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Research Philosophy & Paradigms

Positivism, Interpretivism & Pragmatism, Explained Simply

By: Derek Jansen (MBA) | Reviewer: Eunice Rautenbach (DTech) | June 2023

Research philosophy is one of those things that students tend to either gloss over or become utterly confused by when undertaking formal academic research for the first time. And understandably so – it’s all rather fluffy and conceptual. However, understanding the philosophical underpinnings of your research is genuinely important as it directly impacts how you develop your research methodology.

In this post, we’ll explain what research philosophy is , what the main research paradigms  are and how these play out in the real world, using loads of practical examples . To keep this all as digestible as possible, we are admittedly going to simplify things somewhat and we’re not going to dive into the finer details such as ontology, epistemology and axiology (we’ll save those brain benders for another post!). Nevertheless, this post should set you up with a solid foundational understanding of what research philosophy and research paradigms are, and what they mean for your project.

Overview: Research Philosophy

  • What is a research philosophy or paradigm ?
  • Positivism 101
  • Interpretivism 101
  • Pragmatism 101
  • Choosing your research philosophy

What is a research philosophy or paradigm?

Research philosophy and research paradigm are terms that tend to be used pretty loosely, even interchangeably. Broadly speaking, they both refer to the set of beliefs, assumptions, and principles that underlie the way you approach your study (whether that’s a dissertation, thesis or any other sort of academic research project).

For example, one philosophical assumption could be that there is an external reality that exists independent of our perceptions (i.e., an objective reality), whereas an alternative assumption could be that reality is constructed by the observer (i.e., a subjective reality). Naturally, these assumptions have quite an impact on how you approach your study (more on this later…).

The research philosophy and research paradigm also encapsulate the nature of the knowledge that you seek to obtain by undertaking your study. In other words, your philosophy reflects what sort of knowledge and insight you believe you can realistically gain by undertaking your research project. For example, you might expect to find a concrete, absolute type of answer to your research question , or you might anticipate that things will turn out to be more nuanced and less directly calculable and measurable . Put another way, it’s about whether you expect “hard”, clean answers or softer, more opaque ones.

So, what’s the difference between research philosophy and paradigm?

Well, it depends on who you ask. Different textbooks will present slightly different definitions, with some saying that philosophy is about the researcher themselves while the paradigm is about the approach to the study . Others will use the two terms interchangeably. And others will say that the research philosophy is the top-level category and paradigms are the pre-packaged combinations of philosophical assumptions and expectations.

To keep things simple in this video, we’ll avoid getting tangled up in the terminology and rather focus on the shared focus of both these terms – that is that they both describe (or at least involve) the set of beliefs, assumptions, and principles that underlie the way you approach your study .

Importantly, your research philosophy and/or paradigm form the foundation of your study . More specifically, they will have a direct influence on your research methodology , including your research design , the data collection and analysis techniques you adopt, and of course, how you interpret your results. So, it’s important to understand the philosophy that underlies your research to ensure that the rest of your methodological decisions are well-aligned .

Research philosophy describes the set of beliefs, assumptions, and principles that underlie the way you approach your study.

So, what are the options?

We’ll be straight with you – research philosophy is a rabbit hole (as with anything philosophy-related) and, as a result, there are many different approaches (or paradigms) you can take, each with its own perspective on the nature of reality and knowledge . To keep things simple though, we’ll focus on the “big three”, namely positivism , interpretivism and pragmatism . Understanding these three is a solid starting point and, in many cases, will be all you need.

Paradigm 1: Positivism

When you think positivism, think hard sciences – physics, biology, astronomy, etc. Simply put, positivism is rooted in the belief that knowledge can be obtained through objective observations and measurements . In other words, the positivist philosophy assumes that answers can be found by carefully measuring and analysing data, particularly numerical data .

As a research paradigm, positivism typically manifests in methodologies that make use of quantitative data , and oftentimes (but not always) adopt experimental or quasi-experimental research designs. Quite often, the focus is on causal relationships – in other words, understanding which variables affect other variables, in what way and to what extent. As a result, studies with a positivist research philosophy typically aim for objectivity, generalisability and replicability of findings.

Let’s look at an example of positivism to make things a little more tangible.

Assume you wanted to investigate the relationship between a particular dietary supplement and weight loss. In this case, you could design a randomised controlled trial (RCT) where you assign participants to either a control group (who do not receive the supplement) or an intervention group (who do receive the supplement). With this design in place, you could measure each participant’s weight before and after the study and then use various quantitative analysis methods to assess whether there’s a statistically significant difference in weight loss between the two groups. By doing so, you could infer a causal relationship between the dietary supplement and weight loss, based on objective measurements and rigorous experimental design.

As you can see in this example, the underlying assumptions and beliefs revolve around the viewpoint that knowledge and insight can be obtained through carefully controlling the environment, manipulating variables and analysing the resulting numerical data . Therefore, this sort of study would adopt a positivistic research philosophy. This is quite common for studies within the hard sciences – so much so that research philosophy is often just assumed to be positivistic and there’s no discussion of it within the methodology section of a dissertation or thesis.

Positivism is rooted in the belief that knowledge can be obtained through objective observations and measurements of an external reality.

Paradigm 2: Interpretivism

 If you can imagine a spectrum of research paradigms, interpretivism would sit more or less on the opposite side of the spectrum from positivism. Essentially, interpretivism takes the position that reality is socially constructed . In other words, that reality is subjective , and is constructed by the observer through their experience of it , rather than being independent of the observer (which, if you recall, is what positivism assumes).

The interpretivist paradigm typically underlies studies where the research aims involve attempting to understand the meanings and interpretations that people assign to their experiences. An interpretivistic philosophy also typically manifests in the adoption of a qualitative methodology , relying on data collection methods such as interviews , observations , and textual analysis . These types of studies commonly explore complex social phenomena and individual perspectives, which are naturally more subjective and nuanced.

Let’s look at an example of the interpretivist approach in action:

Assume that you’re interested in understanding the experiences of individuals suffering from chronic pain. In this case, you might conduct in-depth interviews with a group of participants and ask open-ended questions about their pain, its impact on their lives, coping strategies, and their overall experience and perceptions of living with pain. You would then transcribe those interviews and analyse the transcripts, using thematic analysis to identify recurring themes and patterns. Based on that analysis, you’d be able to better understand the experiences of these individuals, thereby satisfying your original research aim.

As you can see in this example, the underlying assumptions and beliefs revolve around the viewpoint that insight can be obtained through engaging in conversation with and exploring the subjective experiences of people (as opposed to collecting numerical data and trying to measure and calculate it). Therefore, this sort of study would adopt an interpretivistic research philosophy. Ultimately, if you’re looking to understand people’s lived experiences , you have to operate on the assumption that knowledge can be generated by exploring people’s viewpoints, as subjective as they may be.

Interpretivism takes the position that reality is constructed by the observer through their experience of it, rather than being independent.

Paradigm 3: Pragmatism

Now that we’ve looked at the two opposing ends of the research philosophy spectrum – positivism and interpretivism, you can probably see that both of the positions have their merits , and that they both function as tools for different jobs . More specifically, they lend themselves to different types of research aims, objectives and research questions . But what happens when your study doesn’t fall into a clear-cut category and involves exploring both “hard” and “soft” phenomena? Enter pragmatism…

As the name suggests, pragmatism takes a more practical and flexible approach, focusing on the usefulness and applicability of research findings , rather than an all-or-nothing, mutually exclusive philosophical position. This allows you, as the researcher, to explore research aims that cross philosophical boundaries, using different perspectives for different aspects of the study .

With a pragmatic research paradigm, both quantitative and qualitative methods can play a part, depending on the research questions and the context of the study. This often manifests in studies that adopt a mixed-method approach , utilising a combination of different data types and analysis methods. Ultimately, the pragmatist adopts a problem-solving mindset , seeking practical ways to achieve diverse research aims.

Let’s look at an example of pragmatism in action:

Imagine that you want to investigate the effectiveness of a new teaching method in improving student learning outcomes. In this case, you might adopt a mixed-methods approach, which makes use of both quantitative and qualitative data collection and analysis techniques. One part of your project could involve comparing standardised test results from an intervention group (students that received the new teaching method) and a control group (students that received the traditional teaching method). Additionally, you might conduct in-person interviews with a smaller group of students from both groups, to gather qualitative data on their perceptions and preferences regarding the respective teaching methods.

As you can see in this example, the pragmatist’s approach can incorporate both quantitative and qualitative data . This allows the researcher to develop a more holistic, comprehensive understanding of the teaching method’s efficacy and practical implications , with a synthesis of both types of data . Naturally, this type of insight is incredibly valuable in this case, as it’s essential to understand not just the impact of the teaching method on test results, but also on the students themselves!

Pragmatism takes a more flexible approach, focusing on the potential usefulness and applicability of the research findings.

Wrapping Up: Philosophies & Paradigms

Now that we’ve unpacked the “big three” research philosophies or paradigms – positivism, interpretivism and pragmatism, hopefully, you can see that research philosophy underlies all of the methodological decisions you’ll make in your study. In many ways, it’s less a case of you choosing your research philosophy and more a case of it choosing you (or at least, being revealed to you), based on the nature of your research aims and research questions .

  • Research philosophies and paradigms encapsulate the set of beliefs, assumptions, and principles that guide the way you, as the researcher, approach your study and develop your methodology.
  • Positivism is rooted in the belief that reality is independent of the observer, and consequently, that knowledge can be obtained through objective observations and measurements.
  • Interpretivism takes the (opposing) position that reality is subjectively constructed by the observer through their experience of it, rather than being an independent thing.
  • Pragmatism attempts to find a middle ground, focusing on the usefulness and applicability of research findings, rather than an all-or-nothing, mutually exclusive philosophical position.

If you’d like to learn more about research philosophy, research paradigms and research methodology more generally, be sure to check out the rest of the Grad Coach blog . Alternatively, if you’d like hands-on help with your research, consider our private coaching service , where we guide you through each stage of the research journey, step by step.

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16 Comments

catherine

was very useful for me, I had no idea what a philosophy is, and what type of philosophy of my study. thank you

JOSHUA BWIRE

Thanks for this explanation, is so good for me

RUTERANA JOHNSON

You contributed much to my master thesis development and I wish to have again your support for PhD program through research.

sintayehu hailu

the way of you explanation very good keep it up/continuous just like this

David Kavuma

Very precise stuff. It has been of great use to me. It has greatly helped me to sharpen my PhD research project!

Francisca

Very clear and very helpful explanation above. I have clearly understand the explanation.

Binta

Very clear and useful. Thanks

Vivian Anagbonu

Thanks so much for your insightful explanations of the research philosophies that confuse me

Nigatu Kalse

I would like to thank Grad Coach TV or Youtube organizers and presenters. Since then, I have been able to learn a lot by finding very informative posts from them.

Ahmed Adumani

thank you so much for this valuable and explicit explanation,cheers

Mike Nkomba

Hey, at last i have gained insight on which philosophy to use as i had little understanding on their applicability to my current research. Thanks

Robert Victor Opusunju

Tremendously useful

Aishat Ayomide Oladipo

thank you and God bless you. This was very helpful, I had no understanding before this.

Salima

USEFULL IN DEED!

Dixon Mwase-Vuma

Explanations to the research paradigm has been easy to follow. Well understood and made my life easy.

Annette

Very useful content. This will make my research life easy.

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Integration and Implementation Insights

Integration and Implementation Insights

A community blog and repository of resources for improving research impact on complex real-world problems

A guide to ontology, epistemology, and philosophical perspectives for interdisciplinary researchers

By Katie Moon and Deborah Blackman

katie-moon

How can understanding philosophy improve our research? How can an understanding of what frames our research influence our choices? Do researchers’ personal thoughts and beliefs shape research design, outcomes and interpretation?

These questions are all important for social science research. Here we present a philosophical guide for scientists to assist in the production of effective social science (adapted from Moon and Blackman, 2014).

deborah-blackman

Understanding philosophy is important because social science research can only be meaningfully interpreted when there is clarity about the decisions that were taken that affect the research outcomes. Some of these decisions are based, not always knowingly, on some key philosophical principles, as outlined in the figure below.

Philosophy provides the general principles of theoretical thinking, a method of cognition, perspective and self-awareness, all of which are used to obtain knowledge of reality and to design, conduct, analyse and interpret research and its outcomes. The figure below shows three main branches of philosophy that are important in the sciences and serves to illustrate the differences between them.

guide-to-ontology-moon

(Source: Moon and Blackman 2014)

The first branch is ontology, or the ‘study of being’, which is concerned with what actually exists in the world about which humans can acquire knowledge. Ontology helps researchers recognize how certain they can be about the nature and existence of objects they are researching. For instance, what ‘truth claims’ can a researcher make about reality? Who decides the legitimacy of what is ‘real’? How do researchers deal with different and conflicting ideas of reality?

To illustrate, realist ontology relates to the existence of one single reality which can be studied, understood and experienced as a ‘truth’; a real world exists independent of human experience. Meanwhile, relativist ontology is based on the philosophy that reality is constructed within the human mind, such that no one ‘true’ reality exists. Instead, reality is ‘relative’ according to how individuals experience it at any given time and place.

Epistemology

The second branch is epistemology, the ‘study of knowledge’. Epistemology is concerned with all aspects of the validity, scope and methods of acquiring knowledge, such as a) what constitutes a knowledge claim; b) how can knowledge be acquired or produced; and c) how the extent of its transferability can be assessed. Epistemology is important because it influences how researchers frame their research in their attempts to discover knowledge.

By looking at the relationship between a subject and an object we can explore the idea of epistemology and how it influences research design. Objectivist epistemology assumes that reality exists outside, or independently, of the individual mind. Objectivist research is useful in providing reliability (consistency of results obtained) and external validity (applicability of the results to other contexts).

Constructionist epistemology rejects the idea that objective ‘truth’ exists and is waiting to be discovered. Instead, ‘truth’, or meaning, arises in and out of our engagement with the realities in our world. That is, a ‘real world’ does not preexist independently of human activity or symbolic language. The value of constructionist research is in generating contextual understandings of a defined topic or problem.

Subjectivist epistemology relates to the idea that reality can be expressed in a range of symbol and language systems, and is stretched and shaped to fit the purposes of individuals such that people impose meaning on the world and interpret it in a way that makes sense to them. For example, a scuba diver might interpret a shadow in the water according to whether they were alerted to a shark in the area (the shark), waiting for a boat (the boat), or expecting a change in the weather (clouds). The value of subjectivist research is in revealing how an individual’s experience shapes their perception of the world.

Philosophical perspectives

Stemming from ontology (what exists for people to know about) and epistemology (how knowledge is created and what is possible to know) are philosophical perspectives, a system of generalized views of the world, which form beliefs that guide action.

Philosophical perspectives are important because, when made explicit, they reveal the assumptions that researchers are making about their research, leading to choices that are applied to the purpose, design, methodology and methods of the research, as well as to data analysis and interpretation. At the most basic level, the mere choice of what to study in the sciences imposes values on one’s subject.

Understanding the philosophical basis of science is critical in ensuring that research outcomes are appropriately and meaningfully interpreted. With an increase in interdisciplinary research, an examination of the points of difference and intersection between the philosophical approaches can generate critical reflection and debate about what we can know, what we can learn and how this knowledge can affect the conduct of science and the consequent decisions and actions.

How does your philosophical standpoint affect your research? What are your experiences of clashing philosophical perspectives in interdisciplinary research? How did you become aware of them and resolve them? Do you think that researchers need to recognize different philosophies in interdisciplinary research teams?

To find out more : Moon, K., and Blackman, D. (2014). A Guide to Understanding Social Science Research for Natural Scientists. Conservation Biology , 28 : 1167-1177. Online:  http://onlinelibrary.wiley.com/doi/10.1111/cobi.12326/full

Biography: Katie Moon is a Post Doctoral Research Fellow at the University of New South Wales, Canberra. She is also an adjunct at the Institute for Applied Ecology at the University of Canberra. She has worked in the environmental policy arena for 17 years within Australia and Europe, in government, the private sector and academia. Her research focuses on how the right policy instruments can be paired to the right people; the role of evidence in policy development and implementation; and how to increase policy implementation success .

Biography: Deborah Blackman is a Professor in Public Sector Management Strategy and Deputy Director of the Public Service Research Group at the University of New South Wales, Canberra. She researches knowledge transfer in a range of applied, real world contexts. The common theme of her work is creating new organisational conversations in order to improve organisational effectiveness. This has included strengthening the performance management framework in the Australian Public Service; the role of social capital in long-term disaster recovery; and developing a new diagnostic model to support effective joined-up working in whole of government initiatives .

Related posts:

A guide for interdisciplinary researchers: Adding axiology alongside ontology and epistemology by Peter Deane https://i2insights.org/2018/05/22/axiology-and-interdisciplinarity/

Epistemological obstacles to interdisciplinary research by Evelyn Brister https://i2insights.org/2017/10/31/epistemology-and-interdisciplinarity/

Transforming transdisciplinarity: Interweaving the philosophical with the pragmatic to move beyond either/or thinking by Katie Ross and Cynthia Mitchell https://i2insights.org/2018/11/13/transdisciplinarity-and-either-or-thinking/

What is the role of theory in transdisciplinary research? by Workshop Group on Theory at 2015 Basel International Transdisciplinary Conference http://i2insights.org/2016/02/17/role-of-theory-in-transdisciplinary-research/

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13 thoughts on “a guide to ontology, epistemology, and philosophical perspectives for interdisciplinary researchers”.

Hi Katie and Deborah, First of all want to thank you for such incredible synthesis! Then I want to ask you, how can we situate a paradigm or an school or though in this map? For example, where do you think we can situate the complex paradigm of Edgar Morin? in between the relativistic ontology? or critical theory? thanks in advance.

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The table summary is admirable. All your write is very nice

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Great post! I really like the table and find it a very helpful illustration!

Hi Kate, thank you very much for helping out. I understand the subject matter more now than before Olushola

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Thanks so much for the debate and discussion around the blog post. Machiel is right in pointing out that the blog post (and the article it is based on) was intended as a conversation piece, and we’re pleased that a useful conversation is taking place. The resources and links are very helpful, philosophy is a fascinating discipline and the opportunity to learn and expand our thinking is endless.

We tried to make it clear in the article the blog post is based on that we wanted to bring attention to philosophy; it was obviously impossible to do the discipline of philosophy any real justice within 6,000 words. We wanted to start a conversation: “The purpose of the guide is to open the door to social science research and thus demonstrate that scientists can bring different and legitimate principles, assumptions, and interpretations to their research.”

As Jessica and Melissa point out, it can be challenging to offer social research to a natural science community that typically adopts a narrow philosophical position (e.g. objectivist). The paper was intended to encourage natural scientists to consider alternative ways of generating knowledge, particularly about the human, as opposed to natural, world.

We accept unequivocally that the framework does not get close to accommodating the depth and diversity of philosophy. Adam, we agree that the approach we have taken may not resonate with some philosophers, but we wanted to communicate with a particular audience (conservation scientists) and so we defined ontologies and epistemologies (and posited them relative to one another) that are most commonly observed within this discipline and that might be best understood by the audience. We tried to identify points of difference between ontologies, epistemologies and philosophical perspectives in an attempt to explain how they can influence research design. In the article, we use a case of deforestation in rainforests to demonstrate how different positions can influence the nature of the research questions and outcomes, including the assumptions that will be made.

We did explain in the introduction to our paper the limitations of our approach: “The multifaceted nature and interpretation of each of the concepts we present in our guide means they can be combined in a diversity of ways (see also Lincoln & Guba 2000; Schwandt 2000; Evely et al. 2008; H¨oijer 2008; Cunliffe 2011; Tang 2011). Therefore, our guide represents just one example of how the elements (i.e., different positions within the main branches of philosophy) of social research can apply specifically to conservation science. We recognize that by distilling and defining the elements in a simplified way we have necessarily constrained argument and debate surrounding each element. Furthermore, the guide had to have some structure. In forming this structure, we do not suggest that researchers must consider first their ontological and then their epistemological position and so on; they may well begin by exploring their philosophical perspective.”

This point comes back to Bruce’s comment, about pragmatic approaches to research. Often researchers pick and choose between a range of options that will allow them to define and answer their research questions in a way that makes most sense to them. We make this point in the paper: “Each perspective is characterized by an often wide ranging pluralism, which reflects the complex evolution of philosophy and the varied contributions of philosophers through time (Crotty 1998). All ontologies, epistemologies, and philosophical perspectives are characterized by this pluralism, including the prevailing (post) positivist approach of the natural sciences. It is common for more than one philosophical perspective to resonate with researchers and for researchers to change their perspective (and thus epistemological and ontological positions) toward their research over time (Moses & Knutsen 2012). Thus, scientists do not necessarily commit to one philosophical perspective and all associated characteristics (Bietsa 2010).”

We tried to anticipate concerns that scholars of philosophy might have with our rather reductionist approach, but felt that the more important contribution to make was to bring attention to alternative worldviews, and highlight the importance of philosophy in generating any type of knowledge.

With respect to the characterization of epistemologies, we adopted a continuum provided by Crotty (1998) that focuses on the relationship between the subject and the object. Again, this choice was made on the basis of our audience, to demonstrate that different types of relationship can exist between subject and object

This blog post has generated an interesting discussion on the Association for Interdisciplinary Studies listserv ([email protected]). Selected excerpts below.

Adam Potthast: I hate to make one of my first posts to this list critical without the time to correct some of the errors, but I don’t think you’d see many philosophers agreeing with the characterizations of philosophical views in this post. The infographic strongly mischaracterizes a lot of these positions, and the section on epistemology doesn’t map on to any of the standard understandings of epistemology in the discipline of philosophy. I’d caution against thinking of it as a reliable source to the philosophy behind science.

Gabriele Bammer: Thanks Adam for raising the alarm. It would be great if you and/or others who have problems with this post would spell out your criticisms – not only via this listserv, but (more importantly from my perspective) in a comment on the blog itself. Non-philosophers are hungry for a version of epistemology, ontology etc that they can understand and use and this blog post (and the paper it is based on) address this need. If it is seriously misleading though, that’s obviously a problem. It’s important that this is pointed out and that better alternatives are offered. I appreciate that time is an issue for everyone – anything you can do will be appreciated.

Stuart Henry: Well a good start, so we don’t reinvent the wheel again is James Welch’s article: https://oakland.edu/Assets/upload/docs/AIS/Issues-in-Interdisciplinary-Studies/2009-Volume-27/05_Vol_27_pp_35_69_Interdisciplinarity_and_the_History_of_Western_Epistemology_(James_Welch_IV) .pdf

Gabriele Bammer: Thanks Stuart, I may be missing something, but it seems to me that Welch’s article covers different terrain, being more about the philosophy underpinning interdisciplinarity. What Moon and Blackman provide is a quick guide to understanding people’s different philosophical positions, so that if you are working in a team, for example, you can better understand why someone sees the world differently. The Toolbox developed by Eigenbrode, O’Rourke and others provides a practical way of uncovering these differences.

Julie Thompson Klein: Good point Gabriele about the value of the Toolbox, though people still need the kind of background you’re aiming to provide.

Machiel Keestra: Although I agree that the blog post should perhaps not so much be taken to offer a current representation of the main positions in philosophy of science or about the interconnections between epistemological and ontological positions, I think it does a nice job in offering a conversation piece: what are relevant positions and options that people might -implicitly– take and how are they different from other positions. Given the modest ambitions of the authors, I think that is a fair result.

In addition to the interesting approach offered by the Toolbox Project, an alternative is presented in Jan Schmidt’s Towards a philosophy of interdisciplinarity: http://link.springer.com/article/10.1007%2Fs10202-007-0037-8 In our Introduction to interdisciplinary research, I’ve inserted an all-too brief philosophy of science which should help to raise some understanding of this difficult issue as well: https://www.academia.edu/22420234/An_Introduction_to_Interdisciplinary_Research._Theory_and_Practice

Lovely work! Thank you. I am also initially trained as a natural scientist, and now consider myself a ‘social-ecological researcher’ and have had to do a lot of learning about ontologies, epistemologies etc. I think I might use this paper as a discussion paper in our department as I think it is crucial for interdisciplinarians to understand these issues.

Kia ora Katie and Debbie, great post! I am a biophysical scientist who has come to social science and one of the struggles is being able to place the new and relevant concepts about questions that we don’t necessarily ask as biophysical scientists. Your table is a really useful aid to this – I immediately sent it to all my colleagues! It also makes it clearer to me how I can use the concept of triangulation that Bruce alluded to in his reply. So thank you for explaining so concisely. Thanks, Melissa

Hi Katie and Deborah,

Thank you for that discussion. I think that you have created a really useful table showing the philosophical continuums/polarities, how the various ontological and epistemological positions relate to each other, and the importance for researchers to be aware of them. In my own research practice, I am not committed to any one particular philosophical theory or perspective. They all appear to be true to some degree, that is, in some conceivable context – even though some of the concepts and philosophical positions appear, in the extreme form of their statement, to be contradictory, that is, if one end of a continuum/polarity is true then by implication it seems the other must be false – thus creating a quandary of research perspective. Hence the attraction, for me, of the application of a multiplicity of methods, approaches and philosophical perspectives – as and when they seem able to give ontological or epistemological insight – with triangulation between the results of the disparate approaches as the temporary arbiter of an evolving meaning and truth. This might be considered a pragmatic, perhaps even an opportunistic, approach to conducting science. However, as the old adage goes “the proof is in the pudding” – how useful is the knowledge obtained?

cheers Bruce

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Home > Books > Management Culture and Corporate Social Responsibility

Philosophy and Paradigm of Scientific Research

Submitted: 17 August 2017 Reviewed: 21 August 2017 Published: 18 April 2018

DOI: 10.5772/intechopen.70628

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Management Culture and Corporate Social Responsibility

Authored by Pranas ?ukauskas, Jolita Vveinhardt and Regina Andriukaitien?

To purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www.cbspd.com | [email protected]

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Before carrying out the empirical analysis of the role of management culture in corporate social responsibility, identification of the philosophical approach and the paradigm on which the research carried out is based is necessary. Therefore, this chapter deals with the philosophical systems and paradigms of scientific research, the epistemology, evaluating understanding and application of various theories and practices used in the scientific research. The key components of the scientific research paradigm are highlighted. Theories on the basis of which this research was focused on identification of the level of development of the management culture in order to implement corporate social responsibility are identified, and the stages of its implementation are described.

  • philosophy of scientific research
  • epistemology
  • values and beliefs
  • basic beliefs
  • formal and informal factors

Author Information

Pranas žukauskas.

  • Vytautas Magnus University, Lithuania

Jolita Vveinhardt *

Regina andriukaitienė.

  • Marijampolė College, Lithuania
  • Lithuanian Sports University, Lithuania

*Address all correspondence to: [email protected]

1. Introduction

1.1. relevance of the research.

Scientific research philosophy is a system of the researcher’s thought, following which new, reliable knowledge about the research object is obtained. In other words, it is the basis of the research, which involves the choice of research strategy, formulation of the problem, data collection, processing, and analysis. The paradigm of scientific research, in turn, consists of ontology, epistemology methodology, and methods. Methodological choice, according to Holden and Lynch [ 1 ], should be related to the philosophical position of the researcher and the analyzed social science phenomenon. In the field of research, several philosophical approaches are possible; however, according to the authors, more extreme approaches can be delimiting. Only intermediary philosophical approach allows the researcher to reconcile philosophy, methodology, and the problem of research. However, Crossan [ 2 ] drew attention to the fact that sometimes there is a big difference between quantitative and qualitative research philosophies and methods, and triangulation of modern research methods is common. It is therefore very important to understand the strengths and weaknesses of each approach. This allows preparing for the research and understanding the analyzed problem better. The theories of research philosophy and paradigms, on the basis of which the research in the monograph focuses on identifying the level of development of the management culture in order to implement corporate social responsibility, are presented in figures that distinguish the levels of organizational culture and their interaction, that is, corporate social responsibility stages, which reflect the philosophy and paradigm of this research.

The problem of the research is raised by the following questions: what are the essential principles of research philosophy and paradigm? and how to apply them to form the research position?

The level of problem exploration. The chapter presents the thoughts of the authors who analyze research philosophy [ 3 , 4 , 5 , 6 , 7 , 8 ] and paradigm [ 3 , 9 , 10 , 11 ], relating them to the key researches of this monograph.

The object of this study is to understand essential principles of research philosophy and paradigm.

The purpose of the research is to analyze the essential principles of research philosophy and paradigm, substantiating the position of the key researches of this monograph.

The objectives of this research are (1) to discuss the fundamental aspects of research philosophy and paradigm; and (2) to substantiate the position of culture management and corporate social responsibility research.

Methods of the research. The descriptive method, analysis of academic sources, generalization, and systematization were used as the methods in this study. Graphical representation and modeling methods were used to convey the position of the research.

2. Philosophy and paradigm of scientific research

2.1. scientific research philosophy.

Each researcher is guided by their own approach to the research itself. It is said that Mill [ 12 ] was the first who called representatives of social sciences to compete with ancient sciences, promising that if his advice was followed, the sudden maturity in these sciences would appear. In the same way as their education appeared from philosophical and theological frames that limited them. Social sciences accepted this advice (probably to a level that would have surprised Mill himself if he were alive) for other reasons as well [ 3 , 13 ]. Research philosophy can be defined as the development of research assumption, its knowledge, and nature [ 7 ]. The assumption is perceived as a preliminary statement of reasoning, but it is based on the philosophizing person’s knowledge and insights that are born as a product of intellectual activity. Hitchcock and Hughes [ 4 ] also claim that research stems from assumptions. This means that different researchers may have different assumptions about the nature of truth and knowledge and its acquisition [ 6 ]. Scientific research philosophy is a method which, when applied, allows the scientists to generate ideas into knowledge in the context of research. There are four main trends of research philosophy that are distinguished and discussed in the works by many authors: the positivist research philosophy, interpretivist research philosophy, pragmatist research philosophy, and realistic research philosophy.

Positivist research philosophy . It claims that the social world can be understood in an objective way. In this research philosophy, the scientist is an objective analyst and, on the basis of it, dissociates himself from personal values and works independently.

The opposite to the above-mentioned research philosophy is the interpretivist research philosophy, when a researcher states that on the basis of the principles it is not easy to understand the social world. Interpretivist research philosophy says that the social world can be interpreted in a subjective manner. The greatest attention here is given to understanding of the ways through which people experience the social world. Interpretivist research philosophy is based on the principle which states that the researcher performs a specific role in observing the social world. According to this research philosophy, the research is based and depends on what the researcher’s interests are.

Pragmatist research philosophy deals with the facts. It claims that the choice of research philosophy is mostly determined by the research problem. In this research philosophy, the practical results are considered important [ 5 ]. In addition, according to Alghamdi and Li [ 14 ], pragmatism does not belong to any philosophical system and reality. Researchers have freedom of choice. They are “free” to choose the methods, techniques, and procedures that best meet their needs and scientific research aims. Pragmatists do not see the world as absolute unity. The truth is what is currently in action; it does not depend on the mind that is not subject to reality and the mind dualism.

Realistic research philosophy [ 5 ] is based on the principles of positivist and interpretivist research philosophies. Realistic research philosophy is based on assumptions that are necessary for the perception of subjective nature of the human.

2.1.1. Scientific research paradigm

The scientific research paradigm helps to define scientific research philosophy. Literature on scientific research claims that the researcher must have a clear vision of paradigms or worldview which provides the researcher with philosophical, theoretical, instrumental, and methodological foundations. Research of paradigms depends on these foundations [ 14 ]. According to Cohen et al. [ 6 ], the scientific research paradigm can be defined as a wide structure encompassing perception, beliefs, and awareness of different theories and practices used to carry out scientific research. The scientific research paradigm is also characterized by a precise procedure consisting of several stages. The researcher, getting over the mentioned stages, creates a relationship between research aims and questions. The term of paradigm is closely related to the “normal science” concept. Scientists who work within the same paradigm frame are guided by the same rules and standards of scientific practice. “That is how the scientific community supports itself,” claims Ružas [ 15 ] citing the French post-positivist Kuhn [ 16 ].

The scientific research paradigm and philosophy depend on various factors, such as the individual's mental model, his worldview, different perception, many beliefs, and attitudes related to the perception of reality, etc. Researchers' beliefs and values are important in this concept in order to provide good arguments and terminology for obtaining reliable results. The researcher’s position in certain cases can have a significant impact on the outcome of the research [ 11 ]. Norkus [ 17 ] draws attention to the fact that the specialists of some subjects of natural science are able by using free discussion to come to general conclusions the innovations of which are really “discoveries,” some of them are significant and some are not. Such consensus is difficult to achieve in social sciences. Academic philosophers claim this fact by the statement that “multi-paradigmatism” is characteristic to the humanities and social sciences, i.e., the permanent coexistence and competition of many different theoretical paradigms.

Gliner and Morgan [ 9 ] describe the scientific research paradigm as the approach or thinking about the research, the accomplishing process, and the method of implementation. It is not a methodology, but rather a philosophy which provides the process of carrying out research, i.e., directs the process of carrying out research in a particular direction. Ontology, epistemology, methodology, and methods describe all research paradigms [ 3 , 10 , 14 ]. Easterby-Smith et al. [ 18 ] discuss three main components of the scientific research paradigm, or three ways in order to understand the philosophy of research ( Table 1 ).

Table 1.

Three components of scientific research paradigm.

Source: Easterby-Smith et al. [ 18 ].

The three paradigms (positivist, constructivist, and critical) which are different by ontological, epistemological, and methodological aspects are also often included in the classification of scholarly paradigms [ 19 ]. In addition, Mackenzie and Knipe [ 20 ] present unique analysis of research paradigms with the most common terms associated with them. According to Mackenzie and Knipe [ 20 ], the description of the terminology is consistent with the descriptions by Leedy and Ormrod [ 21 ] and Schram [ 22 ] appearing in literature most often, despite the fact that it is general rather than specific to disciplines or research. Somekh and Lewin [ 23 ] describe methodology as a set of methods and rules, on the basis of which the research is carried out, and as “the principles, theories and values underlying certain approach to research.” In Walter’s [ 24 ] opinion, methodology is the support research structure, which is influenced by the paradigm in which our theoretical perspective “lives” or develops. Mackenzie and Knipe [ 20 ] state that in most common definitions, it is claimed that methodology is a general approach to research related to the paradigm or theoretical foundation, and the method includes the systematic ways, procedures, or tools used for data collection and analysis ( Figure 1 ).

what is the importance of research philosophy

Figure 1.

Paradigms: terminology, methods, and means of data collection. Source: Adapted by the authors: Mackenzie and Knipe [ 20 ], Mertens [ 25 ], Creswell [ 10 ].

Mackenzie and Knipe [ 20 ] state that it is the paradigm and the research question that should determine which data collection and analysis methods (qualitative/quantitative or mixed) would be the most appropriate for research. In this way, the researchers do not become “the researchers of quantitative, qualitative or mixed methods,” but they adapt the data collection and analysis method that is most suitable for a specific research. According to the authors, the use of several methods may be possible to adapt to any and all paradigms instead of having one single method that could potentially dilute and unnecessarily limit the depth and richness of the research project.

The scientific paradigm refers to a range of problems, by presenting ways of their solutions. The methods are detailed and compared in Table 2 with regard to the basic paradigms.

Table 2.

Comparison of the main paradigms with regard to ontology, epistemology, and research methods.

Source: Adapted by the authors according to Hitchcock and Hughes [ 4 ], Kuhn [ 16 ], Mackenzie and Knipe [ 20 ], Walker and Evers [ 26 ], Brewerton and Millward [ 27 ], Delanty and Strydom [ 28 ], Bagdonas [ 29 ], Phiri [ 30 ], etc.

Although the paradigm has already been mentioned, but for the researcher, in order to understand different combinations of research methods, it is necessary to analyze the basic concepts and to perceive the philosophical position of research problems.

Kuhn [ 16 ] introduced the concept of paradigm (gr. paradeigma—example model) in the science philosophy. Kuhn calls a paradigm a generally accepted scientific knowledge achievement which provides the scientists with problem raising and solving methods for a period of time. According to the author, when some old ideas are being replaced by the new ones, i.e., better, more advanced, etc., then the progress in science is stated. In natural sciences, this is going on confirming the hypothesis by logical arguments and empirical research. When the scientific community reaches a consensus, there appears accepted theory on its basis [ 16 ]. Bagdonas [ 29 ] describes a paradigm as the whole of theoretical and methodological regulations, that is, regulations adopted by the scientific community at a certain stage of development of science and applied as an example, the model, the standard for scientific research, interpretations, evaluation, and hypotheses to understand and solve objectives arising in the process of scientific knowledge. The transition from one competing paradigm to another is the transition from one non-commensurable thing to the other, and it cannot go step by step, promoted by logical and neutral experience [ 31 ].

A more detailed discussion of ontology requires the emphasis of the insights of various scientists. Hitchcock and Hughes [ 4 ] state that ontology is the theory of existence, interested in what exists, and is based on assertions of a particular paradigm about reality and truth. Other authors [ 28 ] simply identify it as a theory about the nature of reality. Hatch [ 32 ] notes that ontology is related to our assumptions about reality, i.e., whether reality is objective or subjective (existing in our minds). The most important questions that differentiated the research by far are threefold and depend on whether differences among assumptions are associated with different reality construction techniques (ontology) where, according to Denzin and Lincoln [ 33 ], the majority of questions asked are “what are the things in reality?” and “how do they really happen?”. Ontological questions are usually associated with real existence and operation matters [ 33 ], varying forms of knowledge about reality (epistemology), since epistemological questions help to ascertain the nature of relationship between the researcher and the respondent, and it is postulated that in order to make an assumption about the true reality, the researcher must follow the “objectivity and value distancing position” to find out what things are in reality, how they occur [ 33 ], and certain reality cognition techniques (methodology). With the help of methodological questions, the researcher mostly tries to figure out ways by which he can get to know his concerns [ 33 ].

Further analysis of the epistemology terminology presents different interpretations by various authors. For example, according to Brewerton and Millward [ 27 ], epistemology refers to the examination of what separates reasonable assurance from the opinion. According to Walker and Evers [ 26 ], generally speaking, epistemology is interested in how the researcher can receive knowledge about the phenomena of interest to him. Wiersma and Jurs [ 11 ] describe epistemology as a research which attempts to clarify the possibilities of knowledge, the boundaries, the origin, the structure, methods and justice, and the ways in which this knowledge can be obtained, confirmed, and adjusted. Hitchcock and Hughes [ 4 ], talking about the impact on epistemology, emphasize that it is very big for both data collection methods and research methodology. Hatch [ 32 ] highlights the idea that epistemology is concerned with knowledge—specific questions presented by the epistemology researchers are how people create knowledge, what the criteria enabling the distinction of good and bad knowledge are, and how should reality be represented or described? Epistemology is closely related to ontology, because the answers to these questions depend on the ontological assumptions about the nature of reality and, in turn, help to create them. Sale et al. [ 34 ], Cohen et al. [ 6 ], and Denzin and Lincoln [ 33 ] note that epistemological assumptions often arise from ontological assumptions. The former encourage a tendency to focus on methods and procedures in the course of research. Šaulauskas [ 35 ] points out that, in general, modern Western philosophy is a “pure” epistemology establishment, and its systemic dissemination vector is basically the reduction of the whole theoretical vision of gender in epistemological discussion.

It is said that in order to understand the reality there are three main types of paradigms to be employed, namely positivism, interpretivism, and realism. The conception of positivism is directly related to the idea of objectivism. Using this philosophical approach, the researchers express their views in order to assess the social world, and instead of subjectivity, they refer to objectivity [ 36 ]. Under this paradigm, researchers are interested in general information and large-scale social data collection rather than focusing on details of the research. In line with this position, the researchers' own personal attitudes are not relevant and do not affect the scientific research. Positivist philosophical approach is most closely associated with the observations and experiments, used for collection of numerical data [ 18 ]. In the sphere of management research, interpretivism can still be called social constructionism. With this philosophical point of view, the researchers take into account their views and values so that they could justify the problem posed in the research [ 18 ]. Kirtiklis [ 37 ] notes that while positivistic philosophy critical trend encourages strict separation of scientific problems solved by research from “speculative” philosophical problems and thus rejects the philosophy, the other trend, called interpretivism, on the contrary, states that philosophy cannot be strictly separated from social sciences, but it must be incorporated or blended into them. With the help of this philosophy, the scientists focus on the facts and figures corresponding to the research problem. This type of philosophical approach makes it possible to understand specific business situations. Using it, the researchers use small data samples and assess them very carefully in order to grasp the attitudes of larger population segments [ 38 ]. Realism, as a research philosophy, focuses on reality and beliefs existing in a certain environment. Two main branches of this philosophical approach are direct and critical realism [ 39 ]. Direct realism is what an individual feels, sees, hears, etc. On the other hand, in critical realism, the individuals discuss their experience in specific situations [ 40 ]. It is a matter of social constructivism, as individuals try to justify their own values and beliefs.

Analyzing other types of paradigms, in a sense, not qualified as the main, constructivism, symbolic interpretivism, pragmatism should be mentioned. The constructivism paradigm in some classifications of paradigms is called the “interpretative paradigm” [ 19 ]. There is no other definition in ontology, epistemology, and methodology; both approaches [ 41 ] have a common understanding of the complex world experience from the perspective of the individuals having this experience. The constructivists point out that various interpretations are possible because we have multiple realities. According to Onwuegbuzie [ 42 ], the reality for constructivists is a product of the human mind, which develops socially, and this changes the reality. The author states that there is dependence between what is known and who knows. So, for this reason, the researcher must become more familiar with what is being researched. Analyzing symbolic interpretivism through the prism of ontology, it can be said that it is the belief that we cannot know the external or objective existence apart from our subjective understanding of it; that, what exists, is what we agree on that it exists (emotion and intuition: experience forms behind the limits of the five senses). Analyzing symbolic interpretivism through epistemological aspect, all knowledge is related to the one who knows and can be understood only in terms of directly related individuals; the truth is socially created through multiple interpretations of knowledge objects created in this way, and therefore they change over time [ 32 ]. Pikturnaitė and Paužuolienė [ 43 ] note that scientists in most cases when analyzing organizational culture communication and dissemination examine the behavior, language, and other informal aspects that need to be observed, understood, and interpreted. Pragmatism, as a philosophy trend, considers practical thinking and action ways as the main, and the criterion of truth is considered for its practical application. However, as noted by Ružas [ 15 ] who analyzed Kuhn’s approach [ 16 ], since there are many ways of the world outlook and it is impossible to prove that one of them is more correct than the other, it should be stated only that in the science development process, they change each other.

The theories, according to which this research concentrates on the management culture development-level setting for the implementation of corporate social responsibility, are presented in Figure 2 , which distinguishes organizational culture levels and their interaction. Figure 3 defines corporate social responsibility stages that reflect the scientific research philosophy and the paradigm of this survey.

what is the importance of research philosophy

Figure 2.

Management culture in the context of organizational culture. Source: Adapted by the authors according to French and Bel [ 44 ], Schein [ 45 , 46 ], Ott [ 47 ], Bounds et al. [ 48 ], Krüger [ 49 ], Franklin and Pagan [ 50 ], etc.

what is the importance of research philosophy

Figure 3.

Corporate social responsibility stages. Source: Adapted by the authors according to Ruževičius [ 52 ].

In order to relatively “separate” management culture from organizational culture, one must look into their component elements of culture. For this reason, below organizational culture levels and components forming them are discussed in detail.

According to Schein [ 45 , 46 ], artifacts are described as the “easiest” observed level, that is, what we see, hear, and feel. The author presents a model that if you happen to go to organizations, you can immediately feel their uniqueness in the way “they perform the work,” that is, open-space office against closed-door offices; employees freely communicating with each other against the muted environment; and formal clothing against informal clothing. However, according to the author, “you should be careful by appealing to these attributes when deciding whether we like or do not like the organization, whether it is operating successfully or unsuccessfully, as at this observation stage it is not clear why organizations present themselves and interact with one another in such a particular way.” Schein [ 45 , 46 ] elaborates the supported values by considerations that “in order to better understand and decipher why the observed matters happen on the first level, people within the organization should be asked to explain that. For example, what happens when it is established that two similar organizations have very similar company values recorded in documents and published, principles, ethics and visions in which their employees believe and adhere to – i.e., described as their culture and reflecting their core values – for all that, the natural formation and working styles of the two organizations are very different, even if they have similar supported values?” According to the author, in order to see these “imbalances,” you need to realize that “unhindered behavior leads to a deeper level of thought and perception.” In shared mental models, for understanding this “deeper” level of culture, one should study the history of the organization, that is, what were the original values, beliefs, and assumptions of its founders and key leaders, which led to the success of the organization? Over time they have become common and are accepted as self-evident as soon as new members of the organization realized that the original values, beliefs, and assumptions of its founders led to organizational success, that is, through common cognition/assimilation of “correct” values, beliefs, and assumptions. Cultural levels distinguished by Schein [ 45 , 46 ] can be “transferred” to the organizational culture iceberg levels formed by French and Bel [ 44 ]. According to the authors [ 45 , 46 , 47 , 48 , 51 ], visible organizational structures consist of ceremonies, communication, heroes, habits, management methods, and so on. French and Bel [ 44 ] distinguish between these formal and informal elements of organizational culture: formal—aims, technology, structure, skills and abilities, financial resources; informal—approaches, values; feelings—anger, fear, frustration, etc.; and interaction group rates. Franklin and Pagan [ 50 ] detail the formal and informal structure of organizational culture factors, allocating them into tangible and intangible factors. Tangible factors (formal or officially authorized) are socialization and/or acculturation experience (if the organization takes care of timely and detailed orientation, it is more likely that the manager will use the process of formal discipline); written documents (if the manager is presented with the relevant policy and relevant procedures, it is more likely that the manager will use the formal discipline process); training (if the organization organizes training on discipline issues, it is more likely that the manager will use the formal discipline process); and structure of the organization (if the organization provides the power to the manager and if the manager has more control, it is more likely that the manager will use the formal discipline process). Intangible factors (informal or informally developed) [ 50 ] include problematic employees (if the employee does not have good professional skills or high position, it is more likely that the manager will use the formal discipline process); socialization/acculturation which manifests itself in the human resource management subdivision activities (if the manager’s solutions are supported and not devalued by organizational management, it is more likely that the manager will use the formal discipline process); the same social status people (if other managers focus on formal discipline process, it is more likely that the manager will use the formal discipline process); groups outside work (if systems of values, partly overlapping, cherished by groups outside, strengthen the organizational culture-supported expectations, it is more likely that the manager will use the formal discipline process). Krüger [ 49 ] formed the change management iceberg which deals with both visible and invisible barriers in the organization. With the help of this iceberg, there is an attempt to force the management to look into the hidden challenges that need to be overcome in order to implement changes in the organization. Iceberg model is relevant to the submitted research presented in this book in the way that implementation of corporate social responsibility is considered as a strong change in the activities of the organization. As stated by Krüger [ 49 ], the change management iceberg is best perceived by managers who understand that the most obvious change obstacles that need to be overcome, such as cost, quality, and time, are only the top of the iceberg, and more complicated obstacles, which have more influence, lie below. The foundation of change management theory is based on the fact that many managers tend to focus only on the obvious obstacles, instead of paying more attention to more complex issues, such as perceptions, beliefs, power, and politics. The theory also distinguishes implementation types (based on what change must take place) and the strategy that should be used. Another aspect of this theory is the people involved in the changes and to what extent they can promote changes or contradict them. So, Krüger [ 49 ] argues that the basis for change is directly related to the management of perceptions, beliefs, power, and politics. If managers understand how this is related to the creation of obstacles, according to the author, they will be able to better implement the changes that they want to perform in their organizations.

It is not enough to analyze only a single component of management culture without evaluation of the entirety. Management culture analysis and changes require a systematic approach, on the basis of which management culture system is presented in the research and its diagnostics is carried out. Having discussed the management culture through formal and informal organizational culture elements, it is appropriate to introduce imputed corporate social responsibility development stages. Figure 4 presents the corporate social responsibility implementation guidelines and corporate social responsibility application plan [ 52 ], together with the supplements of the authors of the book that extend implementation guidelines identified in the plan for the preparation aiming for corporate social responsibility establishment and management system evaluation, which are significant in further process of corporate social responsibility implementation.

what is the importance of research philosophy

Figure 4.

Research philosophy: the main aspects of the research. Source: Adapted by the authors according to Flowers [ 53 ].

Although the plan recommended by Ruževičius [ 52 ] is meant for the companies managed by the public sector, it is estimated that it was prepared in accordance with standards applied in companies operating in the free market, regardless of the origin of the capital. Control system evaluation, which is associated with the previously discussed management culture, is an important process chain because the volume of resource use, cost amounts, and timing as well as ultimate effect depend on its functionality. In addition, it is proposed to assess the possibility of the organization's retreat from corporate social responsibility (shareholders’ change, company restructuring, economic conditions and other relevant circumstances, changes influencing decisions), but it could be part of separate research that this study does not develop.

The research position . Guba and Lincoln [ 3 ] pointed out that the fragmentation of paradigm differences can occur only when there is a new paradigm which is more sophisticated than the existing ones. It is most likely, according to the authors, “if and when the proponents of different approaches meet to discuss the differences rather than argue about their opinion holiness.” All supporters’ dialogue with each other will provide an opportunity to move toward congenial (like-minded) relations. In this research, considering its versatility, one strictly defined position is not complied with. There is compliance with the principle of positivism when a scientist is an objective analyst, isolates himself from personal values, and works independently; in addition, thought and access freedom provided by pragmatism philosophical system is evaluated. Figure 4 summarizes the main elements of the study. The main aim of the research presented in this book is to define the management culture development level which creates an opportunity for organizations to pursue the implementation of corporate social responsibility. The analysis has shown that there is a lack of theoretical insights and empirical research, systematically linking management culture and corporate social responsibility aspects; still this work is not intended to cast a new challenge to already existing theories, but they are connected.

When preparing the research, it was based on academic literature and the insights of experts by using the original questionnaires made by the authors. The employees of two groups of companies, having different socio-demographic characteristics, occupying different positions in organizations are interviewed, and the data obtained are analyzed statistically and interpreted. In this study, the reliability of a specially developed research instrument is argued, and the main focus is on the factors of management culture that influences the implementation of corporate social responsibility at organizational level, as well as evaluating the corporate staff reactions and participation in processes. During the interviews with managers, the management culture as a formal expression of the organizational culture aiming at implementation of corporate social responsibility is revealed.

In this book, great attention is paid to statistical verification of instruments and model in order to be able to make recommendations to the organization management practitioners.

Philosophy of expert evaluation is based on the increasing demand of the versatility of the compiled instrument, and its content suitability for distinguished scales and subscales. The target of this research is to determine the surplus statements, not giving enough necessary information, as well as setting the statements where the content information not only verifies the honesty of the respondent, but also obviously reiterates. Philosophy of expert assessment is based on the research instrument content quality assurance, so that it would consist of statements, revealing in detail the research phenomena and enabling the achievement of the set goal of the research.

The philosophy of expert evaluation is based on the need to increase the versatility of the compiled instrument and its content suitability for derived scales and subscales. This research aims to determine the methodological and psychometric characteristics of the questionnaire with respect to a relatively small sample size, representing the situation of one organization. After eliminating the documented shortcomings during the exploratory research, the aim is to prepare an instrument featuring high methodological and psychometric characteristics, suitable for further research analyzing the cases of different sample sizes and different organizations.

The basic (quantitative and qualitative) research philosophy is based on perception of research data significance, importance for the public, and the principle of objectivity. In order to minimize subjectivity and guarantee reliability and the possibility of further discussions, quantitative research findings are based on conclusion (statistical generalization) and qualitative contextual understanding (analytic generalization). Both research results are presented in detail, openly showing the research organization and implementation process.

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  • 32. Hatch MJ, Cunlife AL. Organization Theory: Modern, Symbolic, and Postmodern Perspectives. 2nd ed. UK: Oxford University Press; 2006. 370 p. ISBN 9780199260218, ISBN 0199260214
  • 33. Denzin NK, Lincoln YS. Entering the field of qualitative research. In: Denzin NK, Lincoln YS, editors. Collecting and Interpreting Qualitative Materials. Thousand Oaks, CA: Sage; 1998. p. 1-34
  • 34. Sale JEM, Lohfeld LH, Brazil K. Revisiting the quantitative-qualitative debate: Implications for mixed-methods research. Quality and Quantity. 2002; 36 (1):43-53. DOI: 10.1023/A:1014301607592
  • 35. Šaulauskas MP. Moderniosios epistemologijos solipsizmas ex principio interno. Metafilosofinis agneologijos svarstymas [Metaphilosophy of modern agneological epistemology: solipsism ex principio interno]. Problemos [Problems]. 2012; 81 :33-43 [in Lithuanian]
  • 36. Cooper DR, Schindler PS. Business Research Method. 12th ed. Boston: McGraw-Hill Education; 2013. p. 692
  • 37. Kirtiklis K. Ar filosofijai tebėra vietos socialiniuose moksluose? [Is there place for philosophy in social sciences?]. Sociologija. Mintis ir veiksmas [Sociology. Thought and Action]. 2010; 1 (26):69-81 [in Lithuanian]
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Dissertations 4: methodology: introduction & philosophy.

  • Introduction & Philosophy
  • Methodology

Introduction

The methodology introduction is a paragraph that describes both the design of the study and the organization of the chapter. This prepares the reader for what is to follow and provides a framework within which to incorporate the materials. 

This paragraph says to the reader, “This is the methodology chapter, this is how it is organized, and this is the type of design I used.” 

In this introduction, you can also state:  

The objectives of your research and/or 

The research question or hypothesis to be tested 

Research Philosophy

Carrying out your own research for your dissertation means that you are engaging in the creation of knowledge. Research philosophy is an aspect of this. It is belief about the way studies should be conducted, how data should be collected and how it is then analysed and used.  At its deepest level, it includes considerations of what is (ontology), like, is there an objective truth or is it everything subjective, and how to know (epistemology), like, can we know the truth, and how can we get to know it.

Writing about your research philosophy, therefore, involves reflecting on your assumptions and beliefs about data collection to develop, analyse, challenge and evaluate them.  

If you need to have a research philosophy section in your dissertation, the handout attached below provides some guidance.  

  • Research Philosophies Offers descriptions of different research philosophies
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Research Philosophy: Importance and Types Research Paper

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The Importance of Research Philosophy

The research paradigm of epistemology, the research paradigm of ontology, the interplay between epistemology and ontology, epistemological interpretivism in qualitative research, reference list.

Research philosophy occupies a significant place in the field of science and education. In general, philosophy deals with the “study of knowledge, reality and existence” (Moon et al ., 2018, p. 296). When concerning the realm of research, the philosophical approach determines the very direction of a scholar’s thought, thus attributing his or her findings to a particular branch of science. In other words, a type of research one chooses when using specific philosophical thinking predetermines the overall theoretical framework, results and contributions of a study (Moon et al ., 2018).

According to Dougherty and Slevc (2019), the identification of one’s research philosophy when participating in scientific research is vital because it clearly articulates the goals and estimated outcomes of a study, as well as the perspectives for its evaluation. The choice of a philosophical approach, such as objectivism, constructivism or others, defines a set of specific categories, sources of data or dissemination goals (Dougherty and Slevc, 2019). Therefore, the application of research philosophy is critical in scientific activity since it provides a concise theoretical platform for a study.

Epistemology is a branch of research philosophy that is aimed at studying the essence of knowledge and scientific facts. As Kivunja and Kuyini (2017) state, this branch enables describing how the knowledge appeared, what forms it has, and how it impacts the world. Epistemology helps to interpret the investigated question in the appropriate context by establishing logical explanations. When choosing this research paradigm, a scholar might retrieve knowledge from such sources as intuitive, authoritative, logical and empirical knowledge (Kivunja and Kuyini, 2017). The character of a study will differ depending on the source.

Therefore, there exist such branches of epistemology as interpretivism and positivism that apply to social sciences. Interpretivism is broadly used in qualitative studies and is based on the idea that any scientific finding should be interpreted within a social context and cannot be quantitatively measured (Gichuru, 2017). Interpretivists argue that their research problems are inherent in human nature and should be investigated accordingly.

On the other hand, positivism is a paradigm that employs the similarities between the natural and social domains to explain the investigated knowledge (Gichuru, 2017). More specifically, it applies the methods used in natural sciences to research the issues of the social domain (Eketu, 2017). Positivism interprets given knowledge only from a scientific perspective relying on empirical data.

Ontology is a philosophical research paradigm that investigates the nature of being. The reality is viewed from the perspective of an individual, and the knowledge is researched via the lens of “physical and ecological systems” of the world that is inhabited by individuals who have their values (McManus et al ., 2017, p. 4). Ontology concentrates on the “categories of things that exist and their relations” as perceived by a researcher (Kivunja and Kuyini, 2017, p. 27). According to this paradigm, a scholar makes particular assumptions about the specific issues under investigation.

There exist several ontological approaches, including objectivism and constructivism. Objectivism deals with the researched phenomena from the point of view that a researcher is external to the investigated problem and might evaluate it objectively (McManus et al ., 2017; Ragab and Arisha, 2018). Objectivists think that all researched phenomena might be viewed as empirical units and might be easily measured. Therefore, this methodology is vastly used in quantitative studies. At the same time, constructivism is defined by a set of different assumptions based on people’s experiences and interactions with the world (Moon et al ., 2018).

From this perspective, the research phenomena are viewed as social constructions (Hay, 2016; Pernecky, 2016). By perceiving reality through the perspective of the human mind, constructivism aims at understanding the essence of being and is usually applied to qualitative studies.

Due to the fact that both epistemology and ontology are the branches of research philosophy, they are linked and share some similar features. For example, objectivism and positivism are connected by the idea that the phenomena under study are measurable and might be evaluated objectively because the researcher is external to the researched problem (Ryan, 2018, Zukauskas, Vveinhardt and Andriukaitiene, 2018).

Also, constructivism and interpretivism are linked because they both investigate a problem or phenomena within a particular context taking into account multiple influential factors of it (Harrison et al ., 2017). Thus, there are significant similarities in the ontological and epistemological approaches that might be useful when choosing a research philosophy.

The most optimal paradigm of research philosophy for a qualitative study in social sciences is epistemological interpretivism. Firstly, the epistemological realm allows for analysing data by logically investigating the essence of knowledge and its forms, which might amplify the credibility of the research findings. Secondly, the interpretivist worldview provides an opportunity to put the phenomena into the social context and investigate them according to their relation to human nature. Therefore, epistemological interpretivism will ensure the best qualitative outcomes of the research.

Dougherty, M. R., Slevc, L. R. and Grand, J. A. (2019) ‘Making research evaluation more transparent: aligning research philosophy, institutional values, and reporting’, Perspectives on Psychological Science, 14(3), pp. 1-21.

Eketu, C. A. (2017) ‘Management research: a thought on validity of positivism’, International Journal of Advanced Academic Research , 3(11), pp. 133-139.

Gichuru, M. J. (2017) ‘ The interpretive research paradigm: a critical review of is research methodologies ’, International Journal of Innovative Research and Advanced Studies , 4(2), pp. 1-5. Webb.

Harrison, H. et al . (2017) ‘ Case study research: foundations and methodological orientations’ , Forum: Qualitative Social Research , 18(1). Web.

Hay, C. (2016) ‘Good in a crisis: the ontological institutionalism of social constructivism’, New Political Economy, 21(6), pp. 520-535.

Kivunja, C. and Kuyini, A. B. (2017) ‘Understanding and applying research paradigms in educational contexts’, International Journal of Higher Education, 6(5), pp. 26-41.

McManus, P. et al . (2017) ‘An investigation in the methodological approaches used in doctoral business research in Ireland’, ECRM 2017: 16th European Conference on Research Methodology for Business and Management Studies , Dublin, pp. 1-11.

Moon, K. et al . (2018) ‘Expanding the role of social science in conservation through an engagement with philosophy, methodology, and methods’, Methods in Ecology and Evolution, 10(3), pp. 294-302.

Pernecky, T. (2016) Epistemology and metaphysics for qualitative research. London: Sage.

Ragab, M. A. and Arisha, A. (2018) ‘Research methodology in business: a starter’s guide’, Management and Organisational Studies , 5(1), pp. 1-23.

Ryan, G. (2018) ‘Introduction to positivism, interpretivism and critical theory’, Nurse Researcher , 25(4), pp. 41-49.

Zukauskas, P., Vveinhardt, J. and Andriukaitiene, R. (2018) Management culture and corporate social responsibility. London: IntechOpen.

  • Ayer’s Key Argument Against Ethical Objectivism
  • Concept of Ontology in Philosophy
  • Social Environments: Subjectivism and Objectivism Relationship
  • Chapter VIII of Adam Smith’s “Wealth of Nations”
  • Xenophanes' Knowledge Theory in Fragment 10
  • Reasoning in Plato’s “Phaedo” Dialogue
  • Voltaire's "Candy in Hollyforest" in Modern America
  • Maurice Merleau-Ponty: How to Know Reality?
  • Chicago (A-D)
  • Chicago (N-B)

IvyPanda. (2021, July 24). Research Philosophy: Importance and Types. https://ivypanda.com/essays/research-philosophy-importance-and-types/

"Research Philosophy: Importance and Types." IvyPanda , 24 July 2021, ivypanda.com/essays/research-philosophy-importance-and-types/.

IvyPanda . (2021) 'Research Philosophy: Importance and Types'. 24 July.

IvyPanda . 2021. "Research Philosophy: Importance and Types." July 24, 2021. https://ivypanda.com/essays/research-philosophy-importance-and-types/.

1. IvyPanda . "Research Philosophy: Importance and Types." July 24, 2021. https://ivypanda.com/essays/research-philosophy-importance-and-types/.

Bibliography

IvyPanda . "Research Philosophy: Importance and Types." July 24, 2021. https://ivypanda.com/essays/research-philosophy-importance-and-types/.

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Research Philosophies and Approaches

By rev. prof jonathan edward tetteh kuwornu-adjaottor posted 19th june 2020 post views: 16,572.

E very research has a philosophy behind it and an approach or approaches for studying a phenomenon. What is philosophy? What are the broad philosophies underpinning research? In this article, I would address these questions.

What is Philosophy?

Philosophy, from the Greek ‘love of wisdom’ is difficult to define since it does not possess a specific object of inquiry. In a broad sense, philosophy is concerned with fundamental problems that arise in every area of human thought and activity, and which cannot be resolved by a specific method. Thus, philosophy is an activity of human reasoning about problems that come up in the world of humans. If research is a study of a phenomenon with a view of understanding it, then there is a relationship between research and philosophy – they both seek to understand problems, but understanding alone is not enough; research acts after understanding to solve problems; thereby making the world a better place to live in.

What Philosophies Underpin Research?

There are three philosophies behind research – positivism, post-positivism and pragmatism.

Positivism as an epistemology (a way of knowing how knowledge is derived and how it is to be validated) is based on the idea that science is the only way to learn about the truth. The positivist determines truth  a priori  (a Latin term meaning, ‘from what comes before’.  And a priori  proposition is one that is known to be true or false, without reference to experience). As a philosophy, positivism adheres to the view that only ‘factual’ knowledge gained through observation (the senses), including measurement, is trustworthy. This school of thought posits that the researcher is limited to data collection and interpretation in a subjective way; and that, research findings are usually observable and quantifiable. In the positivism paradigm, the researcher is independent of the study and there are no provisions for human interests within the study; he or she depends on facts to deduce results from the research. Positivism is applied mainly in Basic Science in which experiments are used to discern natural laws through direct manipulation and observation. So to the positivist, research is subjective; the findings must be subjected to natural laws and principles to make them valid. Positivism employs the Quantitative approach to research which is much more numbers-driven. The emphasis is on the collection of numerical data that can be studied and categorized into frequencies and described in percentages and other descriptive statistical methods such as mode and mean charts and graphs. The conclusion then makes inferences based on that data.

  • Post-Positivism

Post-positivism is a rejection of the central tenets of positivism. Post-positivists are constructivists who believe that we each construct our view of the world based on our perceptions. A post-positivist determines truth  a posteriori  (a Latin term meaning ‘from what comes after’.  A posteriori  propositions are true or false in relation to known established facts of experience).    The epistemology of the post-positivist is that; truth can be known objectively by recognizing the possible effects of biases. The post-positivists postulate that theories, background knowledge and values of the researcher can influence what is observed. They are of the view that research is objective; the findings are open-ended because they could be influenced by a number of factors, including the biases of the researcher. Finding of research on a topic may differ from one researcher to the other because they see reality from different perspectives. Post-positivists use their thought in applied research. Most of the research in the Social Sciences is Applied Research. In other words, the research techniques, procedures and methods that form the body of the methodology are applied to find solutions to practical problems and develop innovative technologies, rather than to acquire knowledge for knowledge’s sake. The post-positivist considers both quantitative and qualitative methods as valid approaches to research. The Qualitative research approach is descriptive in nature because it deals with non-numerical and unquantifiable things. The research might involve some numerical data in that the researcher would document the number of observations; however, the observations themselves would be descriptive of what the animals do.

Pragmatism is derived from the Greek word  pragma,  meaning action. Pragmatism is a deconstructive philosophy in which truth is not seen as an absolute but a moveable and usable construct for understanding the reality of nature. The pragmatists’ epistemology is that truth is ‘what works’ rather than what might be considered absolutely and objectively ‘true’ or ‘real’. The pragmatists hold the view that there are many different ways of interpreting the world and that in conducting research, no single point of view can ever give the entire picture because there may be multiple realities. In terms of research, the pragmatists integrate multiple approaches and strategies such as Qualitative, Quantitative and Action research methods within the same study. Action research is an approach applied in the Social Sciences. Action research goes through a cycle – planning, acting, observing, reflecting, planning and then reporting the findings.

Are you a researcher or an upcoming researcher? You may want to try your hands on the following questions.

  • In what sense can you say that research findings are not absolute truths?
  • Propose a research topic in your field of study and determine which methodological approach you would prefer to use for the study? Justify your choice of approach.

Bibliography

Crowther, D.  & Lancaster G.,  Research Methods: A Concise Introduction to Research Management and Business Consultancy  (Butterworth: Heinemann, 2008).

Kumar, R.,  Research Methodology: A Step-by-Step Guide for Beginners  3 rd  Edition (London: SAGE Publications, 2011).

The Oxford Dictionary of Philosophy  3 rd  Edition (Oxford: Oxford University Press, 2016).

This article is published with the kind courtesy of the author – Prof.  Jonathan Edward Tetteh Kuwornu-Adjaottor. He is an Associate Professor of New Testament and Mother Tongue Biblical Hermeneutics in the Department of Religious Studies, Faculty of Social Sciences, College of Humanities and Social Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

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Research-Methodology

Interpretivism (interpretivist) Research Philosophy

Interpretivism, also known as interpretivist involves researchers to interpret elements of the study, thus interpretivism integrates human interest into a study. Accordingly, “interpretive researchers assume that access to reality (given or socially constructed) is only through social constructions such as language, consciousness, shared meanings, and instruments”. [1] Development of interpretivist philosophy is based on the critique of  positivism  in social sciences. Accordingly, this philosophy emphasizes qualitative analysis over quantitative analysis.

Interpretivism is “associated with the philosophical position of idealism, and is used to group together diverse approaches, including social constructivism , phenomenology and hermeneutics; approaches that reject the objectivist view that meaning resides within the world independently of consciousness” [2] . According to interpretivist approach, it is important for the researcher as a social actor to appreciate differences between people. [3]  Moreover, interpretivism studies usually focus on meaning and may employ multiple methods in order to reflect different aspects of the issue.

Interpretivism Research Philosophy

Important Aspects of Interpretivism

Interpretivist approach is based on naturalistic approach of data collection such as interviews and observations . Secondary data research is also popular with interpretivism philosophy. In this type of studies, meanings emerge usually towards the end of the research process.

The most noteworthy variations of interpretivism include the following:

  • Hermeneutics refers to the philosophy of interpretation and understanding. Hermeneutics mainly focuses on biblical texts and wisdom literature and as such, has a little relevance to business studies.
  • Phenomenology is “the philosophical tradition that seeks to understand the world through directly experiencing the phenomena”. [4]
  • Symbolic interactionism accepts symbols as culturally derived social objects having shared meanings. According to symbolic interactionism symbols provide the means by which reality is constructed

In general interpretivist approach is based on the following beliefs:

1. Relativist ontology .  This approach perceives reality as intersubjectively that is based on meanings and understandings on social and experiential levels.

2. Transactional or subjectivist epistemology.  According to this approach, people cannot be separated from their knowledge; therefore there is a clear link between the researcher and research subject.

The basic differences between positivism and interpretivism are illustrated by Pizam and Mansfeld (2009) in the following manner:

Assumptions and research philosophies

The use of interpretivism approach in business studies involves the following principles as suggested by Klein and Myers (1999)

  • The Fundamental Principle of the Hermeneutic Circle.
  • The Principle of Contextualization
  • The Principle of Interaction between the Researchers and the Subjects
  • The Principle of Abstraction and  Generalization
  • The Principle of Dialogical Reasoning
  • The Principle of Multiple Interpretations
  • The Principle of Suspicion

Advantages and Disadvantages of Interpretivism

Main disadvantages associated with interpretivism relate to subjective nature of this approach and great room for bias on behalf of researcher. Primary data generated in interpretivist studies cannot be generalized since data is heavily impacted by personal viewpoint and values. Therefore, reliability and representativeness of data is undermined to a certain extent as well.

On the positive side, thanks to adoption of interpretivism, qualitative research areas such as cross-cultural differences in organizations, issues of ethics, leadership and analysis of factors impacting leadership etc. can be studied in a great level of depth. Primary data generated via Interpretivism studies might be associated with a high level of validity because data in such studies tends to be trustworthy and honest.

Generally, if you are following interpretivism research philosophy in your dissertation the depth of discussion of research philosophy depends on the level of your studies. For a dissertation at Bachelor’s level it suffices to specify that you are following Interpretivism approach and to describe the essence of this approach in a short paragraph. For a dissertation at Master’s level discussion needs to be expanded into 2-3 paragraphs to include justification of your choice for interpretivist approach.

At a PhD level, on the other hand, discussion of research philosophy can cover several pages and you are expected to discuss the essence of interpretivism by referring to several relevant secondary data sources. Your justification for the selection of interpretivism need to be offered in a succinct way in about two paragraphs.

My e-book,   The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance contains discussions of theory and application of research philosophy. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  research design ,  methods of data collection  and  data analysis  are explained in this e-book in simple words.

John Dudovskiy

Interpretivism Research Philosophy

[1] Myers, M.D. (2008) “Qualitative Research in Business & Management” SAGE Publications

[2] Collins, H. (2010) “Creative Research: The Theory and Practice of Research for the Creative Industries” AVA Publications

[3] Source: Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

[4] Littlejohn, S.W. & Foss, K.A. (2009) “Encyclopedia of Communication Theory” Vol.1, SAGE Publication

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

  • 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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  • Blackmun opinion , in Daubert v. Merrell Dow Pharmaceuticals (92–102), 509 U.S. 579 (1993).
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What is the importance of scientific research in daily life?

What is the importance of scientific research in daily life?

The latest research indicates the breadth of opportunities available in the UK for students from larger countries such as China and India Chemotherapy, surfing the internet, forecasting hurricanes and storms.

What do these things have in common? All of them showed the importance of research in everyday life. We wouldn't be able to do it today without decades of trial and error.

ما هي أهمية البحث العلمي في الحياة اليومية؟ STUDYSHOOT

The importance of scientific research in higher education

Scientific research is not only a tool for solving current problems, it is also an integral part of higher education today. Universities and academic institutions consider scientific research a fundamental focus of their educational programs, as they encourage students to think critically, analyze and explore.

This approach enhances students' ability to interact with the world around them and understand the complex challenges they face.

Research-focused higher education provides students with the opportunity to acquire new skills and broaden their intellectual horizons. By engaging in research projects, students can apply what they learn in the classroom to real-world problems, enhancing their educational experience and making it more comprehensive and effective.

Scientific research plays a crucial role in improving the quality of life, solving modern problems and providing distinguished higher education. Through innovation and discovery, scientific research opens new horizons for humanity and contributes to building a better and more sustainable future. Our investment in scientific research is an investment in our future and the future of future generations.

Scientific research provides us with knowledge

ما هي أهمية البحث العلمي في الحياة اليومية؟ STUDYSHOOT

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what is the importance of research philosophy

Ensuring High-Quality Jobs is as Important as Ethical Food in Local Food Systems

L ocal food systems are increasingly proposed as a worthwhile alternative to the wider global food industry, with a emphasized distinction in their philosophy and operations.

While the admirable aspects and some advantages of local food systems, supported by research and evidence , are numerous, including closer ties between consumers and suppliers , promoting consumption of fresh and healthy food , invigorating local economies, and championing small-scale producers, critics also point to notable downsides.

One misunderstood notion is the “ local trap ,” which mistakenly equates local as automatically being more environmentally friendly, fair, or ethical.

Compromised Salaries and Exploitative Work Practices

Challenging the commonly held belief that local food systems inherently employ better practices than industrial systems, recent research brings to light the presence of low paying and underpaid jobs in these alternative food networks across the U.S.

The inherent criticism against conventional systems not reflecting the “ real cost ” of production also falls short if local systems rely on similarly inadequate salaries.

Migrant labor remains a convoluted topic in North America, with the U.S. making significant advances through the 1983 Migrant and Seasonal Agricultural Worker Protection Act , despite continuing issues in labor laws that ignore essential rights for farm workers.

Canada, on the other hand, with its restrictive work permits for temporary agricultural workers, has come under fire, being described as a hotbed for “contemporary forms of slavery” by a U.N. Special Rapporteur .

Worryingly, the local food sector is also increasingly dependent on migrant labor, with a tendency to overlook the involvement of these workers in marketing narratives, raising questions about the true locality of the products.

Dependency on Voluntary Labor

The reality for many small-scale and labor-intensive local farms is a heavy reliance on volunteer labor for sustainability.

Economist Carole Biewener, through her analysis , questions the point at which volunteerism crosses into exploitation, particularly when individuals perform critical roles without appropriate compensation or learning opportunities.

The reliance on unpaid labor raises fundamental concerns about the long-term viability and ethical standing of these farming practices.

Family Labor Dynamics

Farmers themselves, who are typically overworked and underpaid despite finding fulfillment in local food systems, sometimes have to seek additional employment to survive and maintain their farms, which can be seen as a form of self-exploitation .

It is also disturbing to note the prevalence of child labor in agriculture, a problem receiving little attention within the alternative food movement, except for isolated studies such as the examination of perceptions in Illinois and North Carolina of the safety on small, local farms.

Gender Inequalities

While local food systems have attracted more women farmers than conventional agriculture, assorted obstacles rooted in gender bias, work-family balance issues, and financial disparities continue to challenge their involvement.

Consumer gender dynamics within local markets, often female-dominated, also run the risk of perpetuating a “third shift” scenario, adding to the existing professional and domestic burdens of women.

Promoting Improvements in Local Food Systems

While local food systems set their sights on sustainability and ethical production methodologies, facing up to labor issues is crucial for all participants.

In spite of consumers’ willingness to support local farming practices by paying greater prices, these costs often do not fully acknowledge the true expense of production, which should include a living wage for farmers and laborers.

As highlighted by scholars, achieving sustainable and ethically produced food must encompass job quality and fairness, guiding local food systems towards a just and sustainable future.

Stevens Azima , Research Professional, Université Laval

This content is sourced from The Conversation and is shared under a Creative Commons license. Originally published at The Conversation .

What is the “local trap” in local food systems?

The “local trap” is a misconception that local food systems are inherently more sustainable, just, or ethical simply because they are local. It overlooks the complexities and potential issues within these systems.

Why are job salaries an issue in local food systems?

Local food systems often offer underpaid positions, failing to address the “true cost” of sustainable food production which includes fair wages for workers.

How do local food systems rely on migrant labor?

Local food systems in North America sometimes employ migrant workers on terms that do not provide basic labor rights or pathways to permanent residency – an issue similar to that found in larger, global food systems.

How prevalent is unpaid labor in local food systems?

Many local food farms depend on unpaid labor from family members, volunteers, and interns. This raises questions about the exploitation of labor and the long-term sustainability of these practices.

What are the gender dynamics in local food systems?

Local food systems tend to perpetuate gender-specific challenges, such as stereotypes that exclude women as farmers, disproportionate care duties, and economic disparities, all of which can limit the involvement of women in these systems.

veggies

The four building blocks of change

Large-scale organizational change has always been difficult, and there’s no shortage of research showing that a majority of transformations continue to fail. Today’s dynamic environment adds an extra level of urgency and complexity. Companies must increasingly react to sudden shifts in the marketplace, to other external shocks, and to the imperatives of new business models. The stakes are higher than ever.

So what’s to be done? In both research and practice, we find that transformations stand the best chance of success when they focus on four key actions to change mind-sets and behavior: fostering understanding and conviction, reinforcing changes through formal mechanisms, developing talent and skills, and role modeling. Collectively labeled the “influence model,” these ideas were introduced more than a dozen years ago in a McKinsey Quarterly article, “ The psychology of change management .” They were based on academic research and practical experience—what we saw worked and what didn’t.

Digital technologies and the changing nature of the workforce have created new opportunities and challenges for the influence model (for more on the relationship between those trends and the model, see this article’s companion, “ Winning hearts and minds in the 21st century ”). But it still works overall, a decade and a half later (exhibit). In a recent McKinsey Global Survey, we examined successful transformations and found that they were nearly eight times more likely to use all four actions as opposed to just one. 1 1. See “ The science of organizational transformations ,” September 2015. Building both on classic and new academic research, the present article supplies a primer on the model and its four building blocks: what they are, how they work, and why they matter.

Fostering understanding and conviction

We know from research that human beings strive for congruence between their beliefs and their actions and experience dissonance when these are misaligned. Believing in the “why” behind a change can therefore inspire people to change their behavior. In practice, however, we find that many transformation leaders falsely assume that the “why” is clear to the broader organization and consequently fail to spend enough time communicating the rationale behind change efforts.

This common pitfall is predictable. Research shows that people frequently overestimate the extent to which others share their own attitudes, beliefs, and opinions—a tendency known as the false-consensus effect. Studies also highlight another contributing phenomenon, the “curse of knowledge”: people find it difficult to imagine that others don’t know something that they themselves do know. To illustrate this tendency, a Stanford study asked participants to tap out the rhythms of well-known songs and predict the likelihood that others would guess what they were. The tappers predicted that the listeners would identify half of the songs correctly; in reality, they did so less than 5 percent of the time. 2 2. Chip Heath and Dan Heath, “The curse of knowledge,” Harvard Business Review , December 2006, Volume 8, Number 6, hbr.org.

Therefore, in times of transformation, we recommend that leaders develop a change story that helps all stakeholders understand where the company is headed, why it is changing, and why this change is important. Building in a feedback loop to sense how the story is being received is also useful. These change stories not only help get out the message but also, recent research finds, serve as an effective influencing tool. Stories are particularly effective in selling brands. 3 3. Harrison Monarth, “The irresistible power of storytelling as a strategic business tool,” Harvard Business Review , March 11, 2014, hbr.org.

Even 15 years ago, at the time of the original article, digital advances were starting to make employees feel involved in transformations, allowing them to participate in shaping the direction of their companies. In 2006, for example, IBM used its intranet to conduct two 72-hour “jam sessions” to engage employees, clients, and other stakeholders in an online debate about business opportunities. No fewer than 150,000 visitors attended from 104 countries and 67 different companies, and there were 46,000 posts. 4 4. Icons of Progress , “A global innovation jam,” ibm.com. As we explain in “Winning hearts and minds in the 21st century,” social and mobile technologies have since created a wide range of new opportunities to build the commitment of employees to change.

Reinforcing with formal mechanisms

Psychologists have long known that behavior often stems from direct association and reinforcement. Back in the 1920s, Ivan Pavlov’s classical conditioning research showed how the repeated association between two stimuli—the sound of a bell and the delivery of food—eventually led dogs to salivate upon hearing the bell alone. Researchers later extended this work on conditioning to humans, demonstrating how children could learn to fear a rat when it was associated with a loud noise. 5 5. John B. Watson and Rosalie Rayner, “Conditioned emotional reactions,” Journal of Experimental Psychology , 1920, Volume 3, Number 1, pp. 1–14. Of course, this conditioning isn’t limited to negative associations or to animals. The perfume industry recognizes how the mere scent of someone you love can induce feelings of love and longing.

Reinforcement can also be conscious, shaped by the expected rewards and punishments associated with specific forms of behavior. B. F. Skinner’s work on operant conditioning showed how pairing positive reinforcements such as food with desired behavior could be used, for example, to teach pigeons to play Ping-Pong. This concept, which isn’t hard to grasp, is deeply embedded in organizations. Many people who have had commissions-based sales jobs will understand the point—being paid more for working harder can sometimes be a strong incentive.

Despite the importance of reinforcement, organizations often fail to use it correctly. In a seminal paper “On the folly of rewarding A, while hoping for B,” management scholar Steven Kerr described numerous examples of organizational-reward systems that are misaligned with the desired behavior, which is therefore neglected. 6 6. Steven Kerr, “On the folly of rewarding A, while hoping for B,” Academy of Management Journal , 1975, Volume 18, Number 4, pp. 769–83. Some of the paper’s examples—such as the way university professors are rewarded for their research publications, while society expects them to be good teachers—are still relevant today. We ourselves have witnessed this phenomenon in a global refining organization facing market pressure. By squeezing maintenance expenditures and rewarding employees who cut them, the company in effect treated that part of the budget as a “super KPI.” Yet at the same time, its stated objective was reliable maintenance.

Even when organizations use money as a reinforcement correctly, they often delude themselves into thinking that it alone will suffice. Research examining the relationship between money and experienced happiness—moods and general well-being—suggests a law of diminishing returns. The relationship may disappear altogether after around $75,000, a much lower ceiling than most executives assume. 7 7. Belinda Luscombe, “Do we need $75,000 a year to be happy?” Time , September 6, 2010, time.com.

Would you like to learn more about our People & Organizational Performance Practice ?

Money isn’t the only motivator, of course. Victor Vroom’s classic research on expectancy theory explained how the tendency to behave in certain ways depends on the expectation that the effort will result in the desired kind of performance, that this performance will be rewarded, and that the reward will be desirable. 8 8. Victor Vroom, Work and motivation , New York: John Wiley, 1964. When a Middle Eastern telecommunications company recently examined performance drivers, it found that collaboration and purpose were more important than compensation (see “Ahead of the curve: The future of performance management,” forthcoming on McKinsey.com). The company therefore moved from awarding minor individual bonuses for performance to celebrating how specific teams made a real difference in the lives of their customers. This move increased motivation while also saving the organization millions.

How these reinforcements are delivered also matters. It has long been clear that predictability makes them less effective; intermittent reinforcement provides a more powerful hook, as slot-machine operators have learned to their advantage. Further, people react negatively if they feel that reinforcements aren’t distributed fairly. Research on equity theory describes how employees compare their job inputs and outcomes with reference-comparison targets, such as coworkers who have been promoted ahead of them or their own experiences at past jobs. 9 9. J. S. Adams, “Inequity in social exchanges,” Advances in Experimental Social Psychology , 1965, Volume 2, pp. 267–300. We therefore recommend that organizations neutralize compensation as a source of anxiety and instead focus on what really drives performance—such as collaboration and purpose, in the case of the Middle Eastern telecom company previously mentioned.

Developing talent and skills

Thankfully, you can teach an old dog new tricks. Human brains are not fixed; neuroscience research shows that they remain plastic well into adulthood. Illustrating this concept, scientific investigation has found that the brains of London taxi drivers, who spend years memorizing thousands of streets and local attractions, showed unique gray-matter volume differences in the hippocampus compared with the brains of other people. Research linked these differences to the taxi drivers’ extraordinary special knowledge. 10 10. Eleanor Maguire, Katherine Woollett, and Hugo Spires, “London taxi drivers and bus drivers: A structural MRI and neuropsychological analysis,” Hippocampus , 2006, Volume 16, pp. 1091–1101.

Despite an amazing ability to learn new things, human beings all too often lack insight into what they need to know but don’t. Biases, for example, can lead people to overlook their limitations and be overconfident of their abilities. Highlighting this point, studies have found that over 90 percent of US drivers rate themselves above average, nearly 70 percent of professors consider themselves in the top 25 percent for teaching ability, and 84 percent of Frenchmen believe they are above-average lovers. 11 11. The art of thinking clearly, “The overconfidence effect: Why you systematically overestimate your knowledge and abilities,” blog entry by Rolf Dobelli, June 11, 2013, psychologytoday.com. This self-serving bias can lead to blind spots, making people too confident about some of their abilities and unaware of what they need to learn. In the workplace, the “mum effect”—a proclivity to keep quiet about unpleasant, unfavorable messages—often compounds these self-serving tendencies. 12 12. Eliezer Yariv, “‘Mum effect’: Principals’ reluctance to submit negative feedback,” Journal of Managerial Psychology , 2006, Volume 21, Number 6, pp. 533–46.

Even when people overcome such biases and actually want to improve, they can handicap themselves by doubting their ability to change. Classic psychological research by Martin Seligman and his colleagues explained how animals and people can fall into a state of learned helplessness—passive acceptance and resignation that develops as a result of repeated exposure to negative events perceived as unavoidable. The researchers found that dogs exposed to unavoidable shocks gave up trying to escape and, when later given an opportunity to do so, stayed put and accepted the shocks as inevitable. 13 13. Martin Seligman and Steven Maier, “Failure to escape traumatic shock,” Journal of Experimental Psychology , 1967, Volume 74, Number 1, pp. 1–9. Like animals, people who believe that developing new skills won’t change a situation are more likely to be passive. You see this all around the economy—from employees who stop offering new ideas after earlier ones have been challenged to unemployed job seekers who give up looking for work after multiple rejections.

Instilling a sense of control and competence can promote an active effort to improve. As expectancy theory holds, people are more motivated to achieve their goals when they believe that greater individual effort will increase performance. 14 14. Victor Vroom, Work and motivation , New York: John Wiley, 1964. Fortunately, new technologies now give organizations more creative opportunities than ever to showcase examples of how that can actually happen.

Role modeling

Research tells us that role modeling occurs both unconsciously and consciously. Unconsciously, people often find themselves mimicking the emotions, behavior, speech patterns, expressions, and moods of others without even realizing that they are doing so. They also consciously align their own thinking and behavior with those of other people—to learn, to determine what’s right, and sometimes just to fit in.

While role modeling is commonly associated with high-power leaders such as Abraham Lincoln and Bill Gates, it isn’t limited to people in formal positions of authority. Smart organizations seeking to win their employees’ support for major transformation efforts recognize that key opinion leaders may exert more influence than CEOs. Nor is role modeling limited to individuals. Everyone has the power to model roles, and groups of people may exert the most powerful influence of all. Robert Cialdini, a well-respected professor of psychology and marketing, examined the power of “social proof”—a mental shortcut people use to judge what is correct by determining what others think is correct. No wonder TV shows have been using canned laughter for decades; believing that other people find a show funny makes us more likely to find it funny too.

Today’s increasingly connected digital world provides more opportunities than ever to share information about how others think and behave. Ever found yourself swayed by the number of positive reviews on Yelp? Or perceiving a Twitter user with a million followers as more reputable than one with only a dozen? You’re not imagining this. Users can now “buy followers” to help those users or their brands seem popular or even start trending.

The endurance of the influence model shouldn’t be surprising: powerful forces of human nature underlie it. More surprising, perhaps, is how often leaders still embark on large-scale change efforts without seriously focusing on building conviction or reinforcing it through formal mechanisms, the development of skills, and role modeling. While these priorities sound like common sense, it’s easy to miss one or more of them amid the maelstrom of activity that often accompanies significant changes in organizational direction. Leaders should address these building blocks systematically because, as research and experience demonstrate, all four together make a bigger impact.

Tessa Basford is a consultant in McKinsey’s Washington, DC, office; Bill Schaninger is a director in the Philadelphia office.

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Research Philosophy and Ethics

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what is the importance of research philosophy

  • Christian Hürlimann 5  

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Before discussing the applied methods in this research in chapter 4, including the philosophical assumptions about the MMR approach, the chosen philosophical stance, the underlying philosophical assumptions, and the role of the researcher’s value are discussed. The aim of this chapter is to outline the relevant parts of this topic for this research.

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Hürlimann, C. (2019). Research Philosophy and Ethics. In: Valuation of Renewable Energy Investments. Sustainable Management, Wertschöpfung und Effizienz. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-27469-6_3

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