easiest method of recording your observations. Tips for taking notes include: organizing some shorthand symbols beforehand so that recording basic or repeated actions does not impede your ability to observe, using many small paragraphs, which reflect changes in activities, who is talking, etc., and, leaving space on the page so you can write down additional thoughts and ideas about what’s being observed, any theoretical insights, and notes to yourself that are set aside for further investigation. See drop-down tab for additional information about note-taking.

Photography With the advent of smart phones, an almost unlimited number of high quality photographs can be taken of the objects, events, and people observed during a field study. Photographs can help capture an important moment in time as well as document details about the space where your observation takes place. Taking a photograph can save you time in documenting the details of a space that would otherwise require extensive note taking. However, be aware that flash photography could undermine your ability to observe unobtrusively so assess the lighting in your observation space; if it's too dark, you may need to rely on taking notes. Also, you should reject the idea that photographs represent some sort of "window into the world" because this assumption creates the risk of over-interpreting what they show. As with any product of data gathering, you are the sole instrument of interpretation and meaning-making, not the object itself. Video and Audio Recordings Video or audio recording your observations has the positive effect of giving you an unfiltered record of the observation event. It also facilitates repeated analysis of your observations. This can be particularly helpful as you gather additional information or insights during your research. However, these techniques have the negative effect of increasing how intrusive you are as an observer and will often not be practical or even allowed under certain circumstances [e.g., interaction between a doctor and a patient] and in certain organizational settings [e.g., a courtroom]. Illustrations/Drawings This does not refer to an artistic endeavor but, rather, refers to the possible need, for example, to draw a map of the observation setting or illustrating objects in relation to people's behavior. This can also take the form of rough tables, charts, or graphs documenting the frequency and type of activities observed. These can be subsequently placed in a more readable format when you write your field report. To save time, draft a table [i.e., columns and rows] on a separate piece of paper before an observation if you know you will be entering data in that way.

NOTE:   You may consider using a laptop or other electronic device to record your notes as you observe, but keep in mind the possibility that the clicking of keys while you type or noises from your device can be obtrusive, whereas writing your notes on paper is relatively quiet and unobtrusive. Always assess your presence in the setting where you're gathering the data so as to minimize your impact on the subject or phenomenon being studied.

ANOTHER NOTE:   Techniques of deliberate observation and data gathering are not innate skills; they are skills that must be learned and practiced in order to achieve proficiency. Before your first observation, practice the technique you plan to use in a setting similar to your study site [e.g., take notes about how people choose to enter checkout lines at a grocery store if your research involves examining the choice patterns of unrelated people forced to queue in busy social settings]. When the act of data gathering counts, you'll be glad you practiced beforehand.

YET ANOTHER NOTE:   An issue rarely discussed in the literature about conducting field research is whether you should move around the study site while observing or remaining situated in one place. Moving around can be intrusive, but it facilitates observing people's behavior from multiple vectors. However, if you remain in one place throughout the observation [or during each observation], you will eventually blend into the background and diminish the chance of unintentionally influencing people's behavior. If the site has a complex set of interactions or interdependent activities [e.g., a play ground], consider moving around; if the study site is relatively fixed [e.g., a classroom], then consider staying in one place while observing.

Examples of Things to Document While Observing

• Physical setting . The characteristics of an occupied space and the human use of the place where the observation(s) are being conducted.
• Objects and material culture . This refers to the presence, placement, and arrangement of objects that impact the behavior or actions of those being observed. If applicable, describe the cultural artifacts representing the beliefs [i.e., the values, ideas, attitudes, and assumptions] of the individuals you are observing [e.g., the choice of particular types of clothing in the observation of family gatherings during culturally specific holidays].
• Use of language . Don't just observe but  listen to what is being said, how is it being said, and the tone of conversations among participants.
• Behavior cycles . This refers to documenting when and who performs what behavior or task and how often they occur. Record at which stage this behavior is occurring within the setting.
• The order in which events unfold . Note sequential patterns of behavior or the moment when actions or events take place and their significance. Also, be prepared to note moments that diverge from these sequential patterns of behavior or actions.
• Physical characteristics of subjects. If relevant, document personal characteristics of individuals being observed. Note that, unless this data can be verified in interviews or from documentary evidence, you should only focus on characteristics that can be clearly observed [e.g., clothing, physical appearance, body language].
• Expressive body movements . This would include things like body posture or facial expressions. Note that it may be relevant to also assess whether expressive body movements support or contradict the language used in conversation [e.g., detecting sarcasm].

Brief notes about all of these examples contextualize your observations; however, your observation notes will be guided primarily by your theoretical framework, keeping in mind that your observations will feed into and potentially modify or alter these frameworks.

Sampling Techniques

Sampling refers to the process used to select a portion of the population for study . Qualitative research, of which observation is one method of data gathering, is generally based on non-probability and purposive sampling rather than probability or random approaches characteristic of quantitatively-driven studies. Sampling in observational research is flexible and often continues until no new themes emerge from the data, a point referred to as data saturation.

All sampling decisions are made for the explicit purpose of obtaining the richest possible source of information to answer the research questions. Decisions about sampling assumes you know what you want to observe, what behaviors are important to record, and what research problem you are addressing before you begin the study. These questions determine what sampling technique you should use, so be sure you have adequately answered them before selecting a sampling method.

Ways to sample when conducting an observation include:

• Ad Libitum Sampling -- this approach is not that different from what people do at the zoo; they observe whatever seems interesting at the moment. There is no organized system of recording the observations; you just note whatever seems relevant at the time. The advantage of this method is that you are often able to observe relatively rare or unusual behaviors that might be missed by more deliberately designed sampling methods. This method is also useful for obtaining preliminary observations that can be used to develop your final field study. Problems using this method include the possibility of inherent bias toward conspicuous behaviors or individuals, thereby missing mundane or repeated patterns of behavior, and that you may miss brief interactions in social settings.
• Behavior Sampling -- this involves watching the entire group of subjects and recording each occurrence of a specific behavior of interest and with reference to which individuals were involved. The method is useful in recording rare behaviors missed by other sampling methods and is often used in conjunction with focal or scan methods [see below]. However, sampling can be biased towards particular conspicuous behaviors.
• Continuous Recording -- provides a faithful record of behavior including frequencies, durations, and latencies [the time that elapses between a stimulus and the response to it]. This is a very demanding method because you are trying to record everything within the setting and, thus, measuring reliability may be sacrificed. In addition, durations and latencies are only reliable if subjects remain present throughout the collection of data. However, this method facilitates analyzing sequences of behaviors and ensures obtaining a wealth of data about the observation site and the people within it. The use of audio or video recording is most useful with this type of sampling.
• Focal Sampling -- this involves observing one individual for a specified amount of time and recording all instances of that individual's behavior. Usually you have a set of predetermined categories or types of behaviors that you are interested in observing [e.g., when a teacher walks around the classroom] and you keep track of the duration of those behaviors. This approach doesn't tend to bias one behavior over another and provides significant detail about a individual's behavior. However, with this method, you likely have to conduct a lot of focal samples before you have a good idea about how group members interact. It can also be difficult within certain settings to keep one individual in sight for the entire period of the observation without being intrusive.
• Instantaneous Sampling -- this is where observation sessions are divided into short intervals divided by sample points. At each sample point the observer records if predetermined behaviors of interest are taking place. This method is not effective for recording discrete events of short duration and, frequently, observers will want to record novel behaviors that occur slightly before or after the point of sampling, creating a sampling error. Though not exact, this method does give you an idea of durations and is relatively easy to do. It is also good for recording behavior patterns occurring at a specific instant, such as, movement or body positions.
• One-Zero Sampling -- this is very similar to instantaneous sampling, only the observer records if the behaviors of interest have occurred at any time during an interval instead of at the instant of the sampling point. The method is useful for capturing data on behavior patterns that start and stop repeatedly and rapidly, but that last only for a brief period of time. The disadvantage of this approach is that you get a dimensionless score for an entire recording session, so you only get one one data point for each recording session.
• Scan Sampling -- this method involves taking a census of the entire observed group at predetermined time periods and recording what each individual is doing at that moment. This is useful for obtaining group behavioral data and allows for data that are evenly representative across individuals and periods of time. On the other hand, this method may be biased towards more conspicuous behaviors and you may miss a lot of what is going on between observations, especially rare or unusual behaviors. It is also difficult to record more than a few individuals in a group setting without missing what each individual is doing at each predetermined moment in time [e.g., children sitting at a table during lunch at school]. The use of audio or video recording is useful with this type of sampling.

Alderks, Peter. Data Collection. Psychology 330 Course Documents. Animal Behavior Lab. University of Washington; Emerson, Robert M. Contemporary Field Research: Perspectives and Formulations . 2nd ed. Prospect Heights, IL: Waveland Press, 2001; Emerson, Robert M. et al. “Participant Observation and Fieldnotes.” In Handbook of Ethnography . Paul Atkinson et al., eds. (Thousand Oaks, CA: Sage, 2001), 352-368; Emerson, Robert M. et al. Writing Ethnographic Fieldnotes . 2nd ed. Chicago, IL: University of Chicago Press, 2011; Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University; Hazel, Spencer. "The Paradox from Within: Research Participants Doing-Being-Observed." Qualitative Research 16 (August 2016): 446-457; Pace, Tonio. Writing Field Reports. Scribd Online Library; Presser, Jon and Dona Schwartz. “Photographs within the Sociological Research Process.” In Image-based Research: A Sourcebook for Qualitative Researchers . Jon Prosser, editor (London: Falmer Press, 1998), pp. 115-130; Pyrczak, Fred and Randall R. Bruce. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 5th ed. Glendale, CA: Pyrczak Publishing, 2005; Report Writing. UniLearning. University of Wollongong, Australia; Wolfinger, Nicholas H. "On Writing Fieldnotes: Collection Strategies and Background Expectancies.” Qualitative Research 2 (April 2002): 85-95; Writing Reports. Anonymous. The Higher Education Academy.

Structure and Writing Style

How you choose to format your field report is determined by the research problem, the theoretical framework that is driving your analysis, the observations that you make, and/or specific guidelines established by your professor. Since field reports do not have a standard format, it is worthwhile to determine from your professor what the preferred structure and organization should be before you begin to write. Note that field reports should be written in the past tense. With this in mind, most field reports in the social sciences include the following elements:

I.  Introduction The introduction should describe the research problem, the specific objectives of your research, and the important theories or concepts underpinning your field study. The introduction should describe the nature of the organization or setting where you are conducting the observation, what type of observations you have conducted, what your focus was, when you observed, and the methods you used for collecting the data. Collectively, this descriptive information should support reasons why you chose the observation site and the people or events within it. You should also include a review of pertinent literature related to the research problem, particularly if similar methods were used in prior studies. Conclude your introduction with a statement about how the rest of the paper is organized.

II.  Description of Activities

Your readers only knowledge and understanding of what happened will come from the description section of your report because they were not witnesses to the situation, people, or events that you are writing about. Given this, it is crucial that you provide sufficient details to place the analysis that will follow into proper context; don't make the mistake of providing a description without context. The description section of a field report is similar to a well written piece of journalism. Therefore, a useful approach to systematically describing the varying aspects of an observed situation is to answer the "Five W’s of Investigative Reporting." As Dubbels notes [p. 19], these are:

• What -- describe what you observed. Note the temporal, physical, and social boundaries you imposed to limit the observations you made. What were your general impressions of the situation you were observing. For example, as a student teacher, what is your impression of the application of iPads as a learning device in a history class; as a cultural anthropologist, what is your impression of women's participation in a Native American religious ritual?
• Where -- provide background information about the setting of your observation and, if necessary, note important material objects that are present that help contextualize the observation [e.g., arrangement of computers in relation to student engagement with the teacher].
• When -- record factual data about the day and the beginning and ending time of each observation. Note that it may also be necessary to include background information or key events which impact upon the situation you were observing [e.g., observing the ability of teachers to re-engage students after coming back from an unannounced fire drill].
• Who -- note background and demographic information about the individuals being observed e.g., age, gender, ethnicity, and/or any other variables relevant to your study]. Record who is doing what and saying what, as well as, who is not doing or saying what. If relevant, be sure to record who was missing from the observation.
• Why -- why were you doing this? Describe the reasons for selecting particular situations to observe. Note why something happened. Also note why you may have included or excluded certain information.

III.  Interpretation and Analysis

Always place the analysis and interpretations of your field observations within the larger context of the theoretical assumptions and issues you described in the introduction. Part of your responsibility in analyzing the data is to determine which observations are worthy of comment and interpretation, and which observations are more general in nature. It is your theoretical framework that allows you to make these decisions. You need to demonstrate to the reader that you are conducting the field work through the eyes of an informed viewer and from the perspective of a casual observer.

Here are some questions to ask yourself when analyzing your observations:

• What is the meaning of what you have observed?
• Why do you think what you observed happened? What evidence do you have for your reasoning?
• What events or behaviors were typical or widespread? If appropriate, what was unusual or out of the ordinary? How were they distributed among categories of people?
• Do you see any connections or patterns in what you observed?
• Why did the people you observed proceed with an action in the way that they did? What are the implications of this?
• Did the stated or implicit objectives of what you were observing match what was achieved?
• What were the relative merits of the behaviors you observed?
• What were the strengths and weaknesses of the observations you recorded?
• Do you see connections between what you observed and the findings of similar studies identified from your review of the literature?
• How do your observations fit into the larger context of professional practice? In what ways have your observations possibly changed or affirmed your perceptions of professional practice?
• Have you learned anything from what you observed?

NOTE:   Only base your interpretations on what you have actually observed. Do not speculate or manipulate your observational data to fit into your study's theoretical framework.

IV.  Conclusion and Recommendations

The conclusion should briefly recap of the entire study, reiterating the importance or significance of your observations. Avoid including any new information. You should also state any recommendations you may have based on the results of your study. Be sure to describe any unanticipated problems you encountered and note the limitations of your study. The conclusion should not be more than two or three paragraphs.

V.  Appendix

This is where you would place information that is not essential to explaining your findings, but that supports your analysis [especially repetitive or lengthy information], that validates your conclusions, or that contextualizes a related point that helps the reader understand the overall report. Examples of information that could be included in an appendix are figures/tables/charts/graphs of results, statistics, pictures, maps, drawings, or, if applicable, transcripts of interviews. There is no limit to what can be included in the appendix or its format [e.g., a DVD recording of the observation site], provided that it is relevant to the study's purpose and reference is made to it in the report. If information is placed in more than one appendix ["appendices"], the order in which they are organized is dictated by the order they were first mentioned in the text of the report.

VI.  References

List all sources that you consulted and obtained information from while writing your field report. Note that field reports generally do not include further readings or an extended bibliography. However, consult with your professor concerning what your list of sources should be included and be sure to write them in the preferred citation style of your discipline or is preferred by your professor [i.e., APA, Chicago, MLA, etc.].

Alderks, Peter. Data Collection. Psychology 330 Course Documents. Animal Behavior Lab. University of Washington; Dubbels, Brock R. Exploring the Cognitive, Social, Cultural, and Psychological Aspects of Gaming and Simulations . Hershey, PA: IGI Global, 2018; Emerson, Robert M. Contemporary Field Research: Perspectives and Formulations . 2nd ed. Prospect Heights, IL: Waveland Press, 2001; Emerson, Robert M. et al. “Participant Observation and Fieldnotes.” In Handbook of Ethnography . Paul Atkinson et al., eds. (Thousand Oaks, CA: Sage, 2001), 352-368; Emerson, Robert M. et al. Writing Ethnographic Fieldnotes . 2nd ed. Chicago, IL: University of Chicago Press, 2011; Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University; Pace, Tonio. Writing Field Reports. Scribd Online Library; Pyrczak, Fred and Randall R. Bruce. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 5th ed. Glendale, CA: Pyrczak Publishing, 2005; Report Writing. UniLearning. University of Wollongong, Australia; Wolfinger, Nicholas H. "On Writing Fieldnotes: Collection Strategies and Background Expectancies.” Qualitative Research 2 (April 2002): 85-95; Writing Reports. Anonymous. The Higher Education Academy.

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Conducting Field Research

Some of the most valuable information in the world isn't located in a library or online. Field research is a way of unearthing that information. If you enjoy meeting and talking with people and don't mind what reporters call "legwork," you will relish the fun and satisfaction of obtaining ideas and information first hand.

Field research can be an extraordinarily exciting and rewarding experience leading to important discoveries and breakthrough ideas. Its goal is the same as research done in the library or on the Internet: to gather information that contributes to your understanding of an issue or question and to organize those findings in a cohesive and persuasive document that proposes a new insight, answer or solution.

Far from being at odds with one another-philosophically or practically-these three research techniques actually complement each other. Library and Internet research provides critical background information that prepares the researcher for making observations, and conducting interviews and surveys in the field.

The results will verify or refute, inform and help shape the answer to your research question.

Observing in the Field

First-hand observations will often be a key component in your research project. Your task is to take it all in, recording what you observe while being as unobtrusive as possible. You will want to take notes for future reference: interesting facts, telling details and sensory impressions (sights, sounds and smells), all help when it comes time to reconstruct your observations on paper.

Before you begin, it's important to do a little "legwork". Library and Internet research will help you build a list of possible sites from which to conduct your observation. Depending on the type of site you wish to observe, you may or may not need permission. It's important to find out.

A few phone calls or email inquiries will identify the contact person from whom you can get that information and the procedures you will be expected to follow. You may need to schedule an appointment, for instance.

A private business or a school will likely require identification when you arrive, so be prepared. You might ask your instructor for a statement on college or department letterhead declaring that you are a bona fide student and some specifics about your project and what you intend to do with the results.

In addition to note-taking, you may want to take some photographs or video-tape while observing. Permission for this will also likely be required, as well as waivers or releases signed by the human subjects involved.

Finally, before leaving the observation site, it's a good idea to schedule or request permission for a follow-up visit. When evaluating your initial observations it is highly likely that you will find gaps in your information that can only be filled by further observation. It is also quite possible that your evaluation will produce new ideas or expose areas of interest, previously unthought-of, that you may like to pursue. If not, you can always cancel the follow-up.

Interviewing Experts

Sometimes, the best information comes "straight from the horse's mouth". An interview is a conversation with a purpose; that being, to gather information from a person with first-hand knowledge-a primary source. Whenever possible, arrange a meeting with an expert in the field of your inquiry. Or, if you are investigating a particular group of people, interview a typical member, someone who represents the whole group and can speak for all of them.

You'll be surprised just how many people, from all walks of life, are willing to be interviewed-some even flattered by your attention. Choose them carefully. Regardless of who they are, prepare to interview them thoroughly. Chapter 4 "Writing from Conversation," in the Bedford Guide to Writing, offers some good advice:

• Make an appointment and schedule enough time-at least an hour.
• Be prompt and be prepared. Bring a list of carefully thought-out questions.
• Make sure your subject is willing to be quoted in writing.
• Really listen. This is the art of the interview. Let the person open up.
• Be flexible and allow the interview to go in unexpected directions.
• If a question goes unanswered, go on to the next question. You may be able to come back to it later.
• At the end of the interview, be sure to confirm any direct quotations you may use in your document.
• Make additional notes immediately after the interview, while the conversation is still fresh in your mind.

Be sure to take notes during the interview. These will come in handy later, when you reconstruct the interview on paper. Even when audio-recording, you should do this: In addition to recording important points and accurate quotations, notes allow you to record details that do not lend themselves to audio-recording. Your subject's mood, appearance and behavior, for instance, as well as your sensory impressions of the interview setting will come in handy when you begin constructing your document.

If an expert isn't readily available-perhaps the nearest one is too far away-you may be able to arrange a telephone interview. Make an appointment for a time convenient for both you and your subject. A busy person may not be able to give you even ten minutes on the spur of a moment, but all the time in the world if arranged for in advance. A further word of advice, don't try to wing-it; have written questions in hand before you dial. Take notes and follow all the other rules just as if you were doing the interview in person.

Note: Federal regulations forbid recording an interview over the phone without notifying the person being interviewed. When recording over the phone, you must also use a recorder connector with a warning device that emits a beeping signal at fifteen second intervals.

Corresponding with Experts

Is there a person whose knowledge or opinions you'd like to include in your research gathering but who lives too far away for a personal interview? A letter or e-mail message may do the trick. Be sure to make it short and polite. If you're sending a letter, it's a real good idea to enclose a stamped, self-addressed envelope along with your questions. And, if you're using e-mail, place your questions directly into the text of your email message so that the recipient can respond using the reply button.

Large corporations and organizations, branches of the military and the federal government as well as elected officials are all accustomed to being solicited in this manner. In fact, many of them employ public relations officers whose duties include responding to such solicitation. They will often supply you with free brochures, press releases and other source materials geared toward your inquiry.

Conducting Surveys

Surveys and questionnaires are as much a part of contemporary life as iPods and cell phones. In fact, many people enjoy having their knowledge tapped or their opinion solicited. Filling out a questionnaire can even have a game-like appeal: self-quiz features appear in popular magazines and tabloid newspapers all the time. "How Ambitious Are You?" will headline a thirty-question quiz that you can score yourself. Used judiciously, and with the following points in mind, you may find it useful to conduct a small survey yourself, as part of a research project.

Survey Basics

As a rule, professional pollsters, opinion testers, and survey takers solicit thousands of individuals when exploring the answers to a question. They are chosen to represent either a certain segment of society or a broad range of the populace diversified in geography, income, ethnic background, and education.

The purpose of a survey may be to inform a manufacturer when test-marketing a new product or identifying a new market. Politicians use them to plan their campaigns and judge the mood of their constituents. Regardless, they are widely used because they deliver large stores of useful information quickly and efficiently.

Few student research-writers conduct such extensive surveys as the time, money and effort required is prohibitive. For smaller, less prohibitive surveys then, it is best to report the results of your survey in non-statistical terms.

It's one thing to say that "many of the students" who filled out a questionnaire on reading habits hadn't read a newspaper in the past month; it's another to claim such is true of "seventy-two percent" of the student population when you were only able to give questionnaires to the twenty-two percent who were in the dining hall the day you were there and half of those threw them in the trash on the way out.

A far more useful and reliable way to use a questionnaire is to think of it as a group interview. Use it when you want to collect the same information from a large number of people or when you're more interested in what a group thinks as a whole than what a particular individual thinks. Treat the information you collect as representative and use your findings to build an overall knowledge of the subject or to cull them for interesting or persuasive details and quotations.

Define Your Purpose

If you think you want to use a survey to gather information, you need have a clearly defined purpose. You need to ask yourself:

• What am I trying to discover with this questionnaire?

You will want to build questions that are well thought out and which fulfill your purpose. If, for instance, you want to know how effective a day-care center is in the eyes of working mothers who entrust their children to it's care, you might ask questions like:

• Do your children report that they are happy there?
• Have you ever had reason to complain?
• If so, about what?

Keep it Simple

Any questionnaire you design has to be one that people are willing to answer. The main point is to make participation easy and inviting. If it's too complex or time-consuming, the recipient may throw it away.

Ask questions that call for a check mark in a list of alternative answers, a simple yes or no, or one word (at the most, just a few). As you write each question, check carefully for how well it fits your stated purpose. Check for ambiguity and whether they will elicit the kind of responses for which you are looking. It's a good idea to ask for just one piece of information per question.

Ask Open-Ended Questions

In addition to simple yes/no and multiple-choice questions, you might find it worth while to ask a few "open-ended questions". These call for short written responses. Although they are typically difficult to tally and are likely to draw a smaller number of responses, those you do get might supply a memorable quote or suggest some new ideas when you assess and analyze the results.

Avoid Slanted Questions

Be careful to build unbiased questions that will solicit factual responses. Don't ask:

• How religious are you?

You want to be able to report actual numbers or draw logical inferences about the respondents. Better questions might be:

• What is your religious affiliation?
• How often do you attend religious services?

Make it Easy for People to Respond

Whenever possible, distribute questionnaire at the end of a meeting or discussion. If that's not possible, assemble a group and have them fill out your questionnaire on the spot-at an evening coffee for parents with children in day-care, for instance. Facing them, you can explain the purpose of your research and invite questions, the answers to which will instill confidence and build interest.

If you must mail your questionnaire, include a concise letter explaining your purpose and what use you will make of the replies along with a self-addressed, stamped envelope. You might also indicate how much time will be required: no more than ten minutes, for instance, or some estimate identifying the task as reasonable and easy.

Professional pollsters often offer a small inducement, a morsel of bait to increase the rate of response: a small check or a coupon good for a free jar of pickles. You might promise a copy of your finished document, a brief report of the results, or a listing of each respondent's name in an acknowledgment.

Even with such inducements, professionals find a response rate of fifty percent or higher difficult, if not impossible, to achieve. That is why they often conduct surveys by telephone, with the caller filling in the questionnaire for the respondent. You might also use this technique, but better results will come if you distribute your questionnaire in person.

Tally the Results

When all the responses that you can reasonably expect to collect have been received, sit down and tally up the results. It's easy to count the short answers: the yes's and no's and the one word answers-like Republican or Democrat-but the longer ones are more difficult.

• What is your goal in life?

The answers to open-ended questions need to be summed up or paraphrased and then sorted into rough categories.

• To grow rich
• To serve humanity
• To save my soul

Similar responses can be lumped together and counted, accurately measuring the extent of, or pattern to, the available responses.

Media Genres: Television, Radio, Film, etc.

Intriguing possibilities for field research lie in the media. For a research paper about television and radio, movies, theater or music, you may find the materials close at hand. In the case of television, it's as close as the remote.

Our only advice is to review plenty: watch (or listen) to a large amount before drawing conclusions. Use a VCR, DVD, or other audio-video recording device to keep track of your research and to preserve a record for future reference.

Transcripts, broadcast tapes or telecasts may be available on request, or for a small fee, from network or cable stations. Call, write or email your inquiries.

Lectures, Conferences, Online Forums, and Other Public Discussions

Field research often involves attending a lecture, conference or other public discussion. College organizations frequently bring interesting speakers to campus: the science club might sponsor a nationally known marine biologist, for instance, or the film club might bring in the producer of a successful television program.

Likewise, bound by mutual affiliation, professionals and members of special-interest groups are brought together at regularly scheduled regional and national conferences across this country every day. Regardless of cost, you may want to attend one that addresses your particular research interests.

Health-care providers, legal experts, engineers, scientists and teachers attend them frequently in the course of their professional duties and to further they're careers. You can do the same. They can be a fertile source of fresh ideas and are often open to the public: sometimes admission is free or student discounts are available.

Regardless of cost, attending a professional conference affords an opportunity for taking notes at lectures given by experts and the chance to meet and talk with speakers and fellow attendees as well as to learn and practice the language of a discipline. In addition, you may be able to obtain a copy of the proceedings-usually a set of all the lectures delivered, sometimes with accompanying commentary.

Be on the lookout as well for online discussions such as Chat Room sessions sponsored by Yahoo or CNN Online-that are relevant to your research topic. You can participate in the discussion as an observer, or participate by posting questions. Remember to use your Chat Room program to record the session for later review. You can learn how to record a transcript by consulting the program's online help.

Palmquist, Mike, & Peter Connor. (2007). Conducting Field Research. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=23

Child Care and Early Education Research Connections

Field research.

Field research is a qualitative method of research concerned with understanding and interpreting the social interactions of groups of people, communities, and society by observing and interacting with people in their natural settings. The methods of field research include: direct observation, participant observation, and qualitative interviews. Each of these methods is described here. Terms related to these and other topics in field research are defined in the  Research Glossary .

Direct Observation

Participant observation, qualitative interviews.

Direct observation  is a method of research where the researcher watches and records the activities of individuals or groups engaged in their daily activities. The observations may be unstructured or structured. Unstructured observations involve the researcher observing people and events and recording his/her observations as field notes. Observations are recorded holistically and without the aid of a predetermined guide or protocol. Structured observation, on the other hand, is a technique where a researcher observes people and events using a guide or set protocol that has been developed ahead of time.

Other features of direct observation include:

• The observer does not actively engage the subjects of the study in conversations or interviews, but instead strives to be unobtrusive and detached from the setting.
• Data collected through direct observation may include field notes, checklists and rating scales, documents, and photographs or video images.
• Direct observation is not necessarily an alternative to other types of field methods, such as participant observation or qualitative interviews. Rather, it may be an initial approach to understanding a setting, a group of individuals, or forms of behavior prior to interacting with members or developing interview protocols.
• Direct observation as a research method is most appropriate in open, public settings where anyone has a right to be or congregate. Conducting direct observation in private or closed settings -- without the knowledge or consent of members -- is more likely to raise ethical concerns.

Participant observation  is a field research method whereby the researcher develops an understanding of a group or setting by taking part in the everyday routines and rituals alongside its members. It was originally developed in the early 20th century by anthropologists researching native societies in developing countries. It is now the principal research method used by ethnographers -- specialists within the fields of anthropology and sociology who focus on recording the details of social life occurring in a setting, community, group, or society. The ethnographer, who often lives among the members for months or years, attempts to build trusting relationships so that he or she becomes part of the social setting. As the ethnographer gains the confidence and trust of the members, many will speak and behave in a natural manner in the presence of the ethnographer.

Data from participant observation studies can take several forms:

• Field notes are the primary type of data. The researcher takes notes of his/her observations and experiences and later develops them into detailed, formal field notes.
• Frequently, researchers keep a diary, which is often a more intimate, informal record of the happenings within the setting.
• The practice of participant observation, with its emphasis on developing relationships with members, often leads to both informal, conversational interviews and more formal, in-depth interviews. The data from these interviews can become part of field notes or may consist of separate interview transcripts.

There are a number of advantages and disadvantages to direct and participant observation studies. Here is a list of some of both. While the advantages and disadvantages apply to both types of studies, their impact and importance may not be the same across the two. For example, researchers engaged in both types of observation will develop a rich, deep understanding of the members of the group and the setting in which social interactions occur, but researchers engaged in participant observation research may gain an even deep understanding. And, participant observers have a greater chance of witnessing a wider range of behaviors and events than those engaged in direct observation.

Advantages of observation studies (observational research):

• Provide contextual data on settings, interactions, or individuals.
• A useful tool for generating hypotheses for further study.
• Source of data on events and phenomena that do not involve verbal interactions (e.g., mother-child nonverbal interactions and contact, physical settings where interactions occur).
• The researcher develops a rich, deep understanding of a setting and of the members within the setting.

Disadvantages of observation studies:

• Behaviors observed during direct observation may be unusual or atypical.
• Significant interactions and events may take place when observer is not present.
• Certain topics do not necessarily lend themselves to observation (e.g., attitudes, emotions, affection).
• Reliability of observations can be problematic, especially when multiple observers are involved.
• The researcher must devote a large amount of time (and resources).
• The researcher's objectivity may decline as he or she spends more time among the members of the group.
• The researcher may be faced with a dilemma of choosing between revealing and not revealing his or her identity as a researcher to the members of the group. If he or she introduces him/herself as a researcher, the members may behave differently than if they assume that he or she is just another participant. On the other hand, if the researcher does not, they may feel betrayed upon learning about the research.

Qualitative interviews  are a type of field research method that elicits information and data by directly asking questions of individuals. There are three primary types of qualitative interviews: informal (conversational), semi-structured, and standardized, open-ended. Each is described briefly below along with advantages and disadvantages.

Informal (Conversational) Interviews

• Frequently occur during participant observation or following direct observation.
• The researcher begins by conversing with a member of the group of interest. As the conversation unfolds, the researcher formulates specific questions, often spontaneously, and begins asking them informally.
• Appropriate when the researcher wants maximum flexibility to pursue topics and ideas as they emerge during the exchange

Advantages of informal interviewing:

• Allows the researcher to be responsive to individual differences and to capture emerging information.
• Information that is obtained is not constrained by a predetermined set of questions and/or response categories.
• Permits researcher to delve deeper into a topic and what key terms and constructs mean to study participants.

Disadvantages of informal interviewing:

• May generate less systematic data, which is difficult to classify and analyze.
• The researcher might not be able to capture everything that the interviewee is saying and therefore there is potential for important nuance or information to be lost. For example, the researcher might not have a tape recorder at that moment due to the spontaneous nature of these interviews.
• Quality of the information obtained depends on skills of the interviewer.

Semi-Structured Interviews

• Prior to the interview, a list of predetermined questions or probes, also known as an interview guide, is developed so that each interviewee will respond to a similar series of questions and topics.
• Questions are generally open-ended to elicit as much detail and meaning from the interviewee as possible.
• The researcher is free to pursue and probe other topics as they emerge during the interview.

Advantages of semi-structured interviewing:

• Systematically captures data across interviewees.
• The researcher is able to rephrase or explain questions to the interviewee to ensure that everyone understands the questions the same way and probe (follow-up) a response so that an individual's responses are fully explored.
• Interviewee is allowed the freedom to express his or her views in their own words.

Disadvantages of semi-structured interviewing:

• Does not offer as much flexibility to respond to new topics that unfold during the interview as the informal interview.
• Responses to questions that have been asked in slightly different ways can be more difficult to compare and analyze.
• Interviewer may unconsciously send signals about the types of answers that are expected.

Standardized, Open-Ended Interviews

• Similar to a survey since questions are carefully scripted and written prior to the interview, which serves to minimize variability in question wording and the way questions are asked.
• The researcher asks a uniform series of questions in the same order to each interviewee.
• The questions are open-ended to capture more details and individual differences across interviewees.
• Particularly appropriate for qualitative studies involving multiple interviewers.

Advantages of standardized interviewing:

• All questions are asked the same to each study participant. Data are comparable across interviewees.
• Reduces interviewer effects when several interviewers are used.
• Standardization helps to facilitate the processing and analysis of the data.

Disadvantages of standardized interviewing:

• Does not offer as much flexibility to respond to and probe new topics that unfold during the interview.
• Standardized wording of questions may limit the responses of those being interviewed.

Both standardized and semi-structured interviews involve formally recruiting participants and are typically tape-recorded. The researcher should begin with obtaining informed consent from the interviewee prior to starting the interview. Additionally, the researcher may write a separate field note to describe the interviewee's reactions to the interview, or events that occurred before or after the interview.

See the following for additional information about field research and qualitative research methods.

• Ethnography, Observational Research and Narrative Inquiry  (PDF)
• An Introduction to Qualitative Research  (PDF)

The content on this page was prepared by Jerry West. It was last updated March 2019.

Organizing Academic Research Papers: Writing a Field Report

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

Field reports require the researcher to combine theory and analysis learned in the classroom with methods of observation and practice applied outside of the classroom. The purpose of field reports is to describe an observed person, place, or event and to analyze that observation data in order to identify and categorize common themes in relation to the research problem(s) underpinning the study. The data is often in the form of notes taken during the observation but it can also include any form of data gathering, such as, photography, illustrations, or audio recordings.

How to Approach Writing a Field Report

How to Begin

Field reports are most often assigned in the applied social sciences [e.g., social work, anthropology, gerontology, criminal justice, education, law, the health care professions] where it is important to build a bridge of relevancy between the theoretical concepts learned in the classroom and the practice of actually doing the work you are being taught to do. Field reports are also common in certain science and technology disciplines [e.g., geology] but these reports are organized differently and for different purposes than what is described below.

Professors will assign a field report with the intention of improving your understanding of key theoretical concepts through a method of careful and structured observation of and reflection about real life practice. Field reports facilitate the development of data collection techniques and observation skills and allow you to understand how theory applies to real world situations. Field reports are also an opportunity to obtain evidence through methods of observing professional practice that challenge or refine existing theories.

We are all observers of people, their interactions, places, and events; however, your responsibility when writing a field report is to create a research study based on data generated by the act of observation, a synthesis of key findings, and an interpretation of their meaning. When writing a field report you need to:

• Systematically observe and accurately record the varying aspects of a situation . Always approach your field study with a detailed plan about what you will observe, where you should conduct your observations, and the method by which you will collect and record your data.
• Continuously analyze your observations . Always look for the meaning underlying the actions you observe. Ask yourself: What's going on here? What does this observed activity mean? What else does this relate to? Note that this is an on-going process of reflection and analysis taking place for the duration of your field research.
• Keep the report’s aims in mind while you are observing . Recording what you observe should not be done randomly or haphazardly; you must be focused and pay attention to details. Enter the field with a clear plan about what you are intending to observe and record while, at the same time, be prepared to adapt to changing circumstances as they may arise.
• Consciously observe, record, and analyze what you hear and see in the context of a theoretical framework . This is what separates data gatherings from simple reporting. The theoretical framework guiding your field research should determine what, when, and how you observe and act as the foundation from which you interpret your findings.

Techniques to Record Your Observations Note Taking This is the most commonly used and easiest method of recording your observations. Tips for taking notes include: organizing some shorthand symbols beforehand so that recording basic or repeated actions does not impede your ability to observe, using many small paragraphs, which reflect changes in activities, who is talking, etc., and, leaving space on the page so you can write down additional thoughts and ideas about what’s being observed, any theoretical insights, and notes to yourself about may require further investigation. See drop-down tab for additional information about note-taking. Video and Audio Recordings Video or audio recording your observations has the positive effect of giving you an unfiltered record of the observation event. It also facilitates repeated analysis of your observations. However, these techniques have the negative effect of increasing how intrusive you are as an observer and will often not be practical or even allowed under certain circumstances [e.g., interaction between a doctor and a patient] and in certain organizational settings [e.g., a courtroom]. Illustrations/Drawings This does not an artistic endeavor but, rather, refers to the possible need, for example, to draw a map of the observation setting or illustrating objects in relation to people's behavior. This can also take the form of rough tables or graphs documenting the frequency and type of activities observed. These can be subsequently placed in a more readable format when you write your field report.

Examples of Things to Document While Observing

• Physical setting . The characteristics of an occupied space and the human use of the place where the observation(s) are being conducted.
• Objects and material culture . The presence, placement, and arrangement of objects that impact the behavior or actions of those being observed. If applicable, describe the cultural artifacts representing the beliefs--values, ideas, attitudes, and assumptions--used by the individuals you are observing.
• Use of language . Don't just observe but listen to what is being said, how is it being said, and, the tone of conversation among participants.
• Behavior cycles . This refers to documenting when and who performs what behavior or task and how often they occur. Record at which stage is this behavior occurring within the setting.
• The order in which events unfold . Note sequential patterns of behavior or the moment when actions or events take place and their significance.
• Physical characteristics of subjects. If relevant, note age, gender, clothing, etc. of individuals.
• Expressive body movements . This would include things like body posture or facial expressions. Note that it may be relevant to also assess whether expressive body movements support or contradict the use of language.

Brief notes about all of these examples contextualize your observations; however, your observation notes will be guided primarily by your theoretical framework, keeping in mind that your observations will feed into and potentially modify or alter these frameworks.

Sampling Techniques

Sampling refers to the process used to select a portion of the population for study . Qualitative research, of which observation is one method, is generally based on non-probability and purposive sampling rather than probability or random approaches characteristic of quantitatively-driven studies. Sampling in observational research is flexible and often continues until no new themes emerge from the data, a point referred to as data saturation.

All sampling decisions are made for the explicit purpose of obtaining the richest possible source of information to answer the research questions. Decisions about sampling assumes you know what you want to observe, what behaviors are important to record, and what research problem you are addressing before you begin the study. These questions determine what sampling technique you should use, so be sure you have adequately answered them before selecting a sampling method.

Ways to sample when conducting an observation include:

Ad Libitum Sampling -- this approach is not that different from what people do at the zoo--observing whatever seems interesting at the moment. There is no organized system of recording the observations; you just note whatever seems relevant at the time. The advantage of this method is that you are often able to observe relatively rare or unusual behaviors that might be missed by more deliberate sampling methods. This method is also useful for obtaining preliminary observations that can be used to develop your final field study. Problems using this method include the possibility of inherent bias toward conspicuous behaviors or individuals and that you may miss brief interactions in social settings.

Behavior Sampling -- this involves watching the entire group of subjects and recording each occurance of a specific behavior of particular interest and with reference to which individuals were involved. The method is useful in recording rare behaviors missed by other sampling methods and is often used in conjunction with focal or scan methods. However, sampling can be biased towards particular conspicuous behaviors.

Continuous Recording -- provides a faithful record of behavior including frequencies, durations, and latencies [the time that elapses between a stimulus and the response to it]. This is a very demanding method because you are trying to record everything within the setting and, thus, measuring reliability may be sacrificed. In addition, durations and latencies are only reliable if subjects remain present throughout the collection of data. However, this method facilitates analyzing sequences of behaviors and ensures obtaining a wealth of data about the observation site and the people within it. The use of audio or video recording is most useful with this type of sampling.

Focal Sampling -- this involves observing one individual for a specified amount of time and recording all instances of that individual's behavior. Usually you have a set of predetermined categories or types of behaviors that you are interested in observing [e.g., when a teacher walks around the classroom] and you keep track of the duration of those behaviors. This approach doesn't tend to bias one behavior over another and provides significant detail about a individual's behavior. However, with this method, you likely have to conduct a lot of focal samples before you have a good idea about how group members interact. It can also be difficult within certain settings to keep one individual in sight for the entire period of the observation.

Instantaneous Sampling -- this is where observation sessions are divided into short intervals divided by sample points. At each sample point the observer records if predetermined behaviors of interest are taking place. This method is not effective for recording discrete events of short duration and, frequently, observers will want to record novel behaviors that occur slightly before or after the point of sampling, creating a sampling error. Though not exact, this method does give you an idea of durations and is relatively easy to do. It is also good for recording behavior patterns occurring at a specific instant, such as, movement or body positions.

One-Zero Sampling -- this is very similar to instantaneous sampling, only the observer records if the behaviors of interest have occurred at any time during an interval instead of at the instant of the sampling point. The method is useful for capturing data on behavior patterns that start and stop repeatedly and rapidly, but that last only for a brief period of time. The disadvantage of this approach is that you get a dimensionless score for an entire recording session, so you only get one one data point for each recording session.

Scan Sampling -- this method involves taking a census of the entire observed group at predetermined time periods and recording what each individual is doing at that moment. This is useful for obtaining group behavioral data and allows for data that are evenly representative across individuals and periods of time. On the other hand, this method may be biased towards more conspicuous behaviors and you may miss a lot of what is going on between observations, especially rare or unusual behaviors.

Alderks, Peter. Data Collection. Psychology 330 Course Documents. Animal Behavior Lab. University of Washington; Emerson, Robert M. Contemporary Field Research: Perspectives and Formulations. 2nd ed. Prospect Heights, IL: Waveland Press, 2001; Emerson, Robert M. et al. “Participant Observation and Fieldnotes.” In Handbook of Ethnography. Paul Atkinson et al., eds. (Thousand Oaks, CA: Sage, 2001), 352-368; Emerson, Robert M. et al. Writing Ethnographic Fieldnotes. 2nd ed. Chicago, IL: University of Chicago Press, 2011; Ethnography, Observational Research, and Narrative Inquiry . Writing@CSU. Colorado State University; Pace, Tonio. Writing Field Reports . Scribd Online Library; Pyrczak, Fred and Randall R. Bruce. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences. 5th ed. Glendale, CA: Pyrczak Publishing, 2005; Report Writing . UniLearning. University of Wollongong, Australia; Wolfinger, Nicholas H. On Writing Fieldnotes: Collection Strategies and Background Expectancies.” Qualitative Research 2 (April 2002): 85-95; Writing Reports . Anonymous. The Higher Education Academy.

Structure and Writing Style

How you choose to format your field report is determined by the research problem, the theoretical perspective that is driving your analysis, the observations that you make, and/or specific guidelines established by your professor. Since field reports do not have a standard format, it is worthwhile to determine from your professor what the preferred organization should be before you begin to write. Note that field reports should be written in the past tense. With this in mind, most field reports in the social sciences include the following elements:

I.  Introduction The introduction should describe the specific objective and important theories or concepts underpinning your field study. The introduction should also describe the nature of the organization or setting where you are conducting the observation, what type of observations you have conducted, what your focus was, when you observed, and the methods you used for collecting the data. You should also include a review of pertinent literature.

II.  Description of Activities

Your readers only knowledge and understanding of what happened will come from the description section of your report because they have not been witness to the situation, people, or events that you are writing about. Given this, it is crucial that you provide sufficient details to place the analysis that will follow into proper context; don't make the mistake of providing a description without context. The description section of a field report is similar to a well written piece of journalism. Therefore, a helpful approach to systematically describing the varying aspects of an observed situation is to answer the "Five W’s of Investigative Reporting." These are:

• What -- describe what you observed. Note the temporal, physical, and social boundaries you imposed to limit the observations you made. What were your general impressions of the situation you were observing. For example, as a student teacher, what is your impression of the application of iPads as a learning device in a history class; as a cultural anthropologist, what is your impression of women participating in a Native American religious ritual?
• Where -- provide background information about the setting of your observation and, if necessary, note important material objects that are present that help contextualize the observation [e.g., arrangement of computers in relation to student engagement with the teacher].
• When -- record factual data about the day and the beginning and ending time of each observation. Note that it may also be necessary to include background information or key events which impact upon the situation you were observing [e.g., observing the ability of teachers to re-engage students after coming back from an unannounced fire drill].
• Who -- note the participants in the situation in terms of age, gender, ethnicity, and/or any other variables relevant to your study. Record who is doing what and saying what, as well as, who is not doing or saying what. If relevant, be sure to record who was missing from the observation.
• Why -- why were you doing this? Describe the reasons for selecting particular situations to observe. Note why something happened. Also note why you may have included or excluded certain information.

III.  Interpretation and Analysis

Always place the analysis and interpretations of your field observations within the larger context of the theories and issues you described in the introduction. Part of your responsibility in analyzing the data is to determine which observations are worthy of comment and interpretation, and which observations are more general in nature. It is your theoretical framework that allows you to make these decisions. You need to demonstrate to the reader that you are looking at the situation through the eyes of an informed viewer, not as a lay person.

Here are some questions to ask yourself when analyzing your observations:

• What is the meaning of what you have observed?
• Why do you think what you observed happened? What evidence do you have for your reasoning?
• What events or behaviors were typical or widespread? If appropriate, what was unusual or out of ordinary? How were they distributed among categories of people?
• Do you see any connections or patterns in what you observed?
• Why did the people you observed proceed with an action in the way that they did? What are the implications of this?
• Did the stated or implicit objectives of what you were observing match what was achieved?
• What were the relative merits of the behaviors you observed?
• What were the strengths and weaknesses of the observations you recorded?
• Do you see connections between what you observed and the findings of similar studies identified from your review of the literature?
• How do your observations fit into the larger context of professional practice? In what ways have your observations possibly changed your perceptions of professional practice?
• Have you learned anything from what you observed?

NOTE: Only base your interpretations on what you have actually observed. Do not speculate or manipulate your observational data to fit into your study's theoretical framework.

IV.  Conclusion and Recommendations

The conclusion should briefly recap of the entire study, reiterating the importance or significance of your observations. Avoid including any new information. You should also state any recommendations you may have. Be sure to describe any unanticipated problems you encountered and note the limitations of your study. The conclusion should not be more than two or three paragraphs.

V.  Appendix

This is where you would place information that is not essential to explaining your findings, but that supports your analysis [especially repetitive or lengthy information], that validates your conclusions, or that contextualizes a related point that helps the reader understand the overall report. Examples of information that could be included in an appendix are figures/tables/charts/graphs of results, statistics, pictures, maps, drawings, or, if applicable, transcripts of interviews. There is no limit to what can be included in the appendix or its format [e.g., a DVD recording of the observation site], provided that it is relevant to the study's purpose and reference is made to it in the report. If information is placed in more than one appendix ["appendices"], the order in which they are organized is dictated by the order they were first mentioned in the text of the report.

VI.  References

List all sources that you consulted and obtained information from while writing your field report. Note that field reports generally do not include further readings or an extended bibliography. However, consult with your professor concerning what your list of sources should be included. Be sure to write them in the preferred citation style of your discipline [i.e., APA, Chicago, MLA, etc.].

Alderks, Peter. Data Collection. Psychology 330 Course Documents. Animal Behavior Lab. University of Washington; Emerson, Robert M. Contemporary Field Research: Perspectives and Formulations. 2nd ed. Prospect Heights, IL: Waveland Press, 2001; Emerson, Robert M. et al. “Participant Observation and Fieldnotes.” In Handbook of Ethnography. Paul Atkinson et al., eds. (Thousand Oaks, CA: Sage, 2001), 352-368; Emerson, Robert M. et al. Writing Ethnographic Fieldnotes. 2nd ed. Chicago, IL: University of Chicago Press, 2011; Ethnography, Observational Research, and Narrative Inquiry . Writing@CSU. Colorado State University; Pace, Tonio. Writing Field Reports . Scribd Online Library; Pyrczak, Fred and Randall R. Bruce. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences. 5th ed. Glendale, CA: Pyrczak Publishing, 2005; Report Writing. UniLearning. University of Wollongong, Australia; Wolfinger, Nicholas H. On Writing Fieldnotes: Collection Strategies and Background Expectancies.” Qualitative Research 2 (April 2002): 85-95; Writing Reports. Anonymous. The Higher Education Academy.

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Field Research : Definition, Examples & Methodology

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Table of Contents

What is field research.

Field Research is a method of collecting qualitative data with the aim to understand, observe, and interact with people in their natural setting. It requires specialized market research tools . The goal is to understand how a subject behaves in a specific setting to identify how different variables in this setting may be interacting with the subject. Field research is used most in the field of social science, such as anthropology and health care professions, as in these fields it is vital to create a bridge between theory and practice.

Methods of Field Research

There are 4 main methods of conducting field research, and they are as follows:

• Ethnography

Ethnography is a kind of fieldwork that aims to record and analyse a particular culture, society, or community. This method defines social anthropology, and it usually involves the complete immersion of an anthropologist in the culture and everyday life of the community they are trying to study.

      2. Qualitative Interviews

The goal of qualitative interviews is to provide a researcher with a breadth of information that they can sift through in order to make inferences of their sample group. It does so through interviews by directly asking participants questions. There are three types of qualitative interviews; informal, conversational, and open ended.

     3. Direct observation

This method of field research involves researchers gathering information on their subject through close visual inspection in their natural setting. The researcher, and in this case the observer, remains unobtrusive and detached in order to not influence the behavior of their subject. 

     4. Participant Observation 

In this method of field research, the researchers join people by participating in certain group activities relating to their study in order to observe the participants in the context of said activity. 

Steps to conduct Field Research

The following are some key steps taken in conducting field research:

• Identifying and obtaining a team of researchers who are specialized in the field of research of the study.
• Identifying the right method of field research for your research topic. The various methods of field research are discussed above. A lot of factors will play a role in deciding what method a researcher chooses, such as duration of the study, financial limitations, and type of study.
• Visiting the site/setting of the study in order to study the main subjects of the study.
• Analyzing the data collected through field research.
• Constructively communicating the results of the field research, whether that be through a research paper or newspaper article etc.

Reasons to conduct Field Research

The following are a few reasons as to why field research is conducted, typically via market research tools :

• To understand the context of studies : field research allows researchers to identify the setting of their subjects to draw correlations between how their surroundings may be affecting certain behaviors.
• To acquire in-depth and high quality data :  Field research provides in-depth information as subjects are observed and analysed for a long period of time.
• When there is a lack of data on a certain subject : field research can be used to fill gaps in data that may only be filled through in-depth primary research.

Explore all the survey question types possible on Voxco

Examples of field research.

The following are real studies conducted using field research in order to answer questions about human behavior in certain settings:  

• William Foote Whyte used participant observation in his 1942 study to answer the question “How is the social structure of a local “slum” organized?”.  The study involved over 3 years of participation and observations among an Italian community in Boston’s North End.
• Liebow’s study in 1967 involved twenty months of participation and observations among an African American community in Washington, DC, to answer the question “How do the urban poor live?”.
• American sociologist, Cheri Jo Pascoe, conducted eighteen months of observations and interviews in a racially diverse working-class high school to answer the question “How is masculinity constructed by and among high school students, and what does this mean for our understanding of gender and sexuality?”.

Advantages of Field Research

• Can yield detailed data as researchers get to observe their subjects in their own setting.
• May uncover new social facts : Field research can be used to uncover social facts that may not be easily discernible, and that the research participants may also be unaware of.

No tampering of variables as methods of field research are conducted in natural settings in the real world. Voxco’s mobile offline research software is a powerful tool for conducting field research.

Disadvantages of Field Research

• Expensive to collect : most methods of field research involve the researcher to immerse themselves into new settings for long periods of time in order to acquire in-depth data. This can be expensive.
• Time consuming : Field research is time consuming to conduct.
• Information gathered may lack breadth : Field research involves in-depth studies and will usually tend to have a small sample group as researchers may be unable to collect in-depth data from large groups of people.

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• How to write a research paper

Last updated

11 January 2024

Reviewed by

With proper planning, knowledge, and framework, completing a research paper can be a fulfilling and exciting experience. 

Though it might initially sound slightly intimidating, this guide will help you embrace the challenge. 

By documenting your findings, you can inspire others and make a difference in your field. Here's how you can make your research paper unique and comprehensive.

• What is a research paper?

Research papers allow you to demonstrate your knowledge and understanding of a particular topic. These papers are usually lengthier and more detailed than typical essays, requiring deeper insight into the chosen topic.

To write a research paper, you must first choose a topic that interests you and is relevant to the field of study. Once you’ve selected your topic, gathering as many relevant resources as possible, including books, scholarly articles, credible websites, and other academic materials, is essential. You must then read and analyze these sources, summarizing their key points and identifying gaps in the current research.

You can formulate your ideas and opinions once you thoroughly understand the existing research. To get there might involve conducting original research, gathering data, or analyzing existing data sets. It could also involve presenting an original argument or interpretation of the existing research.

Writing a successful research paper involves presenting your findings clearly and engagingly, which might involve using charts, graphs, or other visual aids to present your data and using concise language to explain your findings. You must also ensure your paper adheres to relevant academic formatting guidelines, including proper citations and references.

Overall, writing a research paper requires a significant amount of time, effort, and attention to detail. However, it is also an enriching experience that allows you to delve deeply into a subject that interests you and contribute to the existing body of knowledge in your chosen field.

• How long should a research paper be?

Research papers are deep dives into a topic. Therefore, they tend to be longer pieces of work than essays or opinion pieces. 

However, a suitable length depends on the complexity of the topic and your level of expertise. For instance, are you a first-year college student or an experienced professional? 

Also, remember that the best research papers provide valuable information for the benefit of others. Therefore, the quality of information matters most, not necessarily the length. Being concise is valuable.

Following these best practice steps will help keep your process simple and productive:

1. Gaining a deep understanding of any expectations

Before diving into your intended topic or beginning the research phase, take some time to orient yourself. Suppose there’s a specific topic assigned to you. In that case, it’s essential to deeply understand the question and organize your planning and approach in response. Pay attention to the key requirements and ensure you align your writing accordingly. 

This preparation step entails

Deeply understanding the task or assignment

Being clear about the expected format and length

Familiarizing yourself with the citation and referencing requirements 

Understanding any defined limits for your research contribution

Where applicable, speaking to your professor or research supervisor for further clarification

2. Choose your research topic

Select a research topic that aligns with both your interests and available resources. Ideally, focus on a field where you possess significant experience and analytical skills. In crafting your research paper, it's crucial to go beyond summarizing existing data and contribute fresh insights to the chosen area.

Consider narrowing your focus to a specific aspect of the topic. For example, if exploring the link between technology and mental health, delve into how social media use during the pandemic impacts the well-being of college students. Conducting interviews and surveys with students could provide firsthand data and unique perspectives, adding substantial value to the existing knowledge.

When finalizing your topic, adhere to legal and ethical norms in the relevant area (this ensures the integrity of your research, protects participants' rights, upholds intellectual property standards, and ensures transparency and accountability). Following these principles not only maintains the credibility of your work but also builds trust within your academic or professional community.

For instance, in writing about medical research, consider legal and ethical norms , including patient confidentiality laws and informed consent requirements. Similarly, if analyzing user data on social media platforms, be mindful of data privacy regulations, ensuring compliance with laws governing personal information collection and use. Aligning with legal and ethical standards not only avoids potential issues but also underscores the responsible conduct of your research.

3. Gather preliminary research

Once you’ve landed on your topic, it’s time to explore it further. You’ll want to discover more about available resources and existing research relevant to your assignment at this stage. 

This exploratory phase is vital as you may discover issues with your original idea or realize you have insufficient resources to explore the topic effectively. This key bit of groundwork allows you to redirect your research topic in a different, more feasible, or more relevant direction if necessary. 

Spending ample time at this stage ensures you gather everything you need, learn as much as you can about the topic, and discover gaps where the topic has yet to be sufficiently covered, offering an opportunity to research it further. 

4. Define your research question

To produce a well-structured and focused paper, it is imperative to formulate a clear and precise research question that will guide your work. Your research question must be informed by the existing literature and tailored to the scope and objectives of your project. By refining your focus, you can produce a thoughtful and engaging paper that effectively communicates your ideas to your readers.

5. Write a thesis statement

A thesis statement is a one-to-two-sentence summary of your research paper's main argument or direction. It serves as an overall guide to summarize the overall intent of the research paper for you and anyone wanting to know more about the research.

A strong thesis statement is:

Concise and clear: Explain your case in simple sentences (avoid covering multiple ideas). It might help to think of this section as an elevator pitch.

Specific: Ensure that there is no ambiguity in your statement and that your summary covers the points argued in the paper.

Debatable: A thesis statement puts forward a specific argument––it is not merely a statement but a debatable point that can be analyzed and discussed.

Here are three thesis statement examples from different disciplines:

Psychology thesis example: "We're studying adults aged 25-40 to see if taking short breaks for mindfulness can help with stress. Our goal is to find practical ways to manage anxiety better."

Environmental science thesis example: "This research paper looks into how having more city parks might make the air cleaner and keep people healthier. I want to find out if more green spaces means breathing fewer carcinogens in big cities."

UX research thesis example: "This study focuses on improving mobile banking for older adults using ethnographic research, eye-tracking analysis, and interactive prototyping. We investigate the usefulness of eye-tracking analysis with older individuals, aiming to spark debate and offer fresh perspectives on UX design and digital inclusivity for the aging population."

6. Conduct in-depth research

A research paper doesn’t just include research that you’ve uncovered from other papers and studies but your fresh insights, too. You will seek to become an expert on your topic––understanding the nuances in the current leading theories. You will analyze existing research and add your thinking and discoveries.  It's crucial to conduct well-designed research that is rigorous, robust, and based on reliable sources. Suppose a research paper lacks evidence or is biased. In that case, it won't benefit the academic community or the general public. Therefore, examining the topic thoroughly and furthering its understanding through high-quality research is essential. That usually means conducting new research. Depending on the area under investigation, you may conduct surveys, interviews, diary studies , or observational research to uncover new insights or bolster current claims.

7. Determine supporting evidence

Not every piece of research you’ve discovered will be relevant to your research paper. It’s important to categorize the most meaningful evidence to include alongside your discoveries. It's important to include evidence that doesn't support your claims to avoid exclusion bias and ensure a fair research paper.

8. Write a research paper outline

Before diving in and writing the whole paper, start with an outline. It will help you to see if more research is needed, and it will provide a framework by which to write a more compelling paper. Your supervisor may even request an outline to approve before beginning to write the first draft of the full paper. An outline will include your topic, thesis statement, key headings, short summaries of the research, and your arguments.

9. Write your first draft

Once you feel confident about your outline and sources, it’s time to write your first draft. While penning a long piece of content can be intimidating, if you’ve laid the groundwork, you will have a structure to help you move steadily through each section. To keep up motivation and inspiration, it’s often best to keep the pace quick. Stopping for long periods can interrupt your flow and make jumping back in harder than writing when things are fresh in your mind.

10. Cite your sources correctly

It's always a good practice to give credit where it's due, and the same goes for citing any works that have influenced your paper. Building your arguments on credible references adds value and authenticity to your research. In the formatting guidelines section, you’ll find an overview of different citation styles (MLA, CMOS, or APA), which will help you meet any publishing or academic requirements and strengthen your paper's credibility. It is essential to follow the guidelines provided by your school or the publication you are submitting to ensure the accuracy and relevance of your citations.

11. Ensure your work is original

It is crucial to ensure the originality of your paper, as plagiarism can lead to serious consequences. To avoid plagiarism, you should use proper paraphrasing and quoting techniques. Paraphrasing is rewriting a text in your own words while maintaining the original meaning. Quoting involves directly citing the source. Giving credit to the original author or source is essential whenever you borrow their ideas or words. You can also use plagiarism detection tools such as Scribbr or Grammarly to check the originality of your paper. These tools compare your draft writing to a vast database of online sources. If you find any accidental plagiarism, you should correct it immediately by rephrasing or citing the source.

12. Revise, edit, and proofread

One of the essential qualities of excellent writers is their ability to understand the importance of editing and proofreading. Even though it's tempting to call it a day once you've finished your writing, editing your work can significantly improve its quality. It's natural to overlook the weaker areas when you've just finished writing a paper. Therefore, it's best to take a break of a day or two, or even up to a week, to refresh your mind. This way, you can return to your work with a new perspective. After some breathing room, you can spot any inconsistencies, spelling and grammar errors, typos, or missing citations and correct them. 

• The best research paper format 

The format of your research paper should align with the requirements set forth by your college, school, or target publication. 

There is no one “best” format, per se. Depending on the stated requirements, you may need to include the following elements:

Title page: The title page of a research paper typically includes the title, author's name, and institutional affiliation and may include additional information such as a course name or instructor's name. 

Table of contents: Include a table of contents to make it easy for readers to find specific sections of your paper.

Abstract: The abstract is a summary of the purpose of the paper.

Methods : In this section, describe the research methods used. This may include collecting data , conducting interviews, or doing field research .

Results: Summarize the conclusions you drew from your research in this section.

Discussion: In this section, discuss the implications of your research . Be sure to mention any significant limitations to your approach and suggest areas for further research.

Tables, charts, and illustrations: Use tables, charts, and illustrations to help convey your research findings and make them easier to understand.

Works cited or reference page: Include a works cited or reference page to give credit to the sources that you used to conduct your research.

Bibliography: Provide a list of all the sources you consulted while conducting your research.

Dedication and acknowledgments : Optionally, you may include a dedication and acknowledgments section to thank individuals who helped you with your research.

• General style and formatting guidelines

Formatting your research paper means you can submit it to your college, journal, or other publications in compliance with their criteria.

Research papers tend to follow the American Psychological Association (APA), Modern Language Association (MLA), or Chicago Manual of Style (CMOS) guidelines.

Here’s how each style guide is typically used:

Chicago Manual of Style (CMOS):

CMOS is a versatile style guide used for various types of writing. It's known for its flexibility and use in the humanities. CMOS provides guidelines for citations, formatting, and overall writing style. It allows for both footnotes and in-text citations, giving writers options based on their preferences or publication requirements.

American Psychological Association (APA):

APA is common in the social sciences. It’s hailed for its clarity and emphasis on precision. It has specific rules for citing sources, creating references, and formatting papers. APA style uses in-text citations with an accompanying reference list. It's designed to convey information efficiently and is widely used in academic and scientific writing.

Modern Language Association (MLA):

MLA is widely used in the humanities, especially literature and language studies. It emphasizes the author-page format for in-text citations and provides guidelines for creating a "Works Cited" page. MLA is known for its focus on the author's name and the literary works cited. It’s frequently used in disciplines that prioritize literary analysis and critical thinking.

To confirm you're using the latest style guide, check the official website or publisher's site for updates, consult academic resources, and verify the guide's publication date. Online platforms and educational resources may also provide summaries and alerts about any revisions or additions to the style guide.

Citing sources

When working on your research paper, it's important to cite the sources you used properly. Your citation style will guide you through this process. Generally, there are three parts to citing sources in your research paper: 

First, provide a brief citation in the body of your essay. This is also known as a parenthetical or in-text citation. 

Second, include a full citation in the Reference list at the end of your paper. Different types of citations include in-text citations, footnotes, and reference lists. 

In-text citations include the author's surname and the date of the citation. 

Footnotes appear at the bottom of each page of your research paper. They may also be summarized within a reference list at the end of the paper. 

A reference list includes all of the research used within the paper at the end of the document. It should include the author, date, paper title, and publisher listed in the order that aligns with your citation style.

10 research paper writing tips:

Following some best practices is essential to writing a research paper that contributes to your field of study and creates a positive impact.

These tactics will help you structure your argument effectively and ensure your work benefits others:

Clear and precise language:  Ensure your language is unambiguous. Use academic language appropriately, but keep it simple. Also, provide clear takeaways for your audience.

Effective idea separation:  Organize the vast amount of information and sources in your paper with paragraphs and titles. Create easily digestible sections for your readers to navigate through.

Compelling intro:  Craft an engaging introduction that captures your reader's interest. Hook your audience and motivate them to continue reading.

Thorough revision and editing:  Take the time to review and edit your paper comprehensively. Use tools like Grammarly to detect and correct small, overlooked errors.

Thesis precision:  Develop a clear and concise thesis statement that guides your paper. Ensure that your thesis aligns with your research's overall purpose and contribution.

Logical flow of ideas:  Maintain a logical progression throughout the paper. Use transitions effectively to connect different sections and maintain coherence.

Critical evaluation of sources:  Evaluate and critically assess the relevance and reliability of your sources. Ensure that your research is based on credible and up-to-date information.

Thematic consistency:  Maintain a consistent theme throughout the paper. Ensure that all sections contribute cohesively to the overall argument.

Relevant supporting evidence:  Provide concise and relevant evidence to support your arguments. Avoid unnecessary details that may distract from the main points.

Embrace counterarguments:  Acknowledge and address opposing views to strengthen your position. Show that you have considered alternative arguments in your field.

7 research tips 

If you want your paper to not only be well-written but also contribute to the progress of human knowledge, consider these tips to take your paper to the next level:

Selecting the appropriate topic: The topic you select should align with your area of expertise, comply with the requirements of your project, and have sufficient resources for a comprehensive investigation.

Use academic databases: Academic databases such as PubMed, Google Scholar, and JSTOR offer a wealth of research papers that can help you discover everything you need to know about your chosen topic.

Critically evaluate sources: It is important not to accept research findings at face value. Instead, it is crucial to critically analyze the information to avoid jumping to conclusions or overlooking important details. A well-written research paper requires a critical analysis with thorough reasoning to support claims.

Diversify your sources: Expand your research horizons by exploring a variety of sources beyond the standard databases. Utilize books, conference proceedings, and interviews to gather diverse perspectives and enrich your understanding of the topic.

Take detailed notes: Detailed note-taking is crucial during research and can help you form the outline and body of your paper.

Stay up on trends: Keep abreast of the latest developments in your field by regularly checking for recent publications. Subscribe to newsletters, follow relevant journals, and attend conferences to stay informed about emerging trends and advancements. 

Engage in peer review: Seek feedback from peers or mentors to ensure the rigor and validity of your research . Peer review helps identify potential weaknesses in your methodology and strengthens the overall credibility of your findings.

• The real-world impact of research papers

Writing a research paper is more than an academic or business exercise. The experience provides an opportunity to explore a subject in-depth, broaden one's understanding, and arrive at meaningful conclusions. With careful planning, dedication, and hard work, writing a research paper can be a fulfilling and enriching experience contributing to advancing knowledge.

How do I publish my research paper? 

Many academics wish to publish their research papers. While challenging, your paper might get traction if it covers new and well-written information. To publish your research paper, find a target publication, thoroughly read their guidelines, format your paper accordingly, and send it to them per their instructions. You may need to include a cover letter, too. After submission, your paper may be peer-reviewed by experts to assess its legitimacy, quality, originality, and methodology. Following review, you will be informed by the publication whether they have accepted or rejected your paper. 

What is a good opening sentence for a research paper? 

Beginning your research paper with a compelling introduction can ensure readers are interested in going further. A relevant quote, a compelling statistic, or a bold argument can start the paper and hook your reader. Remember, though, that the most important aspect of a research paper is the quality of the information––not necessarily your ability to storytell, so ensure anything you write aligns with your goals.

Research paper vs. a research proposal—what’s the difference?

While some may confuse research papers and proposals, they are different documents. 

A research proposal comes before a research paper. It is a detailed document that outlines an intended area of exploration. It includes the research topic, methodology, timeline, sources, and potential conclusions. Research proposals are often required when seeking approval to conduct research. 

A research paper is a summary of research findings. A research paper follows a structured format to present those findings and construct an argument or conclusion.

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• Starting the research process

A Beginner's Guide to Starting the Research Process

When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.

The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a  problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.

This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.

Table of contents

Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.

First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .

Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.

Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.

>>Read more about narrowing down a research topic

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So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .

The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.

Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.

To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.

>>Read more about defining a research problem

Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.

A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.

In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .

>>See research question examples

The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.

There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?

You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.

>>Read more about creating a research design

Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.

As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.

You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.

>>Read more about writing a research proposal

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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Artificial Intelligence: How is It Changing Medical Sciences and Its Future?

Kanadpriya basu.

From the Covisus Inc, Monrovia, CA, USA

Ritwik Sinha

1 Adobe Research, San Jose, CA, USA

2 Whistle Labs, San Francisco, CA, USA

Treena Basu

3 Department of Mathematics, Occidental College, Los Angeles, CA, USA

Associated Data

The first step towards building an artificially intelligent system (after problem selection and development of solutions strategy) is data collection. The creation of well performing models relies on the availability of large quantities of high quality data. The issue of data collection is shrouded in controversy due to patient privacy and due to recent incidents of data breaches by major corporations. Advances in technology have resulted in increased computational and analytic power as well as the ability to store vast amounts of data. Technology such as facial recognition and gene analysis provides a path for an individual to be identified from a pool of people. Patients and the public in general have a right to privacy and the right to choose what data, if any, they would like to share. Data breaches now make it possible for patient data to fall into the hands of the insurance companies resulting in a denial of medical insurance because a patient is deemed more expensive by the insurance provider due to their genetic composition. Patient privacy leads to restricted availability of data, which leads to limited model training and therefore the full potential of a model is not explored.

Artificially intelligent computer systems are used extensively in medical sciences. Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. While computer systems often execute tasks more efficiently than humans, more recently, state-of-the-art computer algorithms have achieved accuracies which are at par with human experts in the field of medical sciences. Some speculate that it is only a matter of time before humans are completely replaced in certain roles within the medical sciences. The motivation of this article is to discuss the ways in which artificial intelligence is changing the landscape of medical science and to separate hype from reality.

Introduction

Artificial intelligence (AI) in varying forms and degrees has been used to develop and advance a wide spectrum of fields, such as banking and financial markets, education, supply chains, manufacturing, retail and e-commerce, and healthcare. Within the technology industry, AI has been an important enabler for many new business innovations. These include web search (e.g., Google), content recommendations (e.g., Netflix), product recommendations (e.g., Amazon), targeted advertising (e.g., Facebook), and autonomous vehicles (e.g., Tesla).

Humans reap the benefits of artificially intelligent systems every day. Starting from the spam free emails that we receive in our inboxes, to smart watches that use inputs from accelerometer sensors to distinguish between mundane activities and aerobic activity, to buying products on online shopping sites, like Amazon that recommend products based on our previous purchase records. These examples represent the use of AI in a variety of fields, such as technology and retail. AI has transformed our everyday lives, with an effect on the way we perceive and process information.

This article aims to present various aspects of AI as it pertains to the medical sciences. The article will focus on past and present day applications in the medical sciences and showcase companies that currently use artificially intelligent systems in the healthcare industry. Furthermore, this article will conclude by highlighting the critical importance of interdisciplinary collaboration resulting in the creation of ethical, unbiased artificially intelligent systems.

What is AI?

AI is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Some applications of AI include automated interfaces for visual perception, speech recognition, decision-making, and translation between languages. AI is an interdisciplinary science.[ 1 ]

It is widely accepted that the term AI was first coined in 1956 when American computer scientist John McCarthy et al . organized the Dartmouth Conference.[ 2 ] Prior to that, work in the field of AI included the Turing test proposed by Alan Turing[ 3 ] as a measure of machine intelligence and a chess-playing program written by Dietrich Prinz.[ 4 ]

Artificially intelligent systems in healthcare have the following typical pattern. Such a system starts with a large amount of data, on these data machine-learning algorithms are employed to gain information, this information is then used to generate a useful output to solve a well-defined problem in the medical system. Figure 1 captures the typical workflow of an AI solution. Applications of AI in the field of medical sciences include matching patient symptoms to appropriate physician,[ 5 ] patient diagnosis,[ 6 ] patient prognosis,[ 7 ] drug discovery,[ 8 , 9 ] bot assistant that can translate languages,[ 10 ] transcribe notes, and organize images and files.[ 11 ]

Illustration outlining the development of an artificially intelligent model

History of AI in Medical Field

Great advances have been made in using artificially intelligent systems in case of patient diagnosis. For example, in the field of visually oriented specialties, such as dermatology,[ 12 , 13 ] clinical imaging data has been used by Esteva et al .[ 6 ] and Hekler et al .[ 14 ] to develop classification models to aid physicians in the diagnosis of skin cancer, skin lesions, and psoriasis. In particular, Esteva et al .,[ 6 ] trained a deep convolutional neural network (DCNN) model using 129,450 images to classify images into one of two categories (also known as binary classification problem in machine learning) as either keratinocyte carcinoma or seborrheic keratosis; and malignant melanoma or benign nevus. They further established that the DCNN achieved performance at par to that of 21 board-certified dermatologists. Their research demonstrated that AI systems were capable of classifying skin cancers with a level of competence comparable to dermatologists and required only a fraction of the time to train the model in comparison to physicians who spend years in medical school and also relied on experience they developed through patient diagnosis over decades.

Much work has also been done in the realm of AI and patient prognosis. For instance, researchers at Google[ 7 ] developed and trained a DCNN using 128,175 retinal fundus images to classify images as diabetic retinopathy and macular edema for adults with diabetes. There are several advantages of the existence of such an artificially intelligent model, such as:

• Automated grading of diabetic retinopathy leading to increased efficiency in diagnosing many patients in shorter time;
• Serving as a second opinion opthalmologists;
• Detection of diabetic retinopathy in early stages due to capability of the model to study images at the granular level-something impossible for a human opthalmologist to do;
• Vast coverage of screening programs reducing barriers to access.

Huge strides have been made in application of AI systems to drug discovery[ 15 ] and providing personalized treatment options.[ 16 ] Companies, such as Verge Genomics, focus on the application of machine-learning algorithms to analyze human genomic data and identify drugs to combat neurological diseases, such as Parkinson's, Alzheimer's, and amyotrophic lateral sclerosis (ALS) in a cost-effective way.

Artificially intelligent systems are also being applied in the healthcare sector to enhance patient experience, patient care, and provide support to physicians through the use of AI assistants. Companies, such as BotMD have built systems that can help 24 h with clinical related issues regarding:

• Instantly finding which physicians are on call and scheduling the next available appointment; the AI system can also search multiple scheduling systems across different hospitals
• Answering prescription related questions, like drug availability and cost-effective alternative drugs
• Assisting doctors search hospital protocol, list of available clinical tools, and available drugs all through the use of a mobile application, thus improving workflow in the hospital.

Companies Using AI in Medical Sciences

Table 1 below lists just a few of hundreds of companies in the field of technology, healthcare, and pharmacies that conduct research on artificially intelligent systems and their applications in the healthcare industry. Additionally, applications of artificially intelligent systems in healthcare can be broadly classified into three categories[ 17 ] (for the companies in Table 1 , the type of AI system is also noted):

Some major companies around the world using artificial intelligence in medical sciences

• Patient-oriented AI
• Clinician-oriented AI and
• Administrative and Operational-oriented AI.

Present Day Use of AI

The most recent application of AI in global healthcare is the prediction of emerging hotspots using contact tracing, and flight traveler data to fight off the novel coronavirus (COVID-19) pandemic.

Contact tracing is a disease control measure used by government authorities to limit spread of a disease. Contact tracing works by contacting and informing individuals that have been exposed to a person who has contracted the disease and instructing them to quarantine to prevent further spread of the disease. As reported by Apple Newsroom,[ 18 ] tech giants like Google and Apple have joined forces to create a contact tracing platform that will use artificial intelligent systems through the use of application programming interfaces commonly referred to as API's on smartphones. The platform will enable users who choose to enroll to report their lab results. Location services will then allow the platform to contact people who may have been in the vicinity of the infected person.

Canadian company BlueDot creates outbreak risk software that mitigates exposure to infectious diseases.[ 19 ] BlueDot published the first scientific paper[ 20 ] on COVID-19 that accurately predicted the global spread of the virus. The company uses techniques such as natural language processing (NLP), machine learning (ML), along with automated infectious disease surveillance by analyzing approximately 100,000 articles from over 65 countries every day, travel itinerary information and flight paths, an area's climate, temperature and even local livestock to help predict future outbreaks.

Myth Versus Reality in AI

There is a lot of hope that AI will be able to advance the healthcare sector in a variety of ways, not just for patient diagnosis, patient prognosis, drug discovery, but also to serve as an assistant for physician and provide a better and more personalized experience for patients. This hope has been fueled by some successful applications of AI in healthcare. Side-by-side however, there are unrealistic expectations of what AI can do and what the landscape of the healthcare industry will look like in the future.

Dr. Anthony Chang was one of 2019's invited speakers for the Society for Artificial Intelligence in Medicine (AIME) conference held in Poznan, Poland, where he presented a lecture entitled: Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective. Below we list two of the more common myths regarding the application of artificially intelligent systems in healthcare.

While nobody can entirely predict the future, the fact is that physicians who understand the role of AI in healthcare will likely have an advantage in their career. For instance, the American College of Radiology (ACR) posted a job advertisement for a Radiologist:

https://jobs.acr.org/job/radiologist-for-teleradiology- ai-practice/50217408/

listing two requirements for the job:

• Must be American Board of Radiology Certified
• Must be enthusiastic, well-trained radiologist excited about a future where radiologists are supported by world-class AI and machine learning.

The use of AI in any field of study consists of many components and programming is just one of them. For the continued growth, development and success of AI applications in healthcare, physicians and data scientists need to continue collaboration to build meaningful AI systems. Physicians need to understand what AI is capable of achieving and need to evaluate how their role can be improved with AI. Physicians need to communicate this information to data scientists who can then build an AI system. The collaboration does not end here. Together physicians and data scientists must figure out what kind of data they have available to use for model training and, further, once the model is built its performance must be analyzed and interpreted, both of which require collaboration between physicians and data scientists. A further trend is the significant commoditization of AI software. For instance, today it is possible to use a visual tool (requiring no coding) to build a visual classifier. An example of such a tool is Teachable Machine by Google.

Limitations and Challenges in the Application of Artificially Intelligent Systems in Medical Science

The application of artificially intelligent systems in any field including healthcare comes with its share of limitations and challenges. The time has come to change our mindset from being reactive to being proactive with regard to downfalls of new technology. Here we discuss those challenges focusing more on those that pertain particularly to healthcare.

Availability of data

Creating biased models.

Biased data

Artificially intelligent systems are then trained with a portion of the data that was collected (also known as training data set) with the remaining data reserved for testing (also known as testing data set). Thus, if the data collected is biased, that is, it targets a particular race, a particular gender, a specific age group then the resulting model will be biased. Thus the data collected must be a true representation of the population for which its use is intended.

Data preprocessing

Even after unbiased data has been collected, it is still possible to create a biased model. The collected data must be preprocessed before it can be used to train an algorithm. The raw data that has been collected often contains errors due to manual entry of data or a variety of other reasons. These entries are sometimes modified through mathematical justification or are simply removed. Care should be taken that data preprocessing does not result in a biased pool of data.

Model selection

With the existence of several algorithms and models to choose from, one must select the algorithm that is best suited for the task at hand. Thus, the process of model selection is extremely important. Bias models are ones that are overly simple and fail to capture the trends present in the dataset.

Presenting biased models

It is important for a user of an artificially intelligent system to have a basic understanding of how such models are built. This way a user can better interpret the output of the model and decide how to make use of the output. For instance, there are many metrics that one could use to evaluate the performance of a model, such as accuracy, precision, recall, F 1 score , and AUC score .[ 21 ] However, not every metric is appropriate for every problem. When the user of an artificially intelligent system is presented with performance metrics of a model, they need to make sure that the metrics appropriate to the problem are being presented and not just the metrics with the highest scores.

Fragmented data

Another limitation of the application of AI is that models that one organization spends time and effort to design and deploy for a specific task (regression, classification, clustering, NLP, etc) cannot be seamlessly transitioned for immediate use to another organization without recalibration. Due to privacy concerns, data sharing is often inaccessible or limited between healthcare organizations resulting in fragmented data limiting the reliability of a model.

Artificial Intelligent systems have a reputation of being blackboxes due to the complexity of the mathematical algorithms involved. There is a need to make models more accessible and interpretable. While there is some recent work in this direction, there is still some progress to be made.[ 23 ]

Conclusion: The Future of AI in Medical Sciences

Despite the above limitations, AI looks well positioned to revolutionize the healthcare industry. AI systems can help free up the time for busy doctors by transcribing notes, entering and organizing patient data into portals (such as EPIC) and diagnosing patients, potentially serving as a means for providing a second opinion for physicians. Artificially intelligent systems can also help patients with follow-up care and availability of prescription drug alternatives. AI also has the capability of remotely diagnosing patients, thus extending medical services to remote areas, beyond the major urban centers of the world. The future of AI in healthcare is bright and promising, and yet much remains to be done.

The application of artificially intelligent systems in healthcare for use by the general public is relatively unexplored. Only recently the FDA (U.S Food and Drug Administration) approved AliveCor's Kardiaband (in 2017) and Apple's smartwatch series 4 (in 2018) to detect atrial fibrillation. The use of a smartwatch is a first step toward empowering people to collect personal health data, and enable rapid interventions from the patient's medical support teams.

There are many negative effects of modern technology on mental health. However, researchers at the University of Southern California (USC) in collaboration with Defense Advanced Research Projects Agency and the U.S. Army found that people suffering from post-traumatic stress and other forms of mental anguish are more open to discussing their concerns with virtual humans than actual humans for fear of judgment. This research[ 23 ] has promising results for the role of virtual assistants resulting in the collection of honest answers from patients that could help doctors diagnose and treat their patients more appropriately and with better information.

Most global pharmaceutical companies have invested their time and money on using AI for drug development of major diseases, such as cancer or cardiovascular disease. However, development of models for diagnosing neglected tropical diseases (malaria and tuberculosis) and rare diseases remains largely unexplored. The FDA now incentivizes companies to develop new treatments for these diseases through priority vouchers.[ 24 ]

Given the impact that AI and machine learning is having on our wider world, it is important for AI to be a part of the curriculum for a range of domain experts. This is particularly true for the medical profession, where the cost of a wrong decision can be fatal. As identified here, there is a lot of nuance in how an AI system is built. Understanding this process and the choices it entails are important for appropriate usage of this automated system. The data used to learn from and the optimization strategy used has a deep impact on the applicability of the AI system to solve a particular problem. An understanding and appreciation of these design decisions is important for medical profession.

AI has the potential to help fix many of healthcare's biggest problems but we are still far from making this a reality. One big problem and barrier from making this a reality is data. We can invent all the promising technologies and machine learning algorithms but without sufficient and well represented data, we cannot realize the full potential of AI in healthcare. The healthcare industry needs to digitize medical records, it needs to come together to agree on the standardization of the data infrastructure, it needs to create an iron-clad system to protect the confidentiality and handle consent of data from patients. Without these radical changes and collaboration in the healthcare industry, it would be challenging to achieve the true promise of AI to help human health.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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Home » Implications in Research – Types, Examples and Writing Guide

Implications in Research – Types, Examples and Writing Guide

Table of Contents

Implications in Research

Implications in research refer to the potential consequences, applications, or outcomes of the findings and conclusions of a research study. These can include both theoretical and practical implications that extend beyond the immediate scope of the study and may impact various stakeholders, such as policymakers, practitioners, researchers , or the general public.

Structure of Implications

The format of implications in research typically follows the structure below:

• Restate the main findings: Begin by restating the main findings of the study in a brief summary .
• Link to the research question/hypothesis : Clearly articulate how the findings are related to the research question /hypothesis.
• Discuss the practical implications: Discuss the practical implications of the findings, including their potential impact on the field or industry.
• Discuss the theoretical implications : Discuss the theoretical implications of the findings, including their potential impact on existing theories or the development of new ones.
• Identify limitations: Identify the limitations of the study and how they may affect the generalizability of the findings.
• Suggest directions for future research: Suggest areas for future research that could build on the current study’s findings and address any limitations.

Types of Implications in Research

Types of Implications in Research are as follows:

Theoretical Implications

These are the implications that a study has for advancing theoretical understanding in a particular field. For example, a study that finds a new relationship between two variables can have implications for the development of theories and models in that field.

Practical Implications

These are the implications that a study has for solving practical problems or improving real-world outcomes. For example, a study that finds a new treatment for a disease can have implications for improving the health of patients.

Methodological Implications

These are the implications that a study has for advancing research methods and techniques. For example, a study that introduces a new method for data analysis can have implications for how future research in that field is conducted.

Ethical Implications

These are the implications that a study has for ethical considerations in research. For example, a study that involves human participants must consider the ethical implications of the research on the participants and take steps to protect their rights and welfare.

Policy Implications

These are the implications that a study has for informing policy decisions. For example, a study that examines the effectiveness of a particular policy can have implications for policymakers who are considering whether to implement or change that policy.

Societal Implications

These are the implications that a study has for society as a whole. For example, a study that examines the impact of a social issue such as poverty or inequality can have implications for how society addresses that issue.

Forms of Implications In Research

Forms of Implications are as follows:

Positive Implications

These refer to the positive outcomes or benefits that may result from a study’s findings. For example, a study that finds a new treatment for a disease can have positive implications for patients, healthcare providers, and the wider society.

Negative Implications

These refer to the negative outcomes or risks that may result from a study’s findings. For example, a study that finds a harmful side effect of a medication can have negative implications for patients, healthcare providers, and the wider society.

Direct Implications

These refer to the immediate consequences of a study’s findings. For example, a study that finds a new method for reducing greenhouse gas emissions can have direct implications for policymakers and businesses.

Indirect Implications

These refer to the broader or long-term consequences of a study’s findings. For example, a study that finds a link between childhood trauma and mental health issues can have indirect implications for social welfare policies, education, and public health.

Importance of Implications in Research

The following are some of the reasons why implications are important in research:

• To inform policy and practice: Research implications can inform policy and practice decisions by providing evidence-based recommendations for actions that can be taken to address the issues identified in the research. This can lead to more effective policies and practices that are grounded in empirical evidence.
• To guide future research: Implications can also guide future research by identifying areas that need further investigation, highlighting gaps in current knowledge, and suggesting new directions for research.
• To increase the impact of research : By communicating the practical and theoretical implications of their research, researchers can increase the impact of their work by demonstrating its relevance and importance to a wider audience.
• To enhance the credibility of research : Implications can help to enhance the credibility of research by demonstrating that the findings have practical and theoretical significance and are not just abstract or academic exercises.
• To foster collaboration and engagement : Implications can also foster collaboration and engagement between researchers, practitioners, policymakers, and other stakeholders by providing a common language and understanding of the practical and theoretical implications of the research.

Example of Implications in Research

Here are some examples of implications in research:

• Medical research: A study on the efficacy of a new drug for a specific disease can have significant implications for medical practitioners, patients, and pharmaceutical companies. If the drug is found to be effective, it can be used to treat patients with the disease, improve their health outcomes, and generate revenue for the pharmaceutical company.
• Educational research: A study on the impact of technology on student learning can have implications for educators and policymakers. If the study finds that technology improves student learning outcomes, educators can incorporate technology into their teaching methods, and policymakers can allocate more resources to technology in schools.
• Social work research: A study on the effectiveness of a new intervention program for individuals with mental health issues can have implications for social workers, mental health professionals, and policymakers. If the program is found to be effective, social workers and mental health professionals can incorporate it into their practice, and policymakers can allocate more resources to the program.
• Environmental research: A study on the impact of climate change on a particular ecosystem can have implications for environmentalists, policymakers, and industries. If the study finds that the ecosystem is at risk, environmentalists can advocate for policy changes to protect the ecosystem, policymakers can allocate resources to mitigate the impact of climate change, and industries can adjust their practices to reduce their carbon footprint.
• Economic research: A study on the impact of minimum wage on employment can have implications for policymakers and businesses. If the study finds that increasing the minimum wage does not lead to job losses, policymakers can implement policies to increase the minimum wage, and businesses can adjust their payroll practices.

How to Write Implications in Research

Writing implications in research involves discussing the potential outcomes or consequences of your findings and the practical applications of your study’s results. Here are some steps to follow when writing implications in research:

• Summarize your key findings: Before discussing the implications of your research, briefly summarize your key findings. This will provide context for your implications and help readers understand how your research relates to your conclusions.
• Identify the implications: Identify the potential implications of your research based on your key findings. Consider how your results might be applied in the real world, what further research might be necessary, and what other areas of study could be impacted by your research.
• Connect implications to research question: Make sure that your implications are directly related to your research question or hypotheses. This will help to ensure that your implications are relevant and meaningful.
• Consider limitations : Acknowledge any limitations or weaknesses of your research, and discuss how these might impact the implications of your research. This will help to provide a more balanced view of your findings.
• Discuss practical applications : Discuss the practical applications of your research and how your findings could be used in real-world situations. This might include recommendations for policy or practice changes, or suggestions for future research.
• Be clear and concise : When writing implications in research, be clear and concise. Use simple language and avoid jargon or technical terms that might be confusing to readers.
• Provide a strong conclusion: Provide a strong conclusion that summarizes your key implications and leaves readers with a clear understanding of the significance of your research.

Purpose of Implications in Research

The purposes of implications in research include:

• Informing practice: The implications of research can provide guidance for practitioners, policymakers, and other stakeholders about how to apply research findings in practical settings.
• Generating new research questions: Implications can also inspire new research questions that build upon the findings of the original study.
• Identifying gaps in knowledge: Implications can help to identify areas where more research is needed to fully understand a phenomenon.
• Promoting scientific literacy: Implications can also help to promote scientific literacy by communicating research findings in accessible and relevant ways.
• Facilitating decision-making : The implications of research can assist decision-makers in making informed decisions based on scientific evidence.
• Contributing to theory development : Implications can also contribute to the development of theories by expanding upon or challenging existing theories.

When to Write Implications in Research

Here are some specific situations of when to write implications in research:

• Research proposal : When writing a research proposal, it is important to include a section on the potential implications of the research. This section should discuss the potential impact of the research on the field and its potential applications.
• Literature review : The literature review is an important section of the research paper where the researcher summarizes existing knowledge on the topic. This is also a good place to discuss the potential implications of the research. The researcher can identify gaps in the literature and suggest areas for further research.
• Conclusion or discussion section : The conclusion or discussion section is where the researcher summarizes the findings of the study and interprets their meaning. This is a good place to discuss the implications of the research and its potential impact on the field.

Advantages of Implications in Research

Implications are an important part of research that can provide a range of advantages. Here are some of the key advantages of implications in research:

• Practical applications: Implications can help researchers to identify practical applications of their research findings, which can be useful for practitioners and policymakers who are interested in applying the research in real-world contexts.
• Improved decision-making: Implications can also help decision-makers to make more informed decisions based on the research findings. By clearly identifying the implications of the research, decision-makers can understand the potential outcomes of their decisions and make better choices.
• Future research directions : Implications can also guide future research directions by highlighting areas that require further investigation or by suggesting new research questions. This can help to build on existing knowledge and fill gaps in the current understanding of a topic.
• Increased relevance: By highlighting the implications of their research, researchers can increase the relevance of their work to real-world problems and challenges. This can help to increase the impact of their research and make it more meaningful to stakeholders.
• Enhanced communication : Implications can also help researchers to communicate their findings more effectively to a wider audience. By highlighting the practical applications and potential benefits of their research, researchers can engage with stakeholders and communicate the value of their work more clearly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Does Income Affect Health? Evidence from a Randomized Controlled Trial of a Guaranteed Income

This paper provides new evidence on the causal relationship between income and health by studying a randomized experiment in which 1,000 low-income adults in the United States received $1,000 per month for three years, with 2,000 control participants receiving $50 over that same period. The cash transfer resulted in large but short-lived improvements in stress and food security, greater use of hospital and emergency department care, and increased medical spending of about $20 per month in the treatment relative to the control group. Our results also suggest that the use of other office-based care—particularly dental care—may have increased as a result of the transfer. However, we find no effect of the transfer across several measures of physical health as captured by multiple well-validated survey measures and biomarkers derived from blood draws. We can rule out even very small improvements in physical health and the effect that would be implied by the cross-sectional correlation between income and health lies well outside our confidence intervals. We also find that the transfer did not improve mental health after the first year and by year 2 we can again reject very small improvements. We also find precise null effects on self-reported access to health care, physical activity, sleep, and several other measures related to preventive care and health behaviors. Our results imply that more targeted interventions may be more effective at reducing health inequality between high- and low-income individuals, at least for the population and time frame that we study.

Many people were instrumental in the success of this project. The program we study and the associated research were supported by generous private funding sources, and we thank the non-profit organizations that implemented the program. We are grateful to Jake Cosgrove, Leo Dai, Joshua Lin, Anthony McCanny, Ethan Sansom, Kevin Didi, Sophia Scaglioni, Oliver Scott Pankratz, Angela Wang-Lin, Jill Adona, Oscar Alonso, Rashad Dixon, Marc-Andrea Fiorina, Ricardo Robles, Jack Bunge, Isaac Ahuvia, and Francisco Brady, all of whom provided excellent research assistance. Alex Nawar, Sam Manning, Elizabeth Proehl, Tess Cotter, Karina Dotson, and Aristia Kinis were invaluable contributors through their work at OpenResearch. Carmelo Barbaro, Janelle Blackwood, Katie Buitrago, Melinda Croes, Crystal Godina, Kelly Hallberg, Kirsten Jacobson, Timi Koyejo, Misuzu Schexnider, and the staff of the Inclusive Economy Lab at the University of Chicago more broadly have provided key support throughout all stages of the project. Kirsten Herrick provided help with the nutrition diary data collection effort of this project. We are grateful for the feedback we received throughout the project from numerous researchers and from our advisory board, as well as useful feedback from seminar and conference participants. This study was approved by Advarra Institutional Review Board (IRB).We received funding for this paper from NIH grant 1R01HD108716-01A1. Any views expressed are those of the authors and not those of the U.S. Census Bureau. The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data used to produce this product. This research was performed at a Federal Statistical Research Data Center under FSRDC Project Number 3011. (CBDRB-FY24-P3011-R11537). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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International development trends in the field of agricultural resources and the environment.

1. Introduction

2. materials and methods, 2.1. data sources, 2.2. research methodology, 2.2.1. statistical methods, 2.2.2. research topic analysis method, 3.1. macro-development trend analysis, 3.1.1. total number of published papers in terms of global and annual changes, 3.1.2. total number of published papers in major countries and annual changes, 3.1.3. analysis of papers published by major research institutions, 3.1.4. cooperation analysis of the important institutions, 3.1.5. distribution of journals, 3.2. analysis of research topics, 3.2.1. analysis of global research hotspots, 3.2.2. comparison of research hotspots between china and the united states, 4. discussion, 4.1. geography and climate, 4.2. policy, 4.3. cultural environment, 4.4. agricultural development level, 5. conclusions and prospects, 5.1. conclusions, 5.2. prospects, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Chuan, L.; Qi, S.; Zhang, H.; Jia, Q.; Wang, A.; Zhao, J. International Development Trends in the Field of Agricultural Resources and the Environment. Sustainability 2024 , 16 , 6516. https://doi.org/10.3390/su16156516

Chuan L, Qi S, Zhang H, Jia Q, Wang A, Zhao J. International Development Trends in the Field of Agricultural Resources and the Environment. Sustainability . 2024; 16(15):6516. https://doi.org/10.3390/su16156516

Chuan, Limin, Shijie Qi, Hui Zhang, Qian Jia, Ailing Wang, and Jingjuan Zhao. 2024. "International Development Trends in the Field of Agricultural Resources and the Environment" Sustainability 16, no. 15: 6516. https://doi.org/10.3390/su16156516

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What is Expected from the ASHG 2024 Annual Meeting: Catering Towards Aspiring Trainees

By Meena Radhakrishnan, MD

I collected questions that my peers and I asked the ASHG staff and committee members. Below are their responses:

• What is ASHG and what is it catered to? The Society’s 8,000 members include researchers, academicians, clinicians, laboratory practice professionals, genetic counselors, nurses, and others who have a special interest in the field of human genetics. Society members contribute to the advancement of science for the benefit of health and society.
• Author content in ASHG’s trainee newsletter, The Nascent Transcript
• Judging ASHG’s annual DNA Day Essay Contest, where member judges review and score essays submitted by high school students.
• Review abstracts for ASHG’s Annual Meeting
• Serve as a moderator at a session during ASHG’s Annual Meeting
• Apply to serve on an ASHG committee in a trainee-designated position
• Is there any coaching to guide prospective trainees interested in applying for the ASHG-NHGRI fellowship programs? Kristin Lewis (Associate Director of Fellowship Programs): In early October, ASHG will host a webinar for anyone interested in learning more about the fellowship programs from the current ASHG-NHGRI fellows. We are also planning on hosting a discussion/session at the Annual Meeting specifically about the fellowships – geared toward prospective applicants. It’s currently scheduled to be held as part of the Career Hub in the Exhibit & Poster Hall.
• What is exciting about the upcoming annual meeting in Denver, Colorado, especially for trainees? Latrice Landry PhD, Genomic Medicine Fellow, Harvard Medical School, Career Development Committee Incoming Chair: The three things I am most looking forward to are 1) The RESEARCH: the exciting cutting edge research talks, building a PROFESSIONAL NETWORK of Geneticists, and the EXHIBITOR HALL – I love the feel of the hall, seeing the cutting edge technology and the freebees.
• What can trainees expect to get out of attending ASHG 2024? Latrice Landry, PhD: Trainees should know what they will get out of the Annual Meeting what they put into it. If you are interested in building your network, then introducing yourself, walking up to people, asking questions and making connections at member’s posters is important. If you are trying to get an idea, work out a method, or find inspiration for your own research download the app and look at the program in advance to plan out your time. Also, be open-minded. At my first conference it was only about science. I didn’t know anyone else and just went to as many oral and poster sessions trying to absorb as much science as possible. As I have grown within the organization, I have formed more connections and grown my network. Some of this has resulted in collaborations, invitations and other professional opportunities.
• What events are targeted to trainees at ASHG 2024? Latrice Landry, PhD: Trainees should look for all of the Career Development Sessions and the Career Development Resources in the Exhibitor’s Hall, the trainee reception, and the career development lunch/workshop. In past year’s resume workshops, career coaches, professional headshots and the amazing staff at the ASHG booth in the exhibitor’s hall have been big hits. Also note the trainee reception is always a great way to meet other trainees.

After asking the staff and committee members these questions, it quenched my thirst and I am very excited to attend the upcoming Annual Meeting at Denver, Colorado from Nov 5 – 9. Hopefully, all early career members and trainees are excited to attend and dive into the ocean of knowledge and opportunities ASHG provides.

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Research paper recommendation system based on multiple features from citation network

• Open access
• Published: 30 July 2024

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• Tayyaba Kanwal 1 &
• Tehmina Amjad   ORCID: orcid.org/0000-0003-1201-498X 1 , 2  

With tremendous growth in the volume of published scholarly work, it becomes quite difficult for researchers to find appropriate documents relevant to their research topic. Many research paper recommendation approaches have been proposed and implemented which include collaborative filtering, content-based, metadata, link-based and multi-level citation network. In this research, a novel Research paper Recommendation system is proposed by integrating Multiple Features (RRMF). RRMF constructs a multi-level citation network and collaboration network of authors for feature integration. The structure and semantic based relationships are identified from the citation network whereas key authors are extracted from collaboration network for the study. For experimentation and analysis, AMiner v12 DBLP-Citation Network is used that covers 4,894,081 academic papers and 45,564,149 citation relationships. The information retrieval metrices including Mean Average Precision, Mean Reciprocal Rank and Normalized Discounted Cumulative Gain are used for evaluating the performance of proposed system. The research results of proposed approach RRMF are compared with baseline Multilevel Simultaneous Citation Network (MSCN) and Google Scholar. Consequently, comparison of RRMF showed 87% better recommendations than the traditional MSCN and Google Scholar.

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Avoid common mistakes on your manuscript.

Introduction

The growth in academic social networks allow researchers to produce scientific work with enhanced collaboration. Researchers and practitioners go through the process of manual filtering to find useful papers that are more specific to their research topic. The process of filtering is very time consuming due to massive number of published papers available in digital repositories. Consequently, the need for developing research paper recommender systems emerges.

Common methods for endorsing research papers include cooperative filtering (CF), content-based (CB) filtering, and citation analysis. Cooperative filtering uses a medium of citations and papers, and it depends on user’s past preferences for making paper suggestions. However, it faces challenges like the cold-start problem and sparsity in the citation-paper matrix (Haruna et al., 2018 ). Content-based filtering, as proposed by Bhagavatula et al. ( 2018 ), addresses these issues by extracting keywords and text from the paper. Still, CB methods often struggle with understanding the semantic context and handling natural language ambiguity (Liu et al., 2015 ).

To handle the cold-start problem in recommender systems, recent advancements have unified deep representation learning into cooperative filtering methods (Al-Hassan et al., 2024 ). This improvement aims to enhance the system’s recommendation competences by learning richer representations of user preferences and item characteristics.

Similarly, research by Nair et al. ( 2021 ) has improved content-based filtering by incorporating semantic context understanding. This approach seeks to provide more accurate and contextually relevant recommendations by better acquisitive the semantic meaning of papers.

Additionally, using social networks has become a strategy to mitigate cold-start problems  (Camacho & Alves-Souza, 2018 ). By using user preferences and interactions within social networks, recommender systems can better adapt recommendations, even with limited user information or rating history.

These developments highlight ongoing efforts to improve the effectiveness and accuracy of research paper recommendation systems, addressing key challenges such as the cold-start problem and the semantic understanding of paper content.

Link-based methods, such as bibliographic coupling (BC) and co-citation (CC), assess the meaning of papers based on their relationships with related papers. Co-citation measures the importance of two papers by identifying common third paper that cites the initial two papers, while bibliographic coupling occurs when two papers cite a common third work. However, these methods often contempt the content of the papers, leading to varying results. Additionally, bibliographic coupling may result in false citations when references are listed but not discussed in the paper's content, leading to inaccurate recommendations. Both BC and CC primarily focus on directly linked papers, overlooking complex relationships within the network.

PageRank (PR) determines a paper’s authority based on its citation count. This often results in lower rankings for recent papers due to their fewer citations, leading to their under-representation in recommendations (Joran Beel & Gipp, 2009 ). A significant influence on received citations of a paper can be identified based on the fact that research is publicly available or not, hence leading towards changes in rankings generated by methods that are based on received citations (Amjad et al., 2022a ).

To address these limitations, recent advancements have focused on enhancing link-based techniques with semantic analysis. For instance, Pornprasit et al. ( 2022 ) proposed integrating semantic understanding into the citation network, aiming to improve the identification of significant papers beyond traditional citation-based metrics.

Moreover, the Multi-level Simultaneous Citation Network (MSCN) (Son & Kim, 2018 ) creates a citation network that includes both directly and indirectly linked papers based on the paper of interest. While MSCN considers the mutual relationships between papers beyond a single-level network, relying solely on citation information may still be insufficient to determine the most significant papers.

The proposed recommendation approach combines citation network of papers and collaboration network of influential authors. The proposed approach does not distinguish between the open access and toll-based access of publications. In this study, a citation network of papers has been created up to six levels and it might be extended to ten-levels. Citation networks consider the semantic relationship between paper of interest and papers which are related to it (Khatoon et al., 2024 ). If a network has more than ten levels it would include a greater number of papers in the network which are mostly not associated to paper of interest (Son & Kim, 2018 ). Centrality measures including closeness centrality, eigenvector centrality, degree centrality and betweenness centrality measures are applied on the citation network. In addition to centralities, vote rank of papers is also calculated to get top ranked papers. The key objective of proposed approach is to integrate multiple features including citations of papers and information of authors in the recommendation system. After getting important papers from citation network, key authors are identified from these papers to determine another quality factor of paper. Finally, top ‘n’ papers which are of high quality and have key authors in it are recommended to user. Thus, the proposed approach overcomes the limitations of traditional recommendation approaches. The primary objective of proposed method is to achieve relevant recommendations by integrating multiple features such as citation network of papers and collaboration network of influential authors.

The research contribution can be summarized as:

Proposed a recommendation approach that combines features from citation networks and collaboration networks of influential authors.

Implement different recommendation strategies for journal papers and conference papers, considering the unique characteristics and user preferences in each context.

Identify key authors from important papers to improve the quality and relevance of recommendations, ensuring users receive high-quality academic materials tailored to their needs and interest.

The rest of the paper is arranged in such a way that literature review is covered in “ Related work ” section, the research methodology is presented in “ Methodology ” section which gives the details about the research methods implemented. Experiments on dataset are discussed in “ Experimentation ” section in which the proposed solution is applied to the AMiner dataset. “ Conclusion and Future Directions ” section finally concludes the paper along with some important future directions.

Related work

In this section, various existing approaches that are used for recommending academic papers are studied and detailed analysis is performed. Each of the existing method has its own rationale for recommending academic papers that matches with the researcher’s paper of interest. In addition, author influence factors are also studied to recommend quality papers to the researchers.

Approaches for research paper recommendation systems

Several recommendation approaches are based on content-based filtering and collaborative filtering. For example, Huang et al. ( 2014 ) introduced the RefSeer recommender system, which provides recommendations based on both local and global topic and citation contexts. The system’s quality is evaluated using precision and recall measures, with the CiteSeer repository for training. Although the evaluation results showed improved recall and precision, there are limitations to this technique. It primarily relies on string matching using the paper's title, keywords, and abstract to compute similarity, which often overlooks the semantics of the paper and results in inaccurate recommendations.

CiteSeerX is a scholarly search engine providing access to scientific literature. Labille et al. ( 2015 ) introduced a recommendation approach that suggest documents based on their similarity to users’ conceptual profiles. They evaluated this approach using 2,190,179 documents and Mean Average Weighted Precision (MAWP). Results from the CiteSeerX database showed that the concept-based method offers accurate recommendations even with limited user profile history.

Context-based collaborative filtering recommender system presented by (Liu et al., 2015 ) uses citation as source of information. In this approach, if two citing papers exist within the same citing paper, then they should be comparable to some level. Then, an association mining technique is applied on each citing paper. Afterwards, these papers are compared to get similarities between them for collaborative filtering and citation scores of similar papers are calculated which are used to predict the citation score of target paper. Finally, citation scores of all papers are obtained and citation score is used to recommend top ‘n’ papers. The approach used by (Liu et al., 2015 ) is evaluated on two real-world dataset and recall, precision and F1 metrics are used for the evaluation of ranking information in the recommendation list.

To ease novice users in seeking significant results according to the topic of interest is motivation of recommender system. Providing rich and right publications to right researchers is the challenging task. Collaborative approach for personalized recommender system is presented by (Haruna et al. 2017 ). Contextual metadata that is publicly available is exploited to infer hidden association that exist between research paper. Regardless of user’s expertise and research filed, the proposed approach provides personalized recommendations. The experiments were conducted on publicly available dataset containing list of 50 researcher’s publication containing various computer science field. The performance evaluation metrics which are used includes recall, precision and F1 measure. The results of collaborative approach outperformed the Context-Based Collaborative Filtering and Co-citation method. The technique used in (Haruna et al. 2017 ) computes the importance of relevant papers based on mean reciprocal rank and mean average precision.

Citation-based approach for recommending scholarly papers is introduced in (Haruna et al., 2018 ). The proposed approach creates a paper citation matrix in which candidate papers represented in the form of rows and citations are represented in the form of column. The target paper is identified based on the query of user. The comparison between co-referenced paper and target paper is checked and to acquire that latent association relation is noticed. For experimental evaluation mean average precision and mean reciprocal rank are measured to recommend significant papers at the top of recommended list. Recall, precision and F1 measure are also calculated to observe the performance of system.

(Son & Kim, 2018 ) extends single level network and proposed a multilevel simultaneous citation network (MSCN) that measures centrality measure to evaluate each papers significance. Centrality measures are examined through four parameters named eigenvector centrality, betweenness centrality, degree centrality and closeness centrality. The technique considers semantic as well as structural relationships to recommend papers. For experimentation, the evaluation measures normalized discounted cumulative gain, mean reciprocal rank are used for assessing the performance of proposed MSCN with Scopus and Google Scholar. Although the technique outperformed the results of Google Scholar and Scopus but still it experiences the limitation of not recommending high quality papers as considering only citation information is not enough. Although the recommendations by MSCN show relevancy with paper of interest but in most of cases many of the recommendations are outdated too. Additional features for recommending high quality papers such as ranking authors or any other measure such as journal information are not incorporated.

Citation indices and social network analysis (SNA) metrics are used by (Bihari & Pandia, 2015 ) to calculate the importance of each author. In social network analysis, a person is considered to be more important who have more links with other persons in the network and a person who have fewer links with other persons is considered to be not important. Citation indices which are used to find important authors include betweenness centrality, eigenvector centrality, closeness centrality, degree centrality, citation count, h-index, i10-index, and g-index. Citation indices are applied on relationship network of authors. Relationship network of authors is based on co-authorship in which authors are associated with those authors who have published journal and conference together. Relationship weight is calculated after the extraction of author and his co-authors. Relationship weight is based on total citation count which is the number of papers published together. Authors are represented by nodes in relationship network of authors and co-authorship relation between authors is represented as edges. After the generation of relationship network of authors, analysis has been performed which shows that centrality measure which is much more appropriate than other centrality measures to figure out key authors in the network is eigenvector centrality. Therefore, an author is considered to be most important author in the network if it gains high eigenvector centrality.

Ferreira ( 2018 ) proposed a recommender system which is based on co-citation analysis and bibliographic coupling. Co-citation is based on connection between two papers which are jointly cited by other papers whereas bibliographic coupling is based on common reference between two papers. The proposed technique combines co-citation with content analysis in case of bibliographic coupling to inspect the effect of in-text citations by using proximity analysis. Hence, three distinct techniques have been used by utilizing the proximity in in-text citations in cases of bibliographic coupling. Techniques used in (Ferreira, 2018 ) includes bibliographic coupling based on DBSCAN (Density-based spatial clustering of applications with noise), centiles-based and section based. DBSCAN algorithm is used to gather the in-text citations. Five different schemes are used to give weights to in-text citations pairs in centile-based approach. Both these techniques use the proximity of in-text citations to consider the final papers for recommendation. Third technique which is section-based uses the structure of paper to gather the in-text citations and allocate weights based on distribution of in-text citations in various sections of research article.

Content based approach for recommendation (Bhagavatula et al., 2018 ) embeds intended users query in vector space and rank the nearest neighbors as candidates. The approach outperformed the results of DBLP and PubMed dataset. F1 measure shows 21% improved results in addition, MRR also showed 22% improvement. OpenCorpus research articles dataset is used. The CB approach returns relevant papers by comparing content in the body of paper. This approach is very costly and time-consuming because most research papers are not freely available and comparing content in the body of paper takes lot of time.

Sparse matrix factorization and low rank approaches have been used in numerous research fields which include image processing and video surveillance. Recommendation method based on sparse matrix factorization and low rank (Dai et al., 2018 ) is suggested in which relations between papers are extracted from citing links. Two paper nodes having same neighbor node is used to define relationship between papers. These relations are collected and merged to create paper affinity matrix. Co-authorship is used to consider relation among authors. The relation among authors is dependent on their research community and their interest profile. If two authors have same research interest, then there exists relation between them. These relations are extracted and merged to create author affinity matrix. Both these matrixes are integrated into LSMF which predicts rating of authors. LSMFPRec make use of content information from paper, structure of network and rating information of author to eliminate cold start problem which occurs mostly in traditional collaborative filtering approaches.

Traditional methods to find important papers include citation count and metrics which are derived from it includes G-index and H-index. (Zhao et al., 2018 ) proposed a methodology in which authors are added into the citation network so the importance of research papers and authors are calculated within the same framework recursively. An algorithm has been proposed for ranking of authors by using heterogeneous author-citation academic network. Their proposed method has been tested on two large networks which includes health domain having 500 citation links and computer science network having 8 million citation links. The limitation of using this approach occurs during transformation of heterogeneous network into author-citation network in which the resulting generated graph is too large to be processed and it is not scalable.

Many traditional techniques depend on priori user profiles and these techniques are not able to recommend new research papers to users. In addition, many approaches do not consider public contextual information and due to restrictions by copyright they are not able to find similarities between research papers. By utilizing context of citation, (Sakib et al. 2020 ) proposed an approach which is used to find hidden links between research papers by using 2-level paper-citation relations. In their proposed approach, two papers are considered to be similar that are co-occurred with the same cited paper and co-occurring same citing research paper. Their proposed approach is useful to recommend new research papers, but it is not significant to determine the quality of research paper or authors.

Maurya et al. ( 2021 ), studied the use of graph neural networks (GNNs). GNNs is inspired by the state-of-the-art PageRank method, and it distributes the computation on multiple sites. Maurya et al. ( 2021 ) introduced new algorithms that use GNNs to iteratively compute PageRank values in a distributed manner, offering improvements over traditional methods by efficiently processing large-scale graph data.

Content based recommendation proposed by Ghumman ( 2023 ), by utilizing the strengths of deep learning techniques. Their proposed method extracts meaningful features from paper metadata, improving the accuracy and relevance of recommendations. Deep learning allows the system to capture semantic information from the content of papers, addressing the limitations of traditional string-matching methods.

Recent advancements, like those by Miriyala and Sanjeev ( 2021 ), focus on enhancing concept-based recommendation systems using knowledge graphs. These systems capture more nuanced relationships between concepts and documents, leading to more accurate and personalized recommendations. Integrating knowledge graphs addresses limitations of traditional approaches, such as simply counting the number of links between papers, which can result in inaccurate recommendations.

Table 1 show a summary of the research paper recommendation approaches discussed in this section.

Techniques for finding influential authors

The authors in (Shi 2020 ) utilize historical data of researchers to find the correlation between papers and authors. They proposed a semantic aware, and task guided ranking model. In this approach, the historical collaborations between paper-author pairs are modeled according to a particular task and paper’s semantic is considered along with author’s latent features and both are used to score author-paper pair. AMiner dataset is used to perform different experiments and it shows that the proposed approach accomplishes better performance. They have used limited features to get ranking of authors and their results suffer from name ambiguity of authors. Resolving name ambiguity in bibliometric networks is an essential task and it can occur when more than one authors have same name or if an author uses more than one name variants (Shoaib et al. 2020 ).

(Zhai et al., 2019 ) proposed a method to handle the authors name disambiguation. It is a challenging problem that exists in many applications. The proposed method is based on semantic fingerprints and fusion feature in which authors are recognized based on the feature of organization and co-author. Fingerprint method has proved to be more efficient in detection of duplicate name. Fingerprints of each paper are generated by using text features. Text features include keyword, abstract, title and venue name. Simhash algorithm has been used to generate semantic fingerprints. The main drawbacks that exist in this approach is the missing and incomplete data of abstract and large amount of noise data will affect the performance of proposed approach.

Ranking of research papers and authors have been determined in (Saputra et al. 2019 ) by using length of a research paper. The proposed methodology (Saputra et al. 2019 ) eliminates the drawback of language modeling which uses title and content from abstract of research paper. They used AMiner citation data and used complete content from dataset. The modified weighted language model (MWLM) has been proposed which is used to combine length of research paper and the number of citations to improve precision of prior research paper. The importance of author in a single research paper is determined by using learning to rank (L2R) method. They used six features from and research papers and authors which includes rank of author, the number of previously published research papers, the number of co-authors in the research paper, the average citation number of research paper, betweenness centrality and the number of attended venues of an author. The drawback of using this approach is that it takes much time to read the entire content of research paper.

For finding rising stars various attributes are considered by (Daud et al., 2020 ). The co-authorship which identifies mutual influence between authors, order of author in the research paper, mutual citations of authors, and ranking of authors by hybridization are considered as vital attributes. The approach of weighting in co-authorship is based on the intuition that junior researcher will influence its senior-less researcher and senior researcher will influence more due to large number of publications which will reduce the fraction of junior researcher co-authored work. In addition, the order of author in research paper is also considered. The first author is considered to be the most important author with the highest contribution in the research paper. Therefore, author having high contribution will get high score and author with low contribution will get low score. The importance of author can be determined by the number of citations of his research papers. But it is unfair in case of new publications because it needs some time for research paper to be cited by other research papers (Amjad et al., 2022b ). Therefore, new authors do not have highly cited research papers. So, the citation count does not get any priority while finding the authors in our proposed approach. We use citation count to remove any ambiguity that occurs between researchers.

Number of citations plays an important role on scientific impact. Authors can easily increase their count of citations by citing their own research papers (self-citations). Friends-Only Citations AnalySer (Focus) (Silva et al., 2020 ) is a method which can identify self-citations and eliminates them in the process of ranking of authors. Focus approach is used in three different categories which includes frequency of authors, distance between authors and citation recency. The main drawback of using focus is that it only works well with page-rank algorithm.

Mutual Influence and Citation Exclusivity Author Rank (MuICE) algorithm proposed (Amjad et al., 2016a ), (Amjad et al., 2016b ) is used for ranking of authors. For calculating mutual influence of authors, the factors like number of research papers, number of citations and the number of times the author appears as first author in research publications are considered. For more effective results, the three factors of mutual influence are incorporated with exclusivity of citations that uses f-index measure. The results conclude that the co-authors greatly influence the work of an author especially if senior researchers appear as collaborator.

Entropy is an information theoretic measure used in (Daud et al., 2019 ) for calculating topic-specificity. The approach attempts to investigates the correlation analysis between topic specificity and citation count of research paper publication venue. It was observed as the research paper publication venues with high citation count have low entropy whereas those with low citation count have high entropy. It is known that a low entropy means that the topic is more specific. This means that the topic specificity and the citation count of the research publication venue are negatively linked.

Ranking algorithms and their applications are widely spreading nowadays. Search engines and recommender systems are used in most of the real-world applications. Ranking algorithms which are based on a network are able to solve problems which includes vital nodes identification from a network perspective. Despite many successful applications of these network-based algorithms, three different challenges have been mentioned in (Mariani & Linyuan 2020 ) which includes factors on which ranking is based, specific problems might reduce their efficiency and consequences of system needs to be examined by the researcher.

Hot topics rising star rank (HTRS-rank) proposed in (Daud et al., 2021 ) is used to identify those researchers who have just started their career as junior researcher or rising star. The proposed HTRS-rank method (TF-IDF) is compared with baseline method WordNet approach. TF-IDF performs better than the baseline WordNet approach as TF-IDF considers the semantics and importance of each term as compared to WordNet which considers the two sentences for finding the semantic similarity.

The study (Amjad & Munir, 2021 ) explored the relationship between top-authors and junior authors or researchers to depict the success rate in terms of impact. The study showed that the junior researchers who collaborates with the top-researchers at start of their career becomes more productive as compared to those who collaborates with influential authority at later stages. In that way, more citations, more longevity, and sociability can be acquired by them. The philosophy researchers are considered in (Amjad & Munir, 2021 ) and Scopus and Ebrary are used as dataset.

Another important key factor to determine the quality of papers is studied. The objective of (Ginieis & Li, 2020 ) is the analysis of Author Affiliation Index (AAI) to find the high-quality journals. AAI can be defined as percentage of journal papers published by authors. Therefore, if a journal has high score of AAI, then authors of those papers publish quality papers, and it can be considered as high-quality journal. But this methodology works only to determine the quality of journals rather than individual research paper.

Finding important authors in co-author network is a very difficult task. The authors (Gopavarapu et al., 2019 ) mentioned the problem of finding future key authors in co-author network. To predict the important authors of future, PubRank method is used which is able to consider the static ranking of journals and mutual influence. PubRank modification is proposed in this methodology and various attributes are considered which includes citations of co-authors, venue of publications, order of appearance in research paper and mutual influence of author based on the contribution of author. The main limitation of this technique is that it works well in predicting future authors but not considering importance of old authors in recently published research papers. Table 2 provides a summary of the methods for determining influential authors discussed in this section.

The literature shows various research paper recommendation approaches. To provide their users with more informative and high-quality research publications, traditional recommendation approaches must overcome several limitations. Metadata and content-based approaches only consider the content of paper while ignoring the semantic context of papers. Collaborative filtering approach use paper citation matrix for recommendation which includes problems like rating imbalance and data sparsity. Multi-level citation network based on the number of papers being cited or directly citing related to paper of interest. The information provided by citation network is not enough to recommend important and useful papers. Therefore, an additional filter of generating collaboration network of authors is also integrated in this research to recommend papers of influential authors to its users. The proposed research work is intended to alleviate the cold start problem of zero-citation count.

Methodology

Methodology includes the systematic study and theoretical analysis of methods and procedures for analyzing a field of study. In this study, we introduce the Research Paper Recommendation System Integrating Multiple Features (RRMF) as a support system providing significant research results to users. “Recommendation” here refers to suggesting research papers based on users’ interests, preferences, and needs, aiding in discovering relevant publications aligning with their research topics, objectives, and expertise.

For experimentation, we use the AMiner dataset (Tang et al., 2008 ). The proposed system generates a multi-level citation network based on the concept of selecting a paper of interest (PI) and extracting its references and citations.

Centrality measures in citation networks, author rankings, relevance to the user's research topic, and other quality indicators determine importance and usefulness in the proposed recommendation system. Important factors such as citation count, journal impact factor, author authority, and relevance to the user's research field are considered. Usefulness refers to how much a paper offers valuable insights or solutions related to the user's research interests. By including features like author rankings, we extend the research and generate a more informative collaborative network, prioritizing influential papers relevant to users' specific needs.

Proposed methodology starts by user selection of paper of interest (PI). When a user selects PI, then extraction stage starts in which six-level citation network of research papers is created based on the references of the PI. Then candidate score is computed for each paper in the network. After computation of candidate score, vote rank and centrality measures are calculated of each paper and papers are ranked according to their average centrality score. Authors are extracted from these above ranked papers. After extraction of authors, collaboration network of each author is generated. Social and academic measures of each author is computed individually to compute ranking score of authors. Consequently, top ranked papers having top ranked authors are recommended. Journal papers and conference papers are separately listed and recommended to user.

Following are the steps performed in proposed methodology:

Create Multi-Level Citation Network up to six-levels.

Exclude irrelevant research papers based on candidate score computation and select more relevant papers having high candidate score.

Calculate centrality measures (betweenness, degree, closeness, eigenvector) and vote rank of each paper and rank them based on their average centrality score.

Generate collaboration network of each author extracted from research papers having high centrality scores.

Social network measures encompassing centrality and academic measures of each author is computed separately to get influential authors.

Consequently, research papers of top ranked authors are recommended to users. Journal papers and conference papers are separately recommended.

Phases of research methodology

The proposed methodology has been divided into different phases. Fig. 1 demonstrates an overview of various phases performed during recommendation process.

Flow chart for proposed research paper recommendation system (RRMF)

Providing most relevant scholarly papers from digital repositories to its end user is one of the challenging tasks. To recommend better research publications the proposed recommender system opts citation network because of its characteristic of using reference list of research paper for network generation. The reference list ensures the provisioning of reliable information. Besides citation network, collaboration network approach is used to generate relationship network for authors.

For proposed recommender system, initially paper of interest is identified and is nominated as a central node. Based on the references cited by paper of interest, multi-level citation network is generated. Hence, the network can be expanded in both directions as forward and backward links by using list of references cited at the end of each paper. Secondly, there needs to filter out candidate papers from generated multi-level citation network. Thirdly, centrality measures and vote rank are applied to determine the ranking of significant candidate papers. Further the proposed approach is extended by generating collaboration network of authors from top ranked research papers. Later, centrality measures and academic measures are calculated for each author and key author analysis is performed. Finally, top relevant research papers having top ranked authors are recommended to its end user. Fig. 2 describes in detail the working of the proposed recommendation system.

Summary of the research paper recommendation system (RRMF): a Generation of six level citation network based on multilevel citation network approach b Calculation of candidate score for each paper referred in citation network to filter candidate list c Compute centrality similarity measures and vote rank for each candidate paper and average ranks of candidate papers are determined d Authors are extracted from recommended candidate papers e Generate collaboration network of influential authors f Compute centrality measures of each author g Determine Rank of authors h Top papers are retrieved i Journal papers and conference papers are recommended separately

Selecting academic papers relevant to research field

Create multi-level citation network

Citation network can be generated via list of references provided at the end of research papers thereby considering cites and cited by relation among the papers. Once the targeted paper of interest is identified (from our dataset), the multi-level citation network is generated. Based on reference list of paper of interest, the citation network generation is started. Fig. 3 demonstrates the six-level citation network where nodes represent the papers and so, links are the citations.

Six-level Citation Network for Research Paper Recommendation System

The papers are considered related by using the “cite” and “cited by” relation. Multi-level citation network up to level six is established in the proposed method. Forward and backward links are used to represent retrieved relevant outcome. The forward link represents all those papers that are citing paper of interest whereas the papers which are cited by the paper of interest represented by backward links. Although creating the citation network up to ten- levels is acceptable but we limit the citation network up to only six levels because if we increase level of citation network, then there are most chances of getting irrelevant research results.

Filter candidate papers from multi-level citation network

In the second phase, we further process multi-level citation network to exclude irrelevant papers and retrieve candidate papers from the generated citation network. Candidate papers are those papers which are relevant with PI. Candidate score for each paper is calculated that quantifies their relevance with targeted PI. Co-citation (CC) and bibliographic coupling (BC) are the two similarity measures to determine the citation relation between papers and are depicted in Fig. 4 . Paper A and paper B are the citing papers in Fig. 4 a and are considered related as they cite papers 1, 2 and 3. Paper A and paper B in Fig. 4 b are both cited by paper 1, 2 and 3 hence, paper A and B are associated.

Example a Bibliographic coupling b Co-citation analysis

For candidate score computation, Co-citation and bibliographic coupling of individual paper are required to be calculated by formulating Eqs. 1 and 2 respectively (Son & Kim, 2018 ). C.C and BC are similarity measures of citation analysis method calculated independently, having an intention to find the appropriate papers that relates to paper of interest.

Bibliographic coupling strength of paper A and paper B in Fig. 4 is calculated to be 3 as both paper A and paper B are citing paper 1, paper 2, and paper 3. Eq. ( 1 ) shows the value of BC to be 1 if both paper A and paper B cites paper j.

Co-citation strength value computed via Eq. ( 2 ) can be calculated as 3. Both paper A and paper B are cited by paper 1, paper 2 and paper 3 respectively. Eq. ( 2 ) shows the value of CC to be 1 if paper j cites both paper A and paper B.

All the cite or cited by papers are considered relevant and candidate score via Eq. ( 3 ) is computed for candidate paper selection.

To measure which paper is closely related to paper of interest, two similarity measures BCstrength and CCstrength are merged to compute candidate score (Cscore) using Eq. ( 3 ).

where P denotes targeted Cscore paper and k represents set of all papers except P. The combination of BC and CC in numerator of Eq. ( 3 ) performs citation analysis and denominator shows network analysis based on distance between paper P and PI. Hence, Cscore is a citation network analysis approach. The higher value in numerator indicates set of papers k which are mostly associated with paper P whereas low value specifies irrelevancy of k and P. \(d(PI,P\) ) denotes distance i.e., the number of links between PI and P. The greater value in the denominator indicates papers are not closely related or domain of papers are different. The low value for d indicates papers are closely related to each other.

Cscore values for each paper in a citation network needs to be calculated and for that purpose, Table 3 is used. Paper P6, P8, P18, and P20 are selected as an instance for Cscore calculation in existing six-level citation network of papers shown in Fig. 3 . The value of co-citation strength for P6 is computed to be four [(PI, P6 ← P10), (P5, P6 ← P10), (P1, P6 ← P10), (PI, P6 ← P11)] and bibliographic coupling strength is four [(P10, P6 → P1), (P7, P6 → P2), (P7, P6 → P2), (P7, P6 → P2)]. Total similarity indicated in numerator of c-score can be calculated as sum of co-citation strength and bibliographic coupling strength. The total similarity value for P6 is 8. The distance in denominator of Cscore value is the number of links that occurs between certain paper P and paper of interest. Two number of links exists between P6 and paper of interest and are represented as distance in the denominator of c-score calculation.

Total similarity calculated for P8 and P20 are same but the Cscore value of P8 is more as compared to P20 and the reason behind that is the distance. The number of links between PI and P8 are two whereas between PI and P20 are three. In that case, P8 is considered more relevant to PI as compared to P20 which is farther i.e., more dissimilar from PI. In another case, although P6 and P18 have same total distance from PI but the Cscore value of P18 is lower because its total similarity is less than P6.

The Cscore for each paper is calculated to find out the relation between paper of interest and all other papers. The papers having low c-score values can be isolated from citation network because a paper having low Cscore is considered to be irrelevant and excluded from the network. Hence, the list of candidate papers is selected. The network size defined in existing research (Son & Kim, 2018 ) for experimentation purpose is between 500 and 800. Therefore, the network size of 500 papers is used in the proposed method of six-level citation network. For creating six to eight level citation, network size of 500-800 papers is quite suitable. Cscore only determines the relevancy of paper with paper of interest so further it cannot be used once the list of candidates is figured out.

Filtering candidate papers from multi-level citation network

Compute centrality measures of each paper

To extract topmost papers from candidate list of selected paper, centrality measures for individual papers are calculated variously based on degree centrality, betweenness centrality, closeness centrality and eigenvector centrality (Bihari & Pandia, 2015 ; Son & Kim, 2018 ). In addition, to centralities vote rank of each paper is also calculated.

The degree centrality (C(Deg)) (Son & Kim, 2018 ) is an instantiation of centrality that assists in finding significant nodes. The nodes having high degree are important. Eq. ( 4 ) calculates the degree centrality.

Nodes or number of papers which are referring to paper are represented by d(P) and the total number of papers in the citation network represented by n. The degree centrality only considers the directly connected papers thereby excluding indirectly connected papers.

To understand the notion of closeness centrality measure (Son & Kim, 2018 ), there needs to check how adjacent a particular node is with respect to entire nodes in a network by computing distance. Closeness centrality \({(C}_{\left(Clo\right)})\) is considered to be reciprocal of total distance and can be formulated by Eq. ( 5 ).

where the total number of papers (nodes) in network is represented by n. d (P, k) can be defined as the distance between all other nodes in network and target paper P i.e., k excluding paper P. The least distance of paper in a network to all other neighboring papers is defined by n-1. According to the concept of closeness centrality, the paper that interacts with maximum number of other nodes is more significant and central.

Another centrality measure termed as Betweenness centrality computes the shortest paths for nodes in a network. Betweenness centrality \({(C}_{\left(Bet\right)})\) is centrality measure based on the shortest path that pass through particular node to get to the other nodes in the network (Son & Kim, 2018 ). The betweenness centrality can be calculated by using Eq. ( 6 ).

where the number of links that pass through shortest route delivers by metric gjv and the number of links that passes through paper P delivers by metric gjv(P).

Eigenvector centrality \({(C}_{\left(Eig\right)})\) is more useful if a node is hub and it measures how many of the important hubs are connected to node (Son & Kim, 2018 ).

\({X}_{J}\) is the eigen score of J and \(\lambda\) is the eigen value and A P,J is the adjacency matrix where,

Another method used for ranking the research papers termed as vote rank is based on citation network. The vote rank is implemented in the proposed recommendation system with an intention to select influential nodes from a citation network. For that purpose, voting scheme is used for ranking each of the node within network. All of the nodes assign voting scores to its neighbor node and hence the node having highest score are elected as influential node. Vote rank can be defined as:

G is generated multi-level citation network whereas num of nodes represents extracted number of nodes that are ranked.

The results of four centrality measures and vote rank calculated above are ranked but it must be noted that all measures have same scale. Formulation of average rank \(AvgR\left(P\right)\) of centrality measures and vote rank returns the list of top ranked research papers.

where \({rank}^{k}(P)\) is the ranking result on paper P and M is the total centrality measure calculated along with vote rank.

Calculate centrality measures of candidate papers

Extract authors from top ranked research papers

The next step in the methodology is to extract authors from the top ranked papers. To extract these authors, the study performs following sub-steps:

Generate collaboration network of each author extracted from list of significant research papers.

Social network measures encompassing centrality measures of each author is computed separately.

Academic measures including citation count and h-index are calculated.

Rank authors based on score computed by centrality measures and academic measures.

Finally, papers of top authors recommended.

Details of each process are explained in the following subsections.

Generation of authors collaboration network

The collaboration between authors and co-authors from a list of retrieved papers is generated and presented in form of a collaboration network. The weighted undirected graph of author and co-author relation is formed on the basis of their publications data. If two authors are listed in the same research publication, then they are collaborated. For instance, consider three candidate research papers: Candidate paper CP1 co-authored by four authors a1, a2, a3, b3 and paper CP2 having three authors b1, b2, b4 and paper CP3 having five authors a1, a2, b3, a4, c1. Authors are linked as {(a1,a2),(a1,a3),(a1,b3),(a2,a3),(a2,b3),(a3,b3)}, {(b1,b2),(b1,b4),(b2,b4)},{(a1,a2),(a1,b3),(a1,a4),(a1,c1),(a2,b3),(a2,a4),(a2,c1),(b3,a4),(b3,c1),(a4,c1)} to represent their co-authorship relation. The network of the author a1 generated is depicted in part (b) of Fig. 5 and respectively for b1 in part (b) of Fig .5 . Each node represents an individual author whereas each edge in a network represents the collaboration relationship between co-authors for particular academic papers written together by some authors. Therefore, the authors that extensively link and collaborates with other authors are considered as significant authors.

a Authors extracted from candidate papers b Collaboration network of author a1 c Collaboration network of author b1

Generate collaboration network of authors

Calculating centrality measures for finding top authors

For computing top ranked authors, several centrality measures and citation indices are adopted in the proposed recommender system. Centrality measures of each author is calculated for determining the central position of each author scattering the info over the network. For centrality analysis, closeness centrality, degree centrality and betweenness centrality of each author is computed with the help of authors collaboration network. To analyze the significance of each author in a specified network eigen vector is also calculated. In addition to centralities, other factors citation count and h-index of author are also calculated.

Degree centrality of an author can be computed by the total number of co-authors directly connected with particular author. Author having more connections or authors having high degree are more central and has greater capacity to influence others. The degree centrality of author can be computed by Eq. ( 10 ).

where, ai indicates an author from list of authors, \({C}_{\left(Deg\right)}\left({a}_{i}\right)\) represents degree centrality of author and d(ai) represents the degree or total number of authors directly connected. Hence, scholar’s co-authorship capacity can be determined through degree centrality.

Closeness centrality computes how close an author is to central position. Author directly connected with other authors has high closeness centrality whereas an author indirectly connected with other authors has low value for closeness centrality. Closeness centrality is computed by Eq. ( 11 ).

The distance between two authors in the author network is represented by \(d({a}_{j}{,a}_{i})\) . So, closeness simply determines the position of scholars in co-author network and its closest distance with other co-authors in the network.

Betweenness centrality measures the shortest distance between two authors and the number of times author used as a connection between the routes. Eq. ( 12 ) computes the betweenness centrality as follows:

\({C}_{\left(Bet\right)}\left({a}_{i}\right)\) is the betweenness centrality of author ai, \({\sigma }_{jk}({a}_{i})\) represents the number of authors that passes through author \({a}_{i}\) whereas \({\sigma }_{jk}\) characterizes the total number of shortest paths from author aj to ak .

Eigenvector centrality is an extended degree centrality measure. The degree centrality of an author considers only the total number of authors that are adjacent to that particular author, whereas the eigenvector considers the total number of adjacent neighboring nodes (authors) that are significant. Just because all the connections in eigenvector are not equal so, it can generally be stated as an author having connection with more influenced authors will lend that author more influence rather than having connection with less influenced authors. In addition to the connections, relative scores are assigned to all the connected nodes in network. Hence, connection as well as score is significant in eigenvector. Connection of particular node with the other nodes possessing high score value assists in assigning more score to that particular node. Eigenvector is based on the concept of adjacency matrix. Let A be the adjacency matrix of graph, A = av,t

\(EV\left({a}_{i}\right)\) is an eigrenvector of author ai, \(\lambda\) is a constant, \({a}_{i,j}\) is an adjacency matrix whereas \({x}_{i}\) is eigenvector score of the author ai.

Calculating academic network measures for authors

There exist various indexing schemes that are used to measure the impact and productivity of authors. For the reason, citation count and h-index in the proposed system are also calculated to provide quality results to its users. As, h-index and citation count in academic network are used to find top ranked authors. Citation count intends to measure the frequency of citations. Basically, it measures the count that how many times referred research article has been cited by other people. For an instance, if there is a publication having four authors with six citation count then all of the four authors are assigned with six citation count. In addition, h-index is an author-level metric that intends to measure both the quantity and quality of research paper. Quantity and quality are two parameters in which number of citations depicts quality of paper whereas number of publications or papers are the quantitative representation. Citation count and publication count both are used to rank the authors. Authors who publish number of highly cited papers are filtered out by using h-index measure. If author publication possesses at least h number of citations for each publication that indicates author has high h-index. Author having highest h-index value is ranked highest whereas an author having lowest h-indexed value is ranked as lowest. For computing h-index initially, there needs to sort an array of citation count of author in ascending order. Then iterate over the array from lowest to highest paper. The count of paper is identified that surely satisfies the h-index condition. For any function f arranged in decreasing order, h-index can be computed by Eq. ( 14 ).

Ranking authors of top ranked papers

Determine rank of authors

As the centrality values are used for impact evaluation so they are used in the proposed system to measure authors impact too along with research articles impact. All the centrality measures calculated above are combinedly used to compute the collaboration score of each author. Authors calculated scores are then converted to rank.

Recommendation of related papers based on author’s ranking

List of authors collaboration score is used to determine the paper-author score. For each paper, its corresponding authors score are summed up to determine rank of paper. Hence, top ranked authors research papers are recommended to users.

Experimentation

There are various methods used in the past to recommend research papers to users. These methods have been divided into two main categories offline and online methods (Beel & Langer 2015 ). Almost 30% of methods have used online methods of evaluation whereas the remaining 70% methods use offline methods. The main drawback of using online method of evaluation is that it is time consuming because it generates new dataset and for getting results evaluator has to wait for many days. Existing datasets which are considered to be standard datasets for research are used by offline methods and these datasets are already used by others. Offline methods are considered to be reliable for evaluation because we use same settings of experiment for various evaluations, and it also increases consistency level of evaluation. Our proposed approach also uses offline method for evaluation.

Dadroit is used to view large json files and dadroit is used to view dataset file. MySQL database is used, and Python language is used to convert the json data to relational database. PyCharm is used for development in Python. Gephi tool is used for generating graphical view of citation network of papers and co-author network between authors. Microsoft Excel is used for generating graphical view of NDCG, MRR and MAP metrics.

AMiner Citation Network Dataset (DBLP + Citation, ACM Citation Network) Footnote 1 has been utilized in proposed approach which is publicly available for researchers. It is generated by associating data from different sources which includes ACM, DBLP, MAG (Microsoft Academic Graph) and other sources. In this dataset, every paper is linked with authors, year, abstract, venue and title. The V12 version (latest version) of DBLP-Citation Network has been generated on Apr 09, 2020 (Tang 2010a ), (Tang et al., 2012 ), (Tang et al. 2010b ), (Tang et al. 2007 ), (Sinha et al., 2015 ). The statistics of dataset has been described in Table 4 . The number of papers published over the year are 4,894,081 and citation relation exist between them is 45,564,149. The graphical network representation of sample papers selected as paper of interest from dataset are illustrated in Figs. 6 and 7 . Figure 6 represents networks generated for PI1, PI2, PI3 and PI4. The Gephi tool is used for citation network generation of papers where nodes represent id of papers and edges are the citations of paper. Similarly, Fig. 7 shows collaboration network generated for authors in which author id are illustrated in nodes and edges are co-authored relations.

Citation Network of Sample Papers PI1, PI2, PI3 and PI4 where nodes represent id of paper and edges are citations between papers

Collaboration Network of Authors where nodes represent id of authors and edges are collaboration relation between authors

Dataset description

The dataset is organized in a JSON file. The attributes included in this version are id, title, authors.name, authors.id, authors.org, venue.id, venue, raw, year, references, fos.w, fos.name, n_citation, page_start, page_end, doc_type, publisher, volume, issue, DOI, indexed-abstract. Id attribute in V12 for paper, venue and author is represented as V12 Long Type instead of string. The attributes of dataset which we are using has been shown in Table  5 .

Table 5 displays the dataset metadata used in the experiments. Although some metadata like Title and author association might seem relevant for a recommender system, they weren’t utilized in this study for specific reasons. The research primarily focused on evaluating the proposed recommendation system's performance based on citation networks and co-author collaboration instead of text-based similarities and ranking. Therefore, certain metadata fields, including Title and author affiliation, were omitted from the recommendation process to maintain focus on the core aspects of the method. By excluding these fields, the study aimed to streamline the recommendation process and underscore the significance of citation relationships and co-authorship patterns in generating relevant recommendations.

Results and discussion

For evaluating the recommendations provided by proposed RRMF several experiments are conducted and results are compared with MSCN and Google Scholar. MSCN is baseline approach based on citation network whereas Google Scholar is proprietary database most widely used by researchers for scholarly literature.

The sample research papers (paper of interest) we have preferred for experiment in proposed research has been shown in Table  6 . The papers from various fields are selected to test different scenarios of proposed research. PI1 is selected as paper of interest and is used to verify the results of recommendation of old paper having few citations count with important authors. Paper of interest PI2 is selected as latest year paper with zero or few citation counts having important authors. PI3 is selected as latest paper with certain citation count. PI4 being current decade paper with zero citation count is considered as an instance, to verify the results of recommendation of paper.

For assessing the validity of proposed scheme, the ranked results from our proposed RRMF recommendation system along with the results of traditional approaches are given to experts for evaluation. The results are evaluated by the researchers of each of the approach that either they are satisfied or not. For evaluating, ten researchers are provided with title of paper, year of publication, citation count and the abstract of the paper of interest.

In this study, each of the four selected papers plays a distinct role in assessing the proposed recommendation system. The performance evaluation is very challenging in scenarios where the ground truth is missing. Therefore, in this study, we have performed a qualitative analysis and picked four different types of papers, and we have explained the reason to pick each of these papers.

Paper 1 (PI1): “Evaluating the Use of Project Glossaries in Automated Trace Retrieval”.

Purpose: This paper, though older, possesses a moderate number of citations. It serves to evaluate the system's capability to suggest older yet potentially relevant papers with fewer citations.

Relevance to Recommendation Systems: Recommender systems often encounter user interest in older or less-cited papers. Assessing the system's performance in recommending such papers is vital for its adaptability to diverse user preferences.

Paper 2 (PI2): “What does a Successful Postdoctoral Fellowship Publication Record Look Like”.

Purpose: Despite being recent, this paper has zero citations, challenging traditional citation-based recommendation methods.

Relevance to Recommendation Systems: Many researchers seek out recent publications, especially those with few or no citations. Evaluating the system's performance in recommending such papers helps determine its ability to spotlight new and potentially impactful research.

Paper 3 (PI3): “A general framework for fast stagewise algorithms”.

Purpose: This recent publication has garnered a certain number of citations, requiring the recommendation system to balance recency and citation count.

Relevance to Recommendation Systems: A paper’s recency and citation count are crucial factors for recommender systems. Assessing the system's performance with such papers aids in understanding its ability to offer well-rounded suggestions.

Paper 4 (PI4): “Scaling properties of mach bands and perceptual models”.

Purpose: Although from the current decade, this paper has zero citations, posing a challenge for traditional citation-based approaches.

Relevance to Recommendation Systems: Recent papers with no citations still hold value and interest for researchers. Evaluating the system’s performance with such papers helps assess its capability to uncover hidden gems in the literature.

In essence, these four papers represent various scenarios encountered in recommendation systems: older papers with moderate citations, recent papers with zero citations, recent papers with moderate citations, and recent papers with no citations. Evaluating the system across these scenarios provides valuable insights into its effectiveness and adaptability in real-world scenarios.

For evaluating the proposed approach, rank-based information retrieval metrics are used. Mean Average Precision (MAP), Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG) are relevance measures adopted in proposed RRMF system.

The concept of MAP is derived from average precision. For mean average precision, relevant recommended papers from set of recommended results are considered. For MAP calculation, firstly precision (pre@k) is calculated, and average of precision’s result is taken to compute average precision ( \(AP@k)\) . Recommendation systems are mostly designed with an intention to recommend top-N results to its end users, therefore calculating precision @k makes more sense. k can be defined as user defined integer hence for recommending top N results to its user the value of k is set by user. In the proposed work, the value of k ranges within 1 to 10. To define precision @k, it is said to be directly proportional to relevant recommendations in top-k sets. Mathematically, precision (pre@k) can be defined by Eq. ( 15 ).

The average precision ( \(AP@k)\) can be calculated using Eq. ( 16 ).

The total number of relevant research papers in the recommendation result list is represented by m and the total number of recommended research papers is represented by n. rel(k) signifies relevance information that either kth item is relevant or not. If rel(k) is related, then denoted by 1 else denoted as rel(k) = 0. Finally, mean of average precision ( \(MAP@k\) ) results is computed by calculating the average of overall \(AP@k\) . This can be done by using Eq. ( 17 ).

N is the total number of recommended research papers.

Mean Reciprocal Rank (MRR) is another information retrieval measure used for evaluation. The rank position at which the first relevant document is identified, and its reciprocal is calculated to compute reciprocal rank (RR). For an instance, if relevant document is retrieved at rank 1 then RR for that document is 1, if relevant document found at rank 2 instead of rank 1 then its RR is ½ = 0.5 and so on respectively. The average across queries measures the MRR.

The formula used for calculating MRR is defined in Eq. ( 18 )

The total number of search queries performed by users is represented by Q and rank i is rank position of first relevant document in i th query. For an instance consider a scenario in which there are three documents Q1, Q2 and Q3 to be searched. The search queries retrieved (0,1,0), (1,0,0), (0,0,1). The rank of relevant result for Q1 is 2 and its RR is 1/2, Q2 is 1 with RR 1/1, Q3 is 3 with RR 1/3. The mRR can be 1/3[(1/2 + 1/1 + 1/3)] = 0.59.

Another measure used for evaluation is Normalized Discounted Cumulative Gain (NDCG). It is based on two suppositions in which the first is the documents that are highly relevant are more useful than the documents that are marginally relevant and the second is the higher the ranked position of a relevant document, the more useful it is for user whereas the relevant document with low ranked position is less useful for user as it is less likely examined. DCG is derived from cumulative gain (CG) which can be defined as summation of graded relevance values of all results in a search result list. At particular rank position p, CG can be computed by using Eq. ( 19 ).

rel i is graded relevance value of result at particular position i.

DCG emphasizes on retrieving relevant documents can be computed at particular rank position p by using Eq. ( 20 ).

The normalized discounted cumulative gain (NDCG) is computed by using Eq. ( 21 )

where IDCG is ideal discounted cumulative gain and can be calculated by arranging all appropriate research articles in the corpus by their relative relevance hence producing maximum possible DCG through position p using Eq. ( 22 ).

rel p represents ordered relevant research papers up to position.

The results generated by evaluation metrics NDCG, MAP, and MRR for the proposed RRMF, baseline MSCN and traditional Google Scholar are illustrated in Figs.  8 , 9 and 10 respectively. The horizontal x-axis of the graphs represents the recommended number of top ranked papers and vertical y-axis represents the corresponding NDCG, MAP, MRR values calculated for the recommended papers.

Comparison of MSCN, Google Scholar and proposed RRMF using NDCG metric

Comparison of MSCN, Google Scholar and proposed RRMF using MRR metric

Comparison of MSCN, Google Scholar and proposed RRMF using MAP metric

For PI1, PI2 PI3 and PI4 selected as paper of interest, RRMF outperforms the Google Scholar and MSCN in all of the cases, except in the case of Paper 3. Overall, the proposed RRMF performs better than MSCN and Google Scholar. As the papers with higher ranks correctly recommended to users are given more weight by NDCG and MRR. The assessment criteria defined to users for evaluating the recommended results range from 0 to 3.0 is for totally irrelevant or zero recommendation result, 1 is for partially relevant recommended papers, 2 is for relevant recommendations and 3 is marked as most relevant recommended result. Figure  10 depicts the comparative results of three recommendation approaches for four papers which are used for experimentation in terms of average number of papers which are correctly recommended to researchers.

The evaluation results provided by researchers clearly declared that the RRMF recommendations are more accurate as compared to Google Scholar and MSCN. As the recommendation approach possessed by Google Scholar is entirely based on citation count whereas MSCN only generates citation network of papers and recommend paper to its users. RRMF generates the multi-level citation network of paper of interest based on its references. In addition, it also incorporates an additional feature of integrating authors ranking. Thereby, using citation network and co-author collaboration the recommended papers by RRMF are mostly related to PI.

For instance, paper 1 (PI1) is selected as an old paper having few citations count, the results revealed the better performance of RRMF. The Google Scholar recommends most of the outdated papers, only two of the recommendations are from recent decade. Google Scholar is unable to recommend papers of recent decade in case of paper 1. As, MSCN is based on citation network approach, so it retrieves relevant papers, but maximum number of recommendations are outdated. RRMF recommends most of the papers from recent decade as it is based on vote ranking approach as well authors ranking is also considered in it. As a result, the graph of paper 1 plotted for NDCG, MRR and MAP clearly showed improved search results of RRMF as compared to Google Scholar and MSCN and are plotted in Figs.  8 , 9 , and 10 .

For paper 2 (PI2), the performance of Google Scholar is declared as worst by the users. As PI2 is the case in which most recent paper of interest is selected as an instance with zero citation count. The reason behind the worst performance result of Google Scholar is based on page rank which ranks the paper based on citation count of paper and if the citation of paper is zero, then in most of the cases Google Scholar is unable to recommend papers. PI2 also has zero citations so there are no research results recommended by Google Scholar. MSCN is based on citation network only, so it is able to give paper recommendations, but the quality of recommended papers is very low and mostly papers are irrelevant and outdated. Therefore, in that case RRMF performs much better by incorporating the authors network too and hence the improved results are recommended. The evaluation results of NDCG, MRR and MAP are shown in Figs.  8 , 9 , and 10 respectively.

When the paper of interest is selected from recent five years with certain citation count such as Paper 3 (PI3), the performance of RRMF is again stable and showed improved search results. The results of evaluation metrics NDCG, MRR and MAP clearly depicts the performance results of the proposed RRMF as compared to Google Scholar and MSCN and plotted results are shown in their respective Figs.  8 , 9 , and 10 . Because of high citation count, Google Scholars performance is better here, as it recommends most of the papers from recent decades. But none of the papers recommended by MSCN are from recent decade, whereas proposed RRMF based on citations network recommends most of the papers from recent decade and also considers the influential authors which results in increased quality of recommended.

In addition, when current decade paper with zero citations is selected as the paper of interest PI4, RRMF recommends better research results as compared to Google Scholar and MSCN. Google Scholar gives few recent papers but most of the outdated papers which are much old as they are retrieved from past two decades, whereas recommendations by MSCN are relevant but it is unable to recommend recent and quality papers. RMF recommends most of the papers from current decade as well as from previous decade. But recommendations by RRMF are not as much older as by Google Scholar or MSCN. Thus, the graph plotted for NDCG clearly depicts the better performance result of RRMF as compared to Google Scholar and MSCN as shown in Fig.  8 . For MAP and MRR, Google Scholar performs partially better but RRMF when compared with MSCN shows improved performance outcome.

Inferences drawn from our experimentation performed for several cases showed improved performance of RRMF as compared to Google Scholar and MSCN. RRMF recommends all of the relevant papers considering into account the quality of paper too as compared to MSCN. RRMF is based on the citation network so, the reference list contains all of the related papers that matches with the user’s paper of interest. In addition, it incorporates the authors ranking procedure too for improved relevant recommendations. Furthermore, the centrality measures used in the proposed method are helpful in determining the influential papers in case where lack of citation count occurs.

Conclusion and future directions

Growth rate of scientific publications recommended by recommendation systems is expanding on daily basis. Filtering most relevant research papers that are suitable to researchers’ field of interest from such vast amount of information becomes quite difficult for researchers. Therefore, the need emerges for developing more effective research paper recommender system which can overcome the limitation of time-consumption. Existing research recommendation systems mostly are based on collaborative filtering, content-based filtering, link-based and information retrieval techniques as their recommendation approaches. The collaborative filtering approach have rating-imbalance and data sparsity issues. Content-based and information retrieval recommendation approaches also have certain limitations as they ignore the popularity and quality of research paper. These research approaches cannot consider the mutual relationship between papers. To overcome this, citation analysis based on concept of bibliographic coupling and co-citation analysis is introduced in this study. Simple citation analysis is not sufficient because it is complex and difficult to handle citation relation between papers. Although it handles complex relationships among papers more appropriately than single-level network. To achieve desired results only MSCN is not sufficient to recommend important and useful research papers. This cannot be done without incorporating a technique which integrates multiple features such as citation between papers and information of authors. Therefore, the current research emphasizes on developing support system as a research recommendation system that provides important and useful papers with top ranked authors. To assess the effectiveness of the proposed RRMF system, the research results are compared with baseline approach MSCN and Google Scholar. The information retrieval metrics MAP, MRR and NDCG are used as evaluation measures. The proposes RRMF outperforms the traditional approaches by recommending high quality research papers.

In future, additional features of journal impact factor can be incorporated to construct more informative network. In addition to journal impact factor feature, relationship between papers is measured by considering the total number of citations used in the content of research paper. It is apparent that if paper cites paper of interest for multiple times, then it is more relevant to paper of interest. The current research uses only the references for citation analysis and the number of papers cited in the content of paper are not considered.

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Department of Computer Science, Faculty of Computing and Information Technology, International Islamic University, Islamabad, Pakistan

Tayyaba Kanwal & Tehmina Amjad

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Kanwal, T., Amjad, T. Research paper recommendation system based on multiple features from citation network. Scientometrics (2024). https://doi.org/10.1007/s11192-024-05109-w

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Received : 02 January 2024

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Published : 30 July 2024

DOI : https://doi.org/10.1007/s11192-024-05109-w

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Multivariable Mendelian randomization to disentangle the alcohol 1 harm paradox.

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The alcohol harm paradox, whereby low socioeconomic position (SEP) groups experience greater alcohol-related harms despite reporting lower alcohol consumption, is yet to be fully understood through observational studies because key drivers are correlated and share similar confounding structures. Multivariable Mendelian randomization (MVMR) were conducted to estimate the direct causal effect of number of drinks per week (DPW) and years of schooling (YOS) on multiple health outcomes. Previously published genome-wide association summary (GWAS) statistics for DPW and YOS were utilised, and summary statistics were generated from individual-level data from UK Biobank (N = 462,818) for all health outcomes. Inverse variance weighted analyses demonstrated evidence for direct effects of DPW and YOS on liver diseases, mental and behavioural disorders due to alcohol, and stroke, indicating that increasing alcohol consumption increased the likelihood of outcomes whereas increasing years of education decreased their likelihood. There was also evidence for a direct effect of DPW on depression, anxiety, influenza/pneumonia, and heart disease. In contrast, there was evidence of a total, but not direct, effect of DPW on depression, influenza/pneumonia, epilepsy, and injuries when accounting for YOS. Although caution is required when interpreting these results due to weak instruments for alcohol, these results provide some evidence that the alcohol harm paradox is partially due to the protective effect of additional years of education, resulting in a reduced likelihood of higher SEP groups developing many alcohol-related outcomes. Replication with strong instruments would be necessary to draw causal inferences.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

GS, HS, MM, LM, and JK are all members of the Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol. This publication is the work of the authors and they will serve as guarantors for the contents of this paper. GS is supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (ref: 218495/Z/19/Z). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval for this study was sought from the UK Biobank (project 9142). UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC). The REC reference for UK Biobank is 11/NW/0382. Further details about the ethics approval sought for data collection in UK Biobank can be found online (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

UK Biobank data is available at www.ukbiobank.ac.uk. GSCAN data is available at doi:10.1038/s41588-018-0307-5. SSGAC data is available at doi:10.1038/nature17671.

https://www.ukbiobank.ac.uk/

doi:10.1038/s41588-018-0307-5

doi:10.1038/nature17671

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