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Organizing Your Social Sciences Research Paper

  • Quantitative Methods
  • Purpose of Guide
  • Design Flaws to Avoid
  • Independent and Dependent Variables
  • Glossary of Research Terms
  • Reading Research Effectively
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
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  • Theoretical Framework
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  • Evaluating Sources
  • Primary Sources
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  • Scholarly vs. Popular Publications
  • Qualitative Methods
  • Insiderness
  • Using Non-Textual Elements
  • Limitations of the Study
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism
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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

  • The data is usually gathered using structured research instruments.
  • The results are based on larger sample sizes that are representative of the population.
  • The research study can usually be replicated or repeated, given its high reliability.
  • Researcher has a clearly defined research question to which objective answers are sought.
  • All aspects of the study are carefully designed before data is collected.
  • Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
  • Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
  • Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

  • Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
  • Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
  • Explain the techniques you used to "clean" your data set.
  • Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
  • Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
  • When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
  • Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
  • Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
  • Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

  • Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
  • Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
  • Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

  • Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
  • Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
  • Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

  • Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

  • Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
  • Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
  • Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
  • Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

  • Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
  • Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
  • Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

  • Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
  • Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
  • Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
  • You can summarize vast sources of information and make comparisons across categories and over time; and,
  • Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

  • Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
  • Uses a static and rigid approach and so employs an inflexible process of discovery;
  • The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
  • Results provide less detail on behavior, attitudes, and motivation;
  • Researcher may collect a much narrower and sometimes superficial dataset;
  • Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
  • The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
  • Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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  • Published: 04 May 2013

Quantitative analysis of organizational culture in occupational health research: a theory-based validation in 30 workplaces of the organizational culture profile instrument

  • Alain Marchand 1 , 2 ,
  • Victor Y Haines III 1 , 2 &
  • Julie Dextras-Gauthier 1 , 2  

BMC Public Health volume  13 , Article number:  443 ( 2013 ) Cite this article

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This study advances a measurement approach for the study of organizational culture in population-based occupational health research, and tests how different organizational culture types are associated with psychological distress, depression, emotional exhaustion, and well-being.

Data were collected over a sample of 1,164 employees nested in 30 workplaces. Employees completed the 26-item OCP instrument. Psychological distress was measured with the General Health Questionnaire (12-item); depression with the Beck Depression Inventory (21-item); and emotional exhaustion with five items from the Maslach Burnout Inventory general survey. Exploratory factor analysis evaluated the dimensionality of the OCP scale. Multilevel regression models estimated workplace-level variations, and the contribution of organizational culture factors to mental health and well-being after controlling for gender, age, and living with a partner.

Exploratory factor analysis of OCP items revealed four factors explaining about 75% of the variance, and supported the structure of the Competing Values Framework. Factors were labeled Group, Hierarchical, Rational and Developmental. Cronbach’s alphas were high (0.82-0.89). Multilevel regression analysis suggested that the four culture types varied significantly between workplaces, and correlated with mental health and well-being outcomes. The Group culture type best distinguished between workplaces and had the strongest associations with the outcomes.


This study provides strong support for the use of the OCP scale for measuring organizational culture in population-based occupational health research in a way that is consistent with the Competing Values Framework. The Group organizational culture needs to be considered as a relevant factor in occupational health studies.

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The expansion of occupational health research to better account for a broader set of contextual factors that are associated with occupational stress and strain remains a significant challenge. Scholars in this field are paying more attention to workplace-level factors, but with an almost exclusive focus on specific psychosocial safety climate variables [ 1 , 2 ]. Notwithstanding such recent advances in the area of macro-organizational influences, the bulk of work stress research remains focused on task-level factors like psychological demands and job control [ 3 ], or effort and rewards [ 4 ]. Although this work design perspective has helped qualify a number of risk factors or stressors that may cause psychological strain and ill-health, it does not account for the broader organizational context in which work is performed.

For occupational health research to progress, researchers can no longer ignore contextual factors nor the functions of organizational culture in the stress process [ 5 ]. Organizational culture is formed through meaningful accumulated learning at the organizational level; experiences of success and failure that are retrieved and incorporated into the culture of an organization and shared by the members [ 6 ]. Considering that organizational culture is a meaningful social characteristic with potentially significant health consequences [ 5 , 7 – 10 ], occupational health researchers engaged in population-based investigations across levels of analysis will increasingly need to address the challenge of using a questionnaire measure of organizational culture in a manner that will allow replications and cross-sectional comparative studies within an accepted frame of reference.

Given the usefulness of quantitative measures of organizational culture [ 11 ] and the need to integrate this fundamental dimension of the workplace context into occupational stress research, the aim of this study is to advance a measurement approach for the study of organizational culture in population-based occupational health research, and to examine a relatively simple short-form questionnaire measure of organizational culture. In the context of complex multilevel occupational health research, other approaches that involve collecting qualitative data [ 12 , 13 ] or a multi-method conceptualization of organizational culture are considered difficult to apply. We will argue further on that a meritorious alternative for such research may be found either in the 26-item Organizational Culture Profile (OCP) survey instrument [ 14 ] or in a shorter form derived from this instrument.

A related challenge is to determine which facets or dimensions of organizational culture are most relevant to the study of work stress and occupational health. Drawing from the Competing Values Framework [ 15 , 16 ], meta-analytic evidence suggests a large significant positive relationship between the Group culture and job satisfaction, whereas the Developmental culture is more strongly positively associated with innovation [ 17 ]. Some types of organizational culture therefore appear to be more relevant than others depending upon the phenomenon being addressed by the research. Just as some culture types appear to be well suited to the study of organizational innovations [ 18 ] or patient satisfaction [ 19 ], or other outcomes of interest, other culture types might more applicable to the study of occupational health. This study will therefore seek to advance a measurement approach that is not only feasible, but also most relevant to occupational stress research. We will therefore seek to determine which culture types are most strongly related to employee mental health and well-being.

This study will therefore validate a measurement approach and test how different organizational culture types are associated with psychological distress, depression, emotional exhaustion, and well-being. By doing so, we hope to a) provide multi-level evidence of the associations between organizational culture and these outcomes of interest and b) offer some guidance to those researchers who wish to include organizational culture in their models and thereby capture some aspects of group dynamics [ 20 ]. In the following sections we describe the OCP survey instrument [ 14 ] and explain how the Competing Values Framework [ 15 , 21 ] might offer a valuable theoretical backdrop for assessing the construct validity of this measure. We then test the construct validity of the 26-item OCP questionnaire instrument with a sample of 1,164 employees nested in 30 workplaces. Next, we test associations between the dimensions of the OCP survey instrument and employee psychological distress, depression, emotional exhaustion and well-being. This theory-based measurement approach, which is clearly lacking in the analysis of OCP items [ 22 ], is intended to contribute to the advancement of occupational health research involving multilevel, population-based investigations that consider the meaningful facets of the organizational context in which stress occurs.

Organizational culture profile survey instrument

The Organizational Culture Profile (OCP) proposed by O’Reilly et al. (1991) is one of the most widely cited survey instruments in the organizational culture literature. At the time of writing these lines, their published paper was cited 2,431 times (Google Scholar). Although this scale was initially developed to assess person-organization fit [ 14 , 23 – 26 ], it holds much promise for population-based research seeking to model the effects of organizational culture on group- or individual-level phenomena. Nonetheless, possibly because of its initial focus, little is known about the aggregate-level properties of this measure. Only a few studies have analyzed these properties [ 27 – 29 ] and most of these were conducted in a single industrial sector with small samples of workplaces. Moreover, the approach has so far been inductive rather than theory-based.

Another issue related to the use of this survey instrument in population-based investigations relates to the number of items that it includes. The original measure had 54 items, but was reduced to 26 [ 14 ]. Subsequent studies applied either the longer or the shorter forms of the scale. Though the size of the longer scale makes it impractical for large scale population-based studies, the 26-item scale would benefit from further validation that would also review the group-level properties of the measure. A related concern is that factor analysis requires at least 10 to 20 subjects per item to achieve a reliable solution [ 30 ] and studies have failed to meet this requirement when using either the 54-item scale [ 31 – 34 ] or the 26-item scale [ 35 , 36 ]. These studies were characterized by overall small employee sample sizes.

There are also considerable inconsistencies in the conceptual structure of the OCP scale. Empirical analysis of the scale items initially generated seven factors labeled innovation, stability, respect to people, outcome orientation, attention to details, team orientation, and aggressiveness [ 14 ]. In subsequent studies, the number of factors was reported to be one [ 31 ], five [ 32 , 36 ], six [ 35 , 37 ], seven [ 27 – 29 , 34 , 38 ], and eight [ 33 ]. Factor loadings also varied considerably from one study to another as did the labeling of the factors. New factors like procedural [ 33 ], easygoing [ 33 , 38 ], supportive, humanistic, and task orientated [ 32 ], teamwork [ 35 ], and teamwork/respect for people [ 36 ] have shown up from one study to the next. These inconsistencies obviously limit the ability to study occupational health phenomena with a common frame of reference and thereby to accumulate a significant body of knowledge relating specific organizational culture types to psychological distress, depression, emotional exhaustion, and well-being.

Such conceptual and measurement challenges are not surprising given that those involved in developing the original scale were less interested in fitting its structure to some established theory or typology of organizational culture than they were in measuring individuals and organizations along commensurate items (i.e., values) for assessing person-organization fit. Without a guiding theoretical framework for the measurement of organizational culture, inconsistencies in its factorial structure might have been expected. Moreover, with significant advances in multilevel theory and research since the original OCP measure was conceived, considering the additional measurement issues that arise at higher levels of analysis [ 39 ], the need to establish its group-level properties could not be overstated; especially when one considers that the proper level of analysis for the study of organizational culture is the organization, unit, or workplace.

Competing values framework

The only modest theoretical anchor for the OCP scale is that it accepts the premise that values are central components of organizational culture. Founded on this same basic premise, different typologies of organizational culture were developed over time [ 40 – 43 ]; including the highly influential competing values framework [ 15 , 21 ]. Although it is not the only value theory with competing values, as exemplified by the Schwartz’s theory of universals in values [ 22 , 44 ], it is widely applied in studies that address the influence of organizational culture on workplace phenomena [ 45 – 51 ]. From the vantage point of this framework, the culture of an organization is defined along two bipolar dimensions or continuums. The first contrasts an internal with an external focus, thereby opposing integration and unity to differentiation and rivalry. The second opposes the search for stability, order and control to flexibility and change. According to their location on these two continuums, four types of organizational culture are described. The Group culture (internal – flexibility/change) favours employee participation, cooperation, mutual trust, team spirit, learning, fulfilling work through human resource development, trust in human potential, cohesiveness, and synergy. The Hierarchical organizational culture (internal – stability/order/control) is characterized by stability and continuity, information management, division of labour, efficiency, formal procedures, order, control, and rules and regulations. The Developmental culture (external – flexibility/change) relies upon environmental scanning, experimenting, innovating, organizational transformation through organic growth or market acquisitions, learning, creativity, adaptability, and growth. The Rational culture (external – stability/order/control) emphasizes decision rules, performance indicators, individual and collective accountabilities, reinforcement contingencies, production, and achieving goals and objectives.

With its widespread appeal and its focus on values, the Competing Values Framework offers an appropriate theoretical basis for assessing the construct validity of the OCP measure. Moreover, the culture types this framework describes appear like they may have some meaningful interpretations in occupational health research. Indeed, values such as “cooperation” and “mutual trust” that characterize the Group culture apparently reflect the extent or quality of relationships at the workplace level. Previously cited findings showing positive associations between the Group culture and attitudinal outcomes lend further support to the association between this culture type and employee well-being. Other arguments could be made to support the associations between other culture types and employee-level mental health and well-being outcomes [ 5 ].

Although this framework advances a measurement approach with the Organizational Culture Assessment Instrument (OCAI), application of this instrument in population based research comprising a diversity of workplaces and employees from different occupations or with varying education levels remains an unrealistic proposition. The OCAI includes six domains with four items to be assessed within each domain. Respondents are prompted to weight each item with a maximum of one hundred points per domain. This makes answering this instrument time consuming and unsuitable for people having low educational attainments as well as for questionnaire surveys that seek to measure several variables at a time.

Table  1 presents the principal values of the Competing Values Framework and the corresponding OCP scale items sorted by the authors according to the above-mentioned four culture types.

Assuming that culture varies from one workplace to another [ 52 ], there therefore appear to be close ties between the values assigned to the culture types of the Competing Values Framework and those included in the OCP survey instrument.

In sum, the literature has, on the one hand, been abuzz about organizational culture, but also relatively quiet when it comes to empirical demonstration of the construct in population-based multilevel research conducted in the area of occupational health. Even with a steady increase in the number of multilevel research models being tested in this area [ 1 , 53 , 54 ], there remains an ongoing and questionable disregard for organizational culture. The development of a questionnaire measure for the study of organizational culture in population-based occupational health research would offer a means for the advancement of such models.

Data were collected in 2009–2010 within 30 Canadian workplaces randomly selected from a list of over 500 companies insured by a large insurance company. Those companies randomly selected by the researchers were invited by their insurer to participate in this study and those workplaces that accepted were referred to the research team. The workplaces in our sample were quite diverse in terms of their products, services, and markets (e.g., motor manufacturing, software development, plumbing supplies, airport maintenance), with 18 in manufacturing and 12 in the service sector. Half (15) of the participating workplaces were unionized and they ranged in sizes from 9 to 391 employees, with an average of 96.9 workers per workplace. In each workplace, researchers first sent a communication to inform all employees about the research project. Then, a random sample of employees was selected and they were invited by the researchers to individually complete a questionnaire on company time using a touch-screen monitor. Consenting workers signed an informed consent and were given the necessary instructions. The questionnaire covered several aspects related to health and well-being, work, family, neighborhood, social networks, personality traits, and demographics. Questionnaire items related to the present study are provided as an Additional file. Overall, 1,164 employees agreed to participate in the survey, for a response rate of 72.5%. Workplace response rates ranged from 51.2% to 100%. On average, 38.8 of employees per workplace completed the questionnaire (minimum = 8; maximum = 136); 31.3% were female, the mean age was 39.3 ( SD =10.4), and 69.9% where living in a couple. After deleting cases with missing values, that available workers sample size was n=1153. The study protocol was approved by the Ethical Committees of the University of Montreal, McGill University, Laval University and Bishop’s University.

Organizational culture

All respondents completed the 26-item OCP scale [ 14 ] in either English or French. This scale includes as many values descriptive of organizational culture (e.g., fairness, risk taking). Respondents were prompted to indicate to what extent each of the values listed in the scale describes their organization on a unipolar rating scale ranging from not at all (1) to a great extent (5).

Mental health and well-being

Psychological distress was measured with the General Health Questionnaire (GHQ) short-form, 12-item scale [ 55 ] (alpha=0.85) (e.g., unable to concentrate on whatever you’re doing, feeling constantly under strain ), and depression with the Beck Depression Inventory (BDI) 21-item scale [ 56 ] (alpha=0.91) (e.g., feeling sad, suicide thoughts ). Using available cut-points for psychological and depression, 23.6% of workers were reporting feeling of distress and 5.7% were reporting moderate to severe symptoms of depression. Emotional exhaustion was assessed with five items from the Maslach Burnout Inventory (MBI) 16-item general survey [ 57 ] (alpha=0.90) (e.g., feeling emotionally drained from work, feeling used up at the end of the workday ), and well-being was measured with the five-item WHO Well-Being Index (WHO-5) [ 58 ] (alpha = 0.83) (e.g., feeling cheerful and in good spirits, feeling calm and relaxed ).

Control variables

Gender was measured as either male (0) or female (1). Age was measured in years and a value of 0 was assigned to respondents not living with a partner and of 1 if living with a partner (i.e., couple). Employment income was measured by asking each respondent to report his or her salary, before taxes and deductions for the past 12 months on a 10-point ordinal scale ranging from 1 = Less than $20,000 to 10 = $100,000 or more. Education level was assessed as the highest diploma obtained on a 10-point ordinal scale ranging from 1= No diploma to 10 = Doctoral. Working hours were measured by the number of hours spent on the job, and Work schedule irregularity was measured on a 4-point Likert-type scale (never/all the time) addressing the frequency of the respondent’s exposure to irregular or unpredictable schedules.

Exploratory factor analysis was conducted to assess the dimensionality of the OCP scale. Factors were extracted using the iterated principal-factor method on the correlation matrix. The number of retained factors was based on the Horn’s Parallel Analysis Test [ 59 ]. Oblique rotation with Kaiser Normalization was applied to extract factors loadings because the factors were expected to correlate as multiple perceptions of organizational culture may coexist within the same organization [ 40 , 60 – 62 ]. Scale reliabilities (Cronbach alphas) were computed with items retained from the rotated factor structure. Multilevel models, with workers (level-1) nested in their respective workplace (level-2), were estimated with restricted maximum likelihood to assess workplace-level variations. If organizational culture exists as a contextual component of workplaces, significant variations of rotated factors are expected at the workplace level. Intraclass correlations quantified the magnitude of these between workplace variations. Finally, multilevel regression models were estimated to evaluate the contribution of organizational culture factors to mental health and well-being after controlling for gender, age, and living with a partner. These three control variables were retained because they were routinely associated with the outcomes [ 63 – 65 ].

Table  2 presents the descriptive statistics and correlations for all the scale items. The correlation coefficients between the 26 OCP scale items ranged from -.20 to.74, with an average correlation of r =.28. It should be notes that the correlations between OCP15 and the other scale items were generally negative.

The rotated solution from the factor analysis is presented in Table  3 along with item Squared Multiple Correlation (SMC). Based on Horn’s Parallel Analysis Test, four factors are identified that together account for about 75% of the scale variance.

Low SMC coefficients are manifest for items OCP15 (.09), OCP25 (.15), and OCP26 (.19), suggesting a small contribution of these items to the factorial solution. Most items did, however, load as expected on their predicted dimensions (see Table  1 ) and the factor correlations support the assumption of correlated factors. It is also quite clear that there is a strong correspondence between the factorial structure obtained and the Competing Values Framework. Consistent with the expectation expressed in Table  1 , the first factor reflects the Group culture; the second, the Hierarchical culture; the third, the Developmental culture; and the fourth, the Rational culture. Only OCP13 and OCP15 deviate from their expected factors. OCP13 ( action oriented ) was expected to load on the Rational culture dimension, but the results indicate a better contribution to the Developmental culture. OCP15 ( not being constrained by many rules ) was expected to load on the Developmental culture; but the results indicate a better contribution to the Group culture. OCP13 was then included as an item in the Developmental culture and OC15 was added to the Group culture. However, because the factor loading of OCP15, OCP25 and OCP26 were low, they were removed in the following analysis. Factor reliabilities based on Cronbach’s alpha (α) are quite acceptable with an alpha of .89 for the Group culture, an alpha of .82 for both the Hierarchical and the Developmental culture types, and an alpha coefficient of .83 for the Rational culture.

The following analysis examined between-workplace variations in the Group, Hierarchical, Developmental, and Rational culture types. The results of the multilevel linear regression models are displayed in Table  4 .

These results show that the four types of organizational culture varied significantly between the 30 workplaces. Moreover, intraclass correlations (Rho) ranged from .03 to .14, expressing that 3% to 14% of the scale variance are between workplaces. The Group culture appears to show the larger differences across workplaces compared to the other culture types.

Finally, considering our interest in occupational health, we examined whether the four organizational culture types were associated with mental health and well-being outcomes. This can be considered a test of the nomological validity of the culture types generated from the above analyses. As a form of construct validity, nomological validity offers a test of the degree to which the dimensions of the construct behave as expected within a system of related constructs.

Before including variables in the models, a set of analysis were carried out to evaluate if the four outcomes varied between workplaces. Results revealed significant between workplace variations for psychological distress (χ 2 =26.51 df 1 p<.01; rho=0.02), depression (χ 2 =3.96 df 1 p<.05; rho=0.02), emotional exhaustion (χ 2 =27.74 df 1 p<.01; rho=0.05), and well-being (χ 2 =29.73 df 1 p<.01; rho=0.05), which supported the adequacy of the use of further multilevel modelling. After controlling for gender, age, marital status, employment income, education level, work hours per week and irregular work schedule, results in Table  5 indicate that the Group culture types is negatively associated with psychological distress, while the Rational culture type is associated with elevated higher level of psychological distress. The same pattern is observed for depression, with the exception of a non-significant association for the Developmental culture type. As for emotional exhaustion, the same variables play the same way as what it was observed for psychological distress. In the fourth regression model, Group and Hierarchical culture types are related to higher scores on well-being. Overall, the Group organizational culture type is consistently and more strongly associated with these mental health and well-being outcomes compared to the other types of organizational culture. With the exception of well-being, the Hierarchical culture appears to be poorly related to these outcomes. It would appear that the greatest contrasts in these patterns of results are between the Group and the Rational culture types. The Group culture is associated with positive health outcomes whereas the Rational culture is quite consistently associated with negative health outcomes.

As far as the covariates are concerned, gender and age are systematically related to the four outcomes, while being in couple is associated with psychological distress and depression. Employment income is unrelated to mental health and well-being, while education level is associated negatively with psychological distress and depression. The number of work hours per week is related to lower feelings of well-being, and having an irregular work schedule is associated with more feelings of depression and emotional exhaustion. Finally, Table  5 shows that psychological distress and depression do not vary across firms after including the organizational culture types and control variables, while emotional exhaustion and well-being still vary significantly between firms.

Although an extensive literature has developed on the topic of organizational culture, few studies were devoted to its measurement and to its association with workers mental health and well-being outcomes. This study assessed the construct validity and workplace-level properties of the 26-item OCP instrument [ 14 ] and its association with mental health and well-being outcomes. The findings support the proposition that the OCP measure can be conceptualized in terms of the four dimensions of the Competing Values Framework [ 15 , 21 ]. This validation also gives a clear cut decision rule for the number of factors derived from the OCP. The findings also show that the workplace-level properties of the OCP scale are adequate, suggesting that it could be applied at the group level in future multilevel studies. The associations between the culture types derived from this scale and mental-health and well-being outcomes further underscore the need to consider organizational culture in occupational health research.

With the exception of two items, the distribution of the OCP items according to organizational culture types is consistent with the Competing Values Framework. Three items (OCP15, OCP25, OCP26), however, had low loadings and were removed from the scale. Overall, seven items loaded on the group culture (OCP1-OCP4, OCP10-OCP12), six on hierarchical type (OCP19-OCP24), five on the developmental type (OCP13,OCP14, OCP16-OCP18), and five items loaded on the rational type of culture (OCP5-OCP9). Table 6 in Appendix 1 describes the final items distribution for each culture type. Furthermore, the four-factor model is responsive to the need for parsimony as well as the need for plausibility in the number of factors selected [ 66 ]. A solution with four common factors is obviously more parsimonious than models with seven or eight factors. Moreover, with reference to the Competing Values Framework, the four-factor model has a sufficient number of factors to account for the “major” factors that define organizational culture across companies and economic sectors [ 28 , 34 , 36 ]. Drawing from a heterogeneous sample of workplaces, our analysis suggests that four factors are sufficient to capture the essential aspects of organizational culture. This measurement approach has the added benefit of being theoretically grounded as it is associated with clear conceptual definitions of different culture types [ 15 , 21 ]. The Competing Values Framework seemingly provides clear conceptual definitions of the Group, Hierarchical, Developmental, and Rational culture types that contrast integration and unity to differentiation and rivalry, and that also oppose the search for stability, order and control to flexibility and change.

Our findings also support a measurement approach that is based on employee perceptions as they seemingly distinguish culture types between firms. This finding is important for advancing multilevel research that generally aggregates such perceptions to reflect unit-level phenomena. Because organizational culture is assumed to be a contextual, firm-level construct, then it must vary between firms or workplace. The results we obtained using multilevel regression modeling show significant variations of all four organizational culture types between workplaces. However, larger differences between workplaces were observed for the Group culture compared to the Hierarchical, Developmental, and Rational cultures. This may indicate that the Group culture is better suited for distinguishing of organizations compared to other types of organizational culture. For the other culture types, we did observe significant differences between workplaces but these differences were not as striking. Therefore, according to the results reported here, workplaces are not important determinants of hierarchical, developmental and rational organizational culture types.

After controlling for employment income, education level, number of work hours per week, and irregular work schedules, we found the Group organizational culture type to be consistently and moderately correlated with mental health and well-being outcomes. Overall, the Group organizational culture performs better at distinguishing organizations compared to other types of organizational culture and this dimension had the strongest associations with mental health and well-being outcomes. Such findings support the use of the Group organizational culture as a significant factor for the study of occupational health. Our results indicate that it can be adequately and conveniently measured with seven items from the OCP instrument. The internal consistency of the Group culture was high. Because the Group organizational culture puts a strong emphasis on human resources and promotes a supportive work environment, employees in organizations characterized by this culture type might experience less work stress that may explain why they seem to report fewer mental health problems and higher levels of well-being.

We therefore recommend that the seven-item Group culture scale be used in future population-based occupational health studies that operate across levels of analysis and that aim to offer a more complete account of the contextual factors that are directly or indirectly associated with occupational health outcomes. In a previous study, the Group culture was associated with employee satisfaction [ 17 ] and our findings underscore its relevance for the study of occupational health. Given that the aim of much occupational health research is to identify risk factors in the work context rather than sort workplaces according to culture types, this measurement approach may be the most responsive to the actual needs of researchers. By addressing the measurement challenge in such a way, scholars might therefore gain valuable insights into a meaningful contextual factor. Our findings further suggest that they may do so while avoiding the significant complexities of other measurement approaches that involve measuring a wider range of culture types.

This study has some limitations. First, this is a cross-sectional study which implies that the relationships observed cannot be interpreted causally and will need to be replicated longitudinally. It is possible that employees with low mental health described their organizational culture in negative terms because of their mental health status, and this may be important if a large number of employees had low mental health within a specific worksite. Second, it may be that evaluations of organizational culture are influenced by gender, age, nationality, seniority, education, and hierarchical level [ 52 ]. Women may notice some components and men other components of the culture in their organization. Older employees may notice a more traditional organizational culture and young workers may give more weight to items related to autonomy and social responsibility. In cross-cultural research, it is acknowledged that national cultures influence values, attitudes and beliefs [ 67 – 69 ]. Therefore, the number and types of organizational cultures may also vary from one country to another. Length of service in an organization could also influence perceptions of organizational culture as a result of the socialization process and the internalization of corporate values [ 14 , 70 ]. Further analysis is needed to clarify these possible confounding effects. Third, multilevel exploratory and confirmatory methods are available [ 71 ] to test within- and between-group factor models, but the sample of workplaces was too small in our study to perform these kinds of analysis with 26 items. Finally, the specific contribution of organizational culture to mental health and well-being will need to be evaluated in future studies against other workplace and individual factors also contributing to psychological distress, depression, emotional exhaustion, and well-being.

In conclusion, this study provides strong support for the use of the OCP scale for measuring organizational culture in population-based occupational health research in a way that is consistent with the Competing Values Framework. It addresses the need to validate instruments that capture at least some aspects of organizational culture [ 72 ]. In particular, the Group organizational culture, because of its capacity to distinguish between workplaces and its associations with mental health and well-being outcomes, needs to be considered as a relevant factor in occupational health studies. Multilevel research and policy could benefit from these findings in efforts to provide a more complete understanding of the influence of organizational culture on business strategy, work design, employee attitudes, behavior, health and well-being.

Table  6 of Appendix 1 presents the final distribution of the 23 retained OCP items according to culture types Group, Hierarchical, Developmental, and Rational.

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This study was supported by the Canadian Health Research Institutes and the Fonds de recherche du Québec-Santé. The authors also thank Standard Life Canada for their help in workplace recruitment assistance.

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Marchand, A., Haines, V.Y. & Dextras-Gauthier, J. Quantitative analysis of organizational culture in occupational health research: a theory-based validation in 30 workplaces of the organizational culture profile instrument. BMC Public Health 13 , 443 (2013). https://doi.org/10.1186/1471-2458-13-443

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  • v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.


Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

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Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.


Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

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Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

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Object name is IJA-60-662-g003.jpg

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

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where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

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where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

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where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

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where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

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Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

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Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

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Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

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If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

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Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

  • The assumption of normality which specifies that the means of the sample group are normally distributed
  • The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

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where X = sample mean, u = population mean and SE = standard error of mean

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where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

  • To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

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where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

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where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

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Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

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A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.


Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

  • StatPages.net – provides links to a number of online power calculators
  • G-Power – provides a downloadable power analysis program that runs under DOS
  • Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
  • SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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Library Home

A Quick Guide to Quantitative Research in the Social Sciences

(11 reviews)

organization of the study in quantitative research example

Christine Davies, Carmarthen, Wales

Copyright Year: 2020

Last Update: 2021

Publisher: University of Wales Trinity Saint David

Language: English

Formats Available

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Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 3/9/24

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers... read more

Comprehensiveness rating: 3 see less

The text provides a brief overview of quantitative research topics that is geared towards research in the fields of education, sociology, business, and nursing. The author acknowledges that the textbook is not a comprehensive resource but offers references to other resources that can be used to deepen the knowledge. The text does not include a glossary or index. The references in the figures for each chapter are not included in the reference section. It would be helpful to include those.

Content Accuracy rating: 4

Overall, the text is accurate. For example, Figure 1 on page 6 provides a clear overview of the research process. It includes general definitions of primary and secondary research. It would be helpful to include more details to explain some of the examples before they are presented. For instance, the example on page 5 was unclear how it pertains to the literature review section.

Relevance/Longevity rating: 4

In general, the text is relevant and up-to-date. The text includes many inferences of moving from qualitative to quantitative analysis. This was surprising to me as a quantitative researcher. The author mentions that moving from a qualitative to quantitative approach should only be done when needed. As a predominantly quantitative researcher, I would not advice those interested in transitioning to using a qualitative approach that qualitative research would enhance their research—not something that should only be done if you have to.

Clarity rating: 4

The text is written in a clear manner. It would be helpful to the reader if there was a description of the tables and figures in the text before they are presented.

Consistency rating: 4

The framework for each chapter and terminology used are consistent.

Modularity rating: 4

The text is clearly divided into sections within each chapter. Overall, the chapters are a similar brief length except for the chapter on data analysis, which is much more comprehensive than others.

Organization/Structure/Flow rating: 4

The topics in the text are presented in a clear and logical order. The order of the text follows the conventional research methodology in social sciences.

Interface rating: 5

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader.

Grammatical Errors rating: 3

There are some grammatical/typographical errors throughout. Of note, for Section 5 in the table of contents. “The” should be capitalized to start the title. In the title for Table 3, the “t” in typical should be capitalized.

Cultural Relevance rating: 4

The examples are culturally relevant. The text is geared towards learners in the UK, but examples are relevant for use in other countries (i.e., United States). I did not see any examples that may be considered culturally insensitive or offensive in any way.

I teach a course on research methods in a Bachelor of Science in Public Health program. I would consider using some of the text, particularly in the analysis chapter to supplement the current textbook in the future.

organization of the study in quantitative research example

Reviewed by Finn Bell, Assistant Professor, University of Michigan, Dearborn on 1/3/24

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary. read more

For it being a quick guide and only 26 pages, it is very comprehensive, but it does not include an index or glossary.

Content Accuracy rating: 5

As far as I can tell, the text is accurate, error-free and unbiased.

Relevance/Longevity rating: 5

This text is up-to-date, and given the content, unlikely to become obsolete any time soon.

Clarity rating: 5

The text is very clear and accessible.

Consistency rating: 5

The text is internally consistent.

Modularity rating: 5

Given how short the text is, it seems unnecessary to divide it into smaller readings, nonetheless, it is clearly labelled such that an instructor could do so.

Organization/Structure/Flow rating: 5

The text is well-organized and brings readers through basic quantitative methods in a logical, clear fashion.

Easy to navigate. Only one table that is split between pages, but not in a way that is confusing.

Grammatical Errors rating: 5

There were no noticeable grammatical errors.

The examples in this book don't give enough information to rate this effectively.

This text is truly a very quick guide at only 26 double-spaced pages. Nonetheless, Davies packs a lot of information on the basics of quantitative research methods into this text, in an engaging way with many examples of the concepts presented. This guide is more of a brief how-to that takes readers as far as how to select statistical tests. While it would be impossible to fully learn quantitative research from such a short text, of course, this resource provides a great introduction, overview, and refresher for program evaluation courses.

Reviewed by Shari Fedorowicz, Adjunct Professor, Bridgewater State University on 12/16/22

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing... read more

Comprehensiveness rating: 5 see less

The text is indeed a quick guide for utilizing quantitative research. Appropriate and effective examples and diagrams were used throughout the text. The author clearly differentiates between use of quantitative and qualitative research providing the reader with the ability to distinguish two terms that frequently get confused. In addition, links and outside resources are provided to deepen the understanding as an option for the reader. The use of these links, coupled with diagrams and examples make this text comprehensive.

The content is mostly accurate. Given that it is a quick guide, the author chose a good selection of which types of research designs to include. However, some are not provided. For example, correlational or cross-correlational research is omitted and is not discussed in Section 3, but is used as a statistical example in the last section.

Examples utilized were appropriate and associated with terms adding value to the learning. The tables that included differentiation between types of statistical tests along with a parametric/nonparametric table were useful and relevant.

The purpose to the text and how to use this guide book is stated clearly and is established up front. The author is also very clear regarding the skill level of the user. Adding to the clarity are the tables with terms, definitions, and examples to help the reader unpack the concepts. The content related to the terms was succinct, direct, and clear. Many times examples or figures were used to supplement the narrative.

The text is consistent throughout from contents to references. Within each section of the text, the introductory paragraph under each section provides a clear understanding regarding what will be discussed in each section. The layout is consistent for each section and easy to follow.

The contents are visible and address each section of the text. A total of seven sections, including a reference section, is in the contents. Each section is outlined by what will be discussed in the contents. In addition, within each section, a heading is provided to direct the reader to the subtopic under each section.

The text is well-organized and segues appropriately. I would have liked to have seen an introductory section giving a narrative overview of what is in each section. This would provide the reader with the ability to get a preliminary glimpse into each upcoming sections and topics that are covered.

The book was easy to navigate and well-organized. Examples are presented in one color, links in another and last, figures and tables. The visuals supplemented the reading and placed appropriately. This provides an opportunity for the reader to unpack the reading by use of visuals and examples.

No significant grammatical errors.

Cultural Relevance rating: 5

The text is not offensive or culturally insensitive. Examples were inclusive of various races, ethnicities, and backgrounds.

This quick guide is a beneficial text to assist in unpacking the learning related to quantitative statistics. I would use this book to complement my instruction and lessons, or use this book as a main text with supplemental statistical problems and formulas. References to statistical programs were appropriate and were useful. The text did exactly what was stated up front in that it is a direct guide to quantitative statistics. It is well-written and to the point with content areas easy to locate by topic.

Reviewed by Sarah Capello, Assistant Professor, Radford University on 1/18/22

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text. read more

Comprehensiveness rating: 4 see less

The text claims to provide "quick and simple advice on quantitative aspects of research in social sciences," which it does. There is no index or glossary, although vocabulary words are bolded and defined throughout the text.

The content is mostly accurate. I would have preferred a few nuances to be hashed out a bit further to avoid potential reader confusion or misunderstanding of the concepts presented.

The content is current; however, some of the references cited in the text are outdated. Newer editions of those texts exist.

The text is very accessible and readable for a variety of audiences. Key terms are well-defined.

There are no content discrepancies within the text. The author even uses similarly shaped graphics for recurring purposes throughout the text (e.g., arrow call outs for further reading, rectangle call outs for examples).

The content is chunked nicely by topics and sections. If it were used for a course, it would be easy to assign different sections of the text for homework, etc. without confusing the reader if the instructor chose to present the content in a different order.

The author follows the structure of the research process. The organization of the text is easy to follow and comprehend.

All of the supplementary images (e.g., tables and figures) were beneficial to the reader and enhanced the text.

There are no significant grammatical errors.

I did not find any culturally offensive or insensitive references in the text.

This text does the difficult job of introducing the complicated concepts and processes of quantitative research in a quick and easy reference guide fairly well. I would not depend solely on this text to teach students about quantitative research, but it could be a good jumping off point for those who have no prior knowledge on this subject or those who need a gentle introduction before diving in to more advanced and complex readings of quantitative research methods.

Reviewed by J. Marlie Henry, Adjunct Faculty, University of Saint Francis on 12/9/21

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of... read more

Considering the length of this guide, this does a good job of addressing major areas that typically need to be addressed. There is a contents section. The guide does seem to be organized accordingly with appropriate alignment and logical flow of thought. There is no glossary but, for a guide of this length, a glossary does not seem like it would enhance the guide significantly.

The content is relatively accurate. Expanding the content a bit more or explaining that the methods and designs presented are not entirely inclusive would help. As there are different schools of thought regarding what should/should not be included in terms of these designs and methods, simply bringing attention to that and explaining a bit more would help.

Relevance/Longevity rating: 3

This content needs to be updated. Most of the sources cited are seven or more years old. Even more, it would be helpful to see more currently relevant examples. Some of the source authors such as Andy Field provide very interesting and dynamic instruction in general, but they have much more current information available.

The language used is clear and appropriate. Unnecessary jargon is not used. The intent is clear- to communicate simply in a straightforward manner.

The guide seems to be internally consistent in terms of terminology and framework. There do not seem to be issues in this area. Terminology is internally consistent.

For a guide of this length, the author structured this logically into sections. This guide could be adopted in whole or by section with limited modifications. Courses with fewer than seven modules could also logically group some of the sections.

This guide does present with logical organization. The topics presented are conceptually sequenced in a manner that helps learners build logically on prior conceptualization. This also provides a simple conceptual framework for instructors to guide learners through the process.

Interface rating: 4

The visuals themselves are simple, but they are clear and understandable without distracting the learner. The purpose is clear- that of learning rather than visuals for the sake of visuals. Likewise, navigation is clear and without issues beyond a broken link (the last source noted in the references).

This guide seems to be free of grammatical errors.

It would be interesting to see more cultural integration in a guide of this nature, but the guide is not culturally insensitive or offensive in any way. The language used seems to be consistent with APA's guidelines for unbiased language.

Reviewed by Heng Yu-Ku, Professor, University of Northern Colorado on 5/13/21

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive... read more

The text covers all areas and ideas appropriately and provides practical tables, charts, and examples throughout the text. I would suggest the author also provides a complete research proposal at the end of Section 3 (page 10) and a comprehensive research study as an Appendix after section 7 (page 26) to help readers comprehend information better.

For the most part, the content is accurate and unbiased. However, the author only includes four types of research designs used on the social sciences that contain quantitative elements: 1. Mixed method, 2) Case study, 3) Quasi-experiment, and 3) Action research. I wonder why the correlational research is not included as another type of quantitative research design as it has been introduced and emphasized in section 6 by the author.

I believe the content is up-to-date and that necessary updates will be relatively easy and straightforward to implement.

The text is easy to read and provides adequate context for any technical terminology used. However, the author could provide more detailed information about estimating the minimum sample size but not just refer the readers to use the online sample calculators at a different website.

The text is internally consistent in terms of terminology and framework. The author provides the right amount of information with additional information or resources for the readers.

The text includes seven sections. Therefore, it is easier for the instructor to allocate or divide the content into different weeks of instruction within the course.

Yes, the topics in the text are presented in a logical and clear fashion. The author provides clear and precise terminologies, summarizes important content in Table or Figure forms, and offers examples in each section for readers to check their understanding.

The interface of the book is consistent and clear, and all the images and charts provided in the book are appropriate. However, I did encounter some navigation problems as a couple of links are not working or requires permission to access those (pages 10 and 27).

No grammatical errors were found.

No culturally incentive or offensive in its language and the examples provided were found.

As the book title stated, this book provides “A Quick Guide to Quantitative Research in Social Science. It offers easy-to-read information and introduces the readers to the research process, such as research questions, research paradigms, research process, research designs, research methods, data collection, data analysis, and data discussion. However, some links are not working or need permissions to access them (pages 10 and 27).

Reviewed by Hsiao-Chin Kuo, Assistant Professor, Northeastern Illinois University on 4/26/21, updated 4/28/21

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and... read more

As a quick guide, it covers basic concepts related to quantitative research. It starts with WHY quantitative research with regard to asking research questions and considering research paradigms, then provides an overview of research design and process, discusses methods, data collection and analysis, and ends with writing a research report. It also identifies its target readers/users as those begins to explore quantitative research. It would be helpful to include more examples for readers/users who are new to quantitative research.

Its content is mostly accurate and no bias given its nature as a quick guide. Yet, it is also quite simplified, such as its explanations of mixed methods, case study, quasi-experimental research, and action research. It provides resources for extended reading, yet more recent works will be helpful.

The book is relevant given its nature as a quick guide. It would be helpful to provide more recent works in its resources for extended reading, such as the section for Survey Research (p. 12). It would also be helpful to include more information to introduce common tools and software for statistical analysis.

The book is written with clear and understandable language. Important terms and concepts are presented with plain explanations and examples. Figures and tables are also presented to support its clarity. For example, Table 4 (p. 20) gives an easy-to-follow overview of different statistical tests.

The framework is very consistent with key points, further explanations, examples, and resources for extended reading. The sample studies are presented following the layout of the content, such as research questions, design and methods, and analysis. These examples help reinforce readers' understanding of these common research elements.

The book is divided into seven chapters. Each chapter clearly discusses an aspect of quantitative research. It can be easily divided into modules for a class or for a theme in a research method class. Chapters are short and provides additional resources for extended reading.

The topics in the chapters are presented in a logical and clear structure. It is easy to follow to a degree. Though, it would be also helpful to include the chapter number and title in the header next to its page number.

The text is easy to navigate. Most of the figures and tables are displayed clearly. Yet, there are several sections with empty space that is a bit confusing in the beginning. Again, it can be helpful to include the chapter number/title next to its page number.

Grammatical Errors rating: 4

No major grammatical errors were found.

There are no cultural insensitivities noted.

Given the nature and purpose of this book, as a quick guide, it provides readers a quick reference for important concepts and terms related to quantitative research. Because this book is quite short (27 pages), it can be used as an overview/preview about quantitative research. Teacher's facilitation/input and extended readings will be needed for a deeper learning and discussion about aspects of quantitative research.

Reviewed by Yang Cheng, Assistant Professor, North Carolina State University on 1/6/21

It covers the most important topics such as research progress, resources, measurement, and analysis of the data. read more

It covers the most important topics such as research progress, resources, measurement, and analysis of the data.

The book accurately describes the types of research methods such as mixed-method, quasi-experiment, and case study. It talks about the research proposal and key differences between statistical analyses as well.

The book pinpointed the significance of running a quantitative research method and its relevance to the field of social science.

The book clearly tells us the differences between types of quantitative methods and the steps of running quantitative research for students.

The book is consistent in terms of terminologies such as research methods or types of statistical analysis.

It addresses the headlines and subheadlines very well and each subheading should be necessary for readers.

The book was organized very well to illustrate the topic of quantitative methods in the field of social science.

The pictures within the book could be further developed to describe the key concepts vividly.

The textbook contains no grammatical errors.

It is not culturally offensive in any way.

Overall, this is a simple and quick guide for this important topic. It should be valuable for undergraduate students who would like to learn more about research methods.

Reviewed by Pierre Lu, Associate Professor, University of Texas Rio Grande Valley on 11/20/20

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas. read more

As a quick guide to quantitative research in social sciences, the text covers most ideas and areas.

Mostly accurate content.

As a quick guide, content is highly relevant.

Succinct and clear.

Internally, the text is consistent in terms of terminology used.

The text is easily and readily divisible into smaller sections that can be used as assignments.

I like that there are examples throughout the book.

Easy to read. No interface/ navigation problems.

No grammatical errors detected.

I am not aware of the culturally insensitive description. After all, this is a methodology book.

I think the book has potential to be adopted as a foundation for quantitative research courses, or as a review in the first weeks in advanced quantitative course.

Reviewed by Sarah Fischer, Assistant Professor, Marymount University on 7/31/20

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable). read more

It is meant to be an overview, but it incredibly condensed and spends almost no time on key elements of statistics (such as what makes research generalizable, or what leads to research NOT being generalizable).

Content Accuracy rating: 1

Contains VERY significant errors, such as saying that one can "accept" a hypothesis. (One of the key aspect of hypothesis testing is that one either rejects or fails to reject a hypothesis, but NEVER accepts a hypothesis.)

Very relevant to those experiencing the research process for the first time. However, it is written by someone working in the natural sciences but is a text for social sciences. This does not explain the errors, but does explain why sometimes the author assumes things about the readers ("hail from more subjectivist territory") that are likely not true.

Clarity rating: 3

Some statistical terminology not explained clearly (or accurately), although the author has made attempts to do both.

Very consistently laid out.

Chapters are very short yet also point readers to outside texts for additional information. Easy to follow.

Generally logically organized.

Easy to navigate, images clear. The additional sources included need to linked to.

Minor grammatical and usage errors throughout the text.

Makes efforts to be inclusive.

The idea of this book is strong--short guides like this are needed. However, this book would likely be strengthened by a revision to reduce inaccuracies and improve the definitions and technical explanations of statistical concepts. Since the book is specifically aimed at the social sciences, it would also improve the text to have more examples that are based in the social sciences (rather than the health sciences or the arts).

Reviewed by Michelle Page, Assistant Professor, Worcester State University on 5/30/20

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new... read more

This text is exactly intended to be what it says: A quick guide. A basic outline of quantitative research processes, akin to cliff notes. The content provides only the essentials of a research process and contains key terms. A student or new researcher would not be able to use this as a stand alone guide for quantitative pursuits without having a supplemental text that explains the steps in the process more comprehensively. The introduction does provide this caveat.

Content Accuracy rating: 3

There are no biases or errors that could be distinguished; however, it’s simplicity in content, although accurate for an outline of process, may lack a conveyance of the deeper meanings behind the specific processes explained about qualitative research.

The content is outlined in traditional format to highlight quantitative considerations for formatting research foundational pieces. The resources/references used to point the reader to literature sources can be easily updated with future editions.

The jargon in the text is simple to follow and provides adequate context for its purpose. It is simplified for its intention as a guide which is appropriate.

Each section of the text follows a consistent flow. Explanation of the research content or concept is defined and then a connection to literature is provided to expand the readers understanding of the section’s content. Terminology is consistent with the qualitative process.

As an “outline” and guide, this text can be used to quickly identify the critical parts of the quantitative process. Although each section does not provide deeper content for meaningful use as a stand alone text, it’s utility would be excellent as a reference for a course and can be used as an content guide for specific research courses.

The text’s outline and content are aligned and are in a logical flow in terms of the research considerations for quantitative research.

The only issue that the format was not able to provide was linkable articles. These would have to be cut and pasted into a browser. Functional clickable links in a text are very successful at leading the reader to the supplemental material.

No grammatical errors were noted.

This is a very good outline “guide” to help a new or student researcher to demystify the quantitative process. A successful outline of any process helps to guide work in a logical and systematic way. I think this simple guide is a great adjunct to more substantial research context.

Table of Contents

  • Section 1: What will this resource do for you?
  • Section 2: Why are you thinking about numbers? A discussion of the research question and paradigms.
  • Section 3: An overview of the Research Process and Research Designs
  • Section 4: Quantitative Research Methods
  • Section 5: the data obtained from quantitative research
  • Section 6: Analysis of data
  • Section 7: Discussing your Results

Ancillary Material

About the book.

This resource is intended as an easy-to-use guide for anyone who needs some quick and simple advice on quantitative aspects of research in social sciences, covering subjects such as education, sociology, business, nursing. If you area qualitative researcher who needs to venture into the world of numbers, or a student instructed to undertake a quantitative research project despite a hatred for maths, then this booklet should be a real help.

The booklet was amended in 2022 to take into account previous review comments.  

About the Contributors

Christine Davies , Ph.D

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  • Knowledge Base
  • Methodology
  • Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

Qualitative vs quantitative research

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

  • Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
  • Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
  • Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

  • Interviews : Asking open-ended questions verbally to respondents.
  • Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
  • Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
  • Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

  • Use quantitative research if you want to confirm or test something (a theory or hypothesis)
  • Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

  • Average scores
  • The number of times a particular answer was given
  • The correlation or causation between two or more variables
  • The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

  • Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
  • Thematic analysis : Closely examining the data to identify the main themes and patterns
  • Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
  • If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organise your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

Examples of quantitative research questions

  • What is the relationship between class size and student academic performance?
  • Does the use of technology in the classroom improve learning outcomes?
  • How does parental involvement affect student achievement?
  • What is the effect of a new drug treatment on reducing blood pressure?
  • Is there a correlation between physical activity levels and the risk of cardiovascular disease?
  • How does socioeconomic status influence access to healthcare services?
  • What factors influence consumer purchasing behavior?
  • Is there a relationship between advertising expenditure and sales revenue?
  • How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

  • Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
  • Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
  • Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
  • Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

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Quantitative Research on Corporate Social Responsibility: A Quest for Relevance and Rigor in a Quickly Evolving, Turbulent World

  • Original Paper
  • Published: 25 November 2022
  • Volume 187 , pages 1–15, ( 2023 )

Cite this article

  • Shuili Du 1 ,
  • Assaad El Akremi 2 &
  • Ming Jia 3  

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In this article, the co-editors of the corporate responsibility: quantitative issues section of the journal provide an overview of the quantitative CSR field and offer some new perspectives on where the field is going. They highlight key issues in developing impactful, theory-driven, and ethically grounded research and call for research that examines complex problems facing businesses and the society (e.g., big data and artificial intelligence, political polarization, and the role of CSR in generating social impact). By examining topics that are under-researched, forward-looking, and socially oriented, scholars can expand the boundary of CSR’s substantive domain and produce research that helps businesses act in a long-term, socially responsible way in this quickly evolving, turbulent environment. They also discuss ways to enhance the methodological rigor of quantitative CSR research and encourage scholars to employ cutting-edge, innovative methods to shed light on the micro-level mechanisms of CSR and reveal patterns and relationships hidden in unstructured big data.

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Research on corporate social responsibility (CSR) has flourished over the last few decades, providing significant insights into whether and how corporations should enact their societal obligations and stakeholder responsibilities. Sustainable and socially responsible development is a grand challenge for our society due to climate change, dwindling natural resources, and exacerbating social and economic inequity. Responding to this grand challenge, more than 12,000 businesses in 160 countries are signatories to the United Nations’ Global Compact, committing to aligning their business strategies and operations with socially responsible principles on human rights, labor, environment, and anti-corruption. In 2019, the CEOs of the Business Roundtable, representing the largest US companies, released a new “Statement on the Purpose of a Corporation” that supersedes previously endorsed principles of shareholder primacy and outlines a modern standard for corporate responsibility (Business Roundtable, 2019 ). Without a doubt, CSR has entered the domain of mainstream business strategy, permeating key aspects of business decision making. At the same time, we live in a quickly evolving, turbulent world, facing unprecedented challenges, including disruptive technologies (e.g., big data, the Internet of Things, artificial intelligence, and blockchain technology), political polarization, shifting geopolitics and international relations, and post-pandemic economic and social issues. These trends present new opportunities and challenges for corporations seeking to fulfill their social responsibility. Thus, the sizable body of CSR literature notwithstanding, we need more, not less, relevant and rigorous CSR research that examines complex and nuanced challenges and tradeoffs facing businesses today and that pushes the boundaries of the field by increasing the breadth and depth of CSR research topics.

Reflecting the prominence of CSR and the widespread scholarly enthusiasm with the topic, the CSR quantitative section of the Journal of Business Ethics receives several hundred submissions annually, of which only a small percentage are accepted for publication. The standards for publication are significantly higher than in the past for several reasons. First, as the field of CSR quantitative research matures, it becomes more difficult to provide novel and significant theoretical contributions. Previous studies on CSR have already examined many key outcomes (e.g., corporate financial performance, innovation, goodwill effect, stakeholder satisfaction and loyalty; Godfrey et al., 2009 ; Servaes & Tamayo, 2013 ; Valentine & Fleischman, 2008 ), antecedents (e.g., board and CEO characteristics, stakeholder pressure; Jia & Zhang, 2013 ; Perez-Batres et al., 2012 ), underlying psychological processes (e.g., identification, CSR attribution; Gond et al., 2017 ; Sen & Bhattacharya, 2001 ), and contingencies (e.g., corporate reputation, CSR fit, stakeholder characteristics; Sen et al., 2016 ). To generate significant theoretical contributions, CSR scholars need to either incrementally advance current CSR knowledge or offer an original, dramatically new perspective on CSR-related phenomena (e.g., strategic silence on CSR communication; Carlos & Lewis, 2017 ; Wang et al., 2021a , 2021b ), both of which become increasingly difficult as the body of CSR quantitative research expands. There are, however, plenty of opportunities for relevant and rigorous CSR research that tackles current and emerging social problems and issues, such as those related to big data and artificial intelligence and those related to political polarization. In line with the most recent JBE editorial that emphasizes “reconnecting to the social in business ethics” (Islam & Greenwood, 2021 ), CSR scholarship should be future-oriented and have some degree of foresight or prescience (Corley & Gioia, 2011 ) in trying to anticipate, conceptualize, and influence significant future problems related to firms’ social responsibility. It is important to conceptualize emerging topics and engage in research that shapes the future of the business world by questioning accepted practices and promulgating new ways of doing business responsibly.

The second reason that the standards for publications are higher is methodological. The journal and reviewers have set the bar high regarding methodological clarity and rigor. Papers with a strong method section should provide a clear rationale for sample selection and construct operationalization, explain and justify model specification and data analysis approaches, and sufficiently address key methodological concerns, such as construct validity, common method bias, endogeneity issues, and robustness tests. Innovative approaches in methods, such as utilizing multiple study designs (e.g., a laboratory experiment coupled with a field survey or an archival study) and employing cutting-edge technologies in data collection and analysis (e.g., eye tracking, neuroscience tools, textual analysis, and natural language processing), are highly appreciated.

Looking at the papers submitted to the CSR quantitative section, we find that rejected papers often exhibit one or more of the following characteristics: (1) weak theoretical contribution, sometimes due to a paper’s focus on a narrow and highly incremental topic or its lack of finer-grained conceptualization and insights (e.g., main effect hypotheses with little insight into the underlying mechanism and/or contingent factors); (2) questionable methods, sometimes due to weaknesses in the study design, sampling, measurement, or data analysis or a lack of empirical support for the hypotheses; and (3) poor writing, which manifests in various ways, ranging from substantive aspects such as unconvincing motivation for the study and incoherent or weak explanatory logic for the hypotheses, to technical aspects such as grammatical and punctuation errors, typos, and improper formatting. It is not uncommon for poor writing to hinder an otherwise promising paper.

In contrast, accepted papers tend to not only focus on an important topic and have a strong theory section but also demonstrate methodological rigor and offer rich insights with theoretical and practical value. To illustrate, while most previous CSR research examines business outcomes but neglects the social outcomes of corporate social initiatives, Boodoo et al. ( 2022 ) focus on the social outcomes of corporate philanthropy in the case of health grants by corporate foundations and find that, paradoxically, health grants are less likely to go to areas with more severe health needs, thus exacerbating health inequity. This research has important implications for the social efficacy of corporate philanthropy and calls for a data-driven and needs-based approach to the distribution of corporate donations and resources. Another example is the paper by Miller et al. ( 2022 ) examining the interplay between firms and individuals in the same geographic communities and finding that firms with high CSR performance positively influence the social distancing behaviors of individuals during the COVID-19 pandemic. This research breaks new ground by expanding the scope of CSR outcomes and revealing a previously unexamined effect of CSR: how a firm’s CSR influences individuals’ ethical behavior in their communities.

As the section editors of the CSR quantitative section, we would like to share our view of where the field of CSR quantitative research is going, highlight several substantive topic areas that are timely but under-researched, as well as discuss ways to enhance the methodological rigor of research and call for the utilization of innovative methodological techniques. This editorial seeks to stimulate research on relevant, forward-looking topics and increase emphasis on methodological rigor and innovativeness.

Developing Impactful, Theory-Driven, and Ethically Grounded CSR Research

Research on CSR has been criticized for both a lack of theoretical foundations (Wang et al., 2020 ) and deficient practical impact (Barnett et al., 2020 ). Despite the tremendous growth of CSR research, we still question the value of the field and critique its insightfulness for managerial and organizational practices. The “countless” corporate investment in terms of time and money in CSR initiatives notwithstanding (Davidson et al., 2019 ), firms still struggle to determine how, where and when to devote their social and environmental efforts (Wang et al., 2020 ). Quantitative CSR researchers should move toward more novel theoretical development, stronger scientific rigor, and broader applied insight rather than filling gaps in the literature and refining analytic methods.

Impactful CSR Research

There are multiple ways to increase the potential impact of CSR research. First, we call for more research to quantitatively examine the societal and environmental outcomes of CSR. Until recently, CSR research was mainly dominated by a business-centric focus, primarily concerned with the business case of CSR and how CSR can improve firm-level outcomes such as financial performance, reputation, and competitive advantage. As a result, we know most about CSR’s impact on businesses and the various benefits for businesses, and least about how CSR affects the major societal issues it was intended to tackle (Blowfield, 2007 ). Calling for a shift in CSR research from a business-centric to a society-centric focus, Wickert ( 2021 , p. 15) urged, “We need to know more about how to effectively capture the impact of CSR beyond financial performance, as well as how different social and ecological outcomes are linked to what businesses do in the name of CSR.” Quantitative CSR research should investigate cause-effect relationships between CSR initiatives and societal outcomes such as workers’ health, equality and inclusion, biodiversity and natural environment resilience, and labor conditions and sustainable sourcing in global supply chains. It is also important to go beyond a short-term focus to examine the long-term, multifaceted, and sometimes double-edged impact of CSR on society and the environment (e.g., Luo et al., 2018 ; Wood, 2010 ). Such a socially oriented approach to quantitative CSR research will be more impactful and will broaden the predominant business case logic with social, ecological, and ethical cases (Wickert, 2021 ).

Second, producing impactful CSR research requires researchers to embrace new and bolder ideas instead of only focusing on theoretical “gaps” or methodological refinements. Impact should go beyond the narrower metric of research citations and measure whether a study pushes the boundary of existing CSR literature by tackling local and global societal problems in a quickly evolving, volatile, uncertain, and complex context. In addition to investigating “grand challenges” such as poverty, health, inequality, and climate change, researchers can produce novel insights into emergent phenomena that are significant and important to individuals, corporations, and the society, such as the changing role of CSR in an environment characterized by big data and smart technologies (Du & Xie, 2021 ) and political polarization and shifting geopolitical dynamics (Korschun et al., 2020 ), as well as the role of CSR in generating social impact and building societal resilience during major crises (e.g., the Covid pandemic and the Russia-Ukraine War). Impact also comes from adopting multiple levels of analyses and innovative and rich methodological approaches such as field experiments and textual analysis using machine learning algorithms.

In summary, impactful CSR research investigates new, significant, and societally relevant topics and utilizes rich data analytic methods that better determine causation rather than just ascertain correlation. Theoretical and empirical rigor is not opposed to but rather contributes to the greater impact of quantitative CSR research.

Theory-Driven CSR Research

Theory-driven quantitative CSR research is important for several reasons. First, we need a theory-driven approach precisely because quantitative CSR research has often been criticized for being undertheorized (Wang et al., 2020 ). The field lacks both theoretical foundation and coherence despite the application of multiple theoretical perspectives, including stakeholder theory, agency theory, upper echelons theory, economic theories of information and incentives at the macro level and social exchange theory, identity theory, attribution theory, and justice theory at the micro-level. Many such theories, originated in other fields and based on the primacy of shareholder interests, either do not fit well within the CSR context or could not adequately account for the complexity of the intersection between economic, social, environmental, and governance interests that characterize the CSR field (Hilliard, 2019 ; Wang et al., 2020 ). Moreover, the field of CSR has been mainly practice-driven and empirically focused on the business case examining the relationship between CSR and corporate financial performance. This phenomena-driven focus, more prominent in earlier CSR research, has hindered the theoretical development of the field, limited its theoretical insights, and favored a loose application of theories and a lack of investigation of the underlying causal mechanisms and boundary conditions (Wang et al., 2020 ).

Second, a theory-driven approach to quantitative CSR research is necessary because using sophisticated empirical methods without theory-based causal analysis at best yields shallow and misleading results (Simmons et al., 2011 ). Theory provides guidance to research questions and logical reasoning, forces discipline in methodology (i.e., measurement, data collection, analysis), and imparts meaning to empirical results (Cortina, 2016 ; Van de Ven, 2007 ; Van Maanen et al., 2007 ). Third, when authors build their quantitative study upon a strong and relevant theoretical framework from the beginning, they can more clearly explain their theoretical contributions and show what is novel, significant, and insightful in their work beyond what we already know at a theoretical level. Starting with a solid theoretical framework is crucial for producing novel and impactful insights because “identifying the uniqueness and novelty of a given approach is difficult in the absence of a solid understanding of what is already known or assumed to be true in the literature” (Shaw, 2017 , p. 821).

Responsible and Ethically Grounded CSR Research

It is simplistic to say that all CSR research will contribute to making organizations more ethical and more socially responsible. Rather than describing and taking for granted what is socially responsible and ethical in corporate actions, CSR researchers should critically investigate and assess the ethical premises and the potential positive and negative social impact of these actions. We need to not only understand the role of ethics in business, but also use principles of ethics to evaluate and prescribe the role of business in society (Islam & Greenwood, 2021 , p. 1). For example, previous research has shown that CSR actions can cause unintended harm to some stakeholders who are vulnerable and beleaguered (Willness, 2019 ) and can lead to moral hazards where firms use CSR as reputation insurance to benefit themselves at the cost of society (Luo et al., 2018 ). Responsible research on CSR implies the importance of assessing the potential unintended negative effects of CSR practices and avoiding promoting organizational practices that are harmful to vulnerable stakeholders and society.

To promote responsible quantitative CSR research, scholars need to go beyond a narrow business case perspective when examining CSR phenomena and incorporate an evaluative element to orient ethical and socially responsible corporate actions. For example, when certain CSR actions may have negative effects on firm performance, rather than suggesting that firms should not practice these socially responsible actions, responsible CSR research should reveal the underlying mechanisms for why such negative impacts might occur, understand how to minimize the negative impacts, and examine the ways that firms could better approach these CSR actions to create positive social and business value (Hideg et al., 2020 ). To make quantitative CSR research more responsible, a crucial step is to deepen the study of CSR’s nonfinancial, social and environmental impact, such as the nuanced effects of CSR on community and stakeholder well-being, poverty reduction, diversity and inclusion, and climate change.

Furthermore, when studying the social impact of CSR, researchers should examine not only antecedents and outcomes, but also the underlying processes and boundary conditions of CSR actions. A deeper understanding of the causal mechanisms and contingencies will provide guidance for more effective CSR decision making and implementation and, in turn, accentuate the social impact of organizations’ CSR initiatives. Finally, responsible and ethically grounded CSR research should take into account conflicts of interest among various stakeholder groups to help organizations better understand the priorities and the nonintentional effects of CSR on various groups. To that end, research should shift from considering CSR as an aggregate and homogeneous construct to the analysis of specific subdimensions of sustainable development. The United Nation’s 17 sustainable development goals (SDGs) Footnote 1 include an array of more concrete, diverse and comprehensive goals as compared to the often-used broad categorization of environmental, social, and governance performance. We encourage future quantitative CSR research to examine whether and how firms’ CSR could advance specific SDGs.

Substantive Topic Areas that are Under-Researched and Forward-Looking

Our society is rapidly transforming and faces unprecedented challenges, including disruptive technologies (e.g., big data, artificial intelligence, and blockchain technology), political polarization, shifting geopolitics and international relations, and post-pandemic economic and social issues. By examining research topics that are under-researched, forward-looking, and socially oriented, quantitative CSR scholars can expand the boundary of the field’s substantive domain and produce impactful research that helps businesses act in a long-term, socially responsible way in this fast evolving, turbulent environment.

CSR in the Era of Datafication and Artificial Intelligence

Big data and artificial intelligence (AI) are perhaps today’s most dominant trends, transforming businesses and individual lives and presenting abundant opportunities for CSR research in the era of datafication and AI. AI refers to the ability of machines to carry out tasks by displaying intelligent, human-like behaviors (e.g., machine learning, computer vision, speech recognition, and natural language processing; Russell & Norvig, 2016 ). Over the last decade, AI technologies have experienced exponential growth and are being deployed on a rapidly increasing scale in many industries ranging from manufacturing, transportation, and communications, to retail, healthcare, and financial services. Powered by big data and continuously improving algorithms, AI systems can automate decision making, boost productivity and economy, and liberate individuals from tedious and repetitive work. The promised benefits of AI are numerous. For example, self-driving cars can dramatically reduce car accidents; AI-based healthcare could help solve the elderly care crisis in many developed countries; and smart and precision agriculture can reduce the usage of water, fertilizer, and pesticides while increasing yield.

At the same time, however, increasing datafication and the widespread deployment of AI have triggered many ethical and social issues and raised many urgent research questions for CSR scholars. Forward-looking scholars should broaden and deepen the conceptualization of CSR to better address the emerging ethical and societal challenges in the era of datafication and AI. For example, companies have an unprecedented responsibility to enhance the cybersecurity of their information systems and sensitive data and protect the data privacy of their stakeholders. Data breaches now occur more frequently than ever (Martin et al., 2017 ), exposing sensitive and confidential personal information of stakeholders and causing emotional stress, humiliation, and possibly financial loss. Researchers should examine the characteristics of effective cybersecurity practices that minimize the occurrence of data breaches. Relatedly, there is an urgent need to conceptualize and examine corporate responsibility in the digital space related to protecting stakeholder privacy and well-being. Individual consumers’ demographic information and behavioral data are being continuously tracked and analyzed, and the resultant insights are used in targeted advertising, content customization, and other ethically questionable business practices to achieve profit maximization (Zuboff, 2019 ). We call for research on socially responsible privacy practices that are centered around stakeholder well-being. One important research question is to examine the characteristics of corporate responsible data practices that are effective in protecting the privacy and security of stakeholders’ sensitive data. Researchers can also examine how a firm’s (ir) responsible data privacy practices influence its CSR reputation and stakeholder relationships.

Another area for future research relates to addressing the various limitations of AI and the associated ethical and social issues. Research suggests that most AI algorithms exhibit biases against minority and underprivileged groups, mirroring deep imbalances in the institutional environment and reinforcing social injustice (Zou & Schiebinger, 2018 ). Such AI biases will have profound negative social impact, especially considering that AI technologies are being deployed in many high-stakes domains, ranging from self-driving cars and mortgage lending to medical diagnosis and law enforcement. Future research can investigate the ethics of AI algorithms and the effects of AI applications on firms’ diversity, equity, and inclusion performance in the workplace and the marketplace. CSR scholars should compare and contrast various corporate approaches to dealing with AI biases and examine their efficacy in terms of the consequent social outcomes (e.g., inclusion and social equity metrics, well-being of vulnerable and disadvantaged stakeholders).

Finally, the increasing deployment of AI triggers other societal issues, such as potential large-scale unemployment due to automation and the widespread social media and smartphone addiction, all with far-reaching societal and political implications. These issues are fertile ground for relevant and impactful CSR research projects. For example, one promising area of research is to examine what are characteristics of effective corporate initiatives that reskill or upskill their employees to help them thrive in a digital, AI-mediated economy. It is also important to assess the social and business outcome of such employee-oriented CSR initiatives as well as contingent factors.

Overall, the ethical and societal challenges of datafication and AI are fertile ground for impactful CSR research. As companies navigate the uncharted territories of an increasingly AI-mediated economy, they could benefit from CSR research that sheds light on how companies can shape the future of ethical and socially responsible AI and achieve symbiosis between AI technologies and society. Relatedly, the emergence and availability of massive, unstructured big data and AI-enabled machine learning technologies (e.g., natural language processing, image processing, text, and sentiment analysis) also provide opportunities for quantitative researchers to explore new CSR topics and advance knowledge on existing topics.

CSR in a Politically Polarized Environment

We live in a world that is more politically polarized than ever, with a global political system that is undergoing profound transformation. In the United States, the disagreement has become nearly irreconcilable between Democrats and Republicans on the economy, racial justice, climate change, law enforcement, international engagement, and a long list of other issues (Pew Research Center, 2020 ). In Europe, Brexit has polarized British politics, and the rise of right-wing populism has disrupted party systems in other European countries, such as France, Germany, and Austria (Noury & Roland, 2020 ). Political polarization has also manifested itself in the global south in countries such as Brazil, India, and Kenya (Carothers & O’Donohue, 2019 ). This widening ideological divide is caused in part by economic factors related to globalization and trade openness, rising inequality, and economic crises and anxiety; in part by a cultural backlash against the multiculturism and cultural evolution of the last 50 years (i.e., evolution toward gender equality, laws against the discrimination of ethnic and sexual minorities, etc., Inglehart & Norris, 2016 ); and in part by the prevalence of social media, the social media filter bubble, and fake news (Spohr 2017 ).

Against this backdrop of the widening political fissure, corporate political activism has become a frontier area of CSR (Moorman, 2020 ; Smith & Korschun, 2018 ), as is evident from the uptick in the number of companies taking a stand on politically controversial issues. For example, the US apparel company Patagonia created a space in its stores for customers to sign a petition against President Trump’s executive order discontinuing protections of large swaths of federal parklands (Stanley, 2020 ). Dick’s Sporting Goods took a highly publicized stance on gun control by removing guns from its stores after the 2018 Parkland, Florida school shooting (Bomey, 2018 ). Irish airline company Ryanair ran newspaper advertisements in 2016 against Brexit, arguing that consumers would end up paying more to fly outside of the United Kingdom (Davies, 2016 ). Indeed, business leaders increasingly consider it appropriate for companies to take a stand on political issues; according to a CMO survey (Moorman, 2020 ), 47.2% of marketing leaders consider it appropriate to make changes to products and services in response to political issues, and 33.3% consider it appropriate to have executives speak out on political issues. Comparing CSR and corporate activism, Eilert and Cherup ( 2020 ) note that while CSR generally focuses on issues that are widely favored or accepted in the institutional environment (e.g., supporting education, community outreach), corporate activism tends to focus on issues that are controversial in the institutional environment (e.g., gun control, transgender rights, racial equity) and thus has a moderate to high likelihood of triggering negative stakeholder reactions. These controversial sociopolitical issues are “salient unresolved social matters on which societal and institutional opinion is split, thus potentially engendering acrimonious debate among groups” (Nalick et al., 2016 , p. 386). Corporate activism pushes the boundary of traditional CSR in the sense that while both seek to “do good” for society, corporate activism addresses issues that face barriers in their progress toward a solution and promotes social change by “placing pressures on institutions” (Den Hond & De Bakker, 2007 , p. 901).

In this polarized environment, stakeholders are more likely to view companies through a political lens and expect companies to engage in partisan and controversial sociopolitical issues (Korschun et al., 2020 ). Recent research has begun to examine important questions about corporate sociopolitical activism, such as investor reactions to corporate activism (Bhagwat et al., 2020 ), various mental models of corporate activism (Moorman, 2020 ), and the efficacy of CEO activism (Chatterji & Toffel, 2019 ). As the frontier area of CSR research, there are many promising avenues for future research on corporate sociopolitical activism. Future research can investigate key antecedent conditions of corporate sociopolitical activism (e.g., issue-, company-, and stakeholder-specific characteristics) and examine how stakeholders react differently to corporate sociopolitical activism as compared to traditional CSR initiatives. Additionally, given the inherent business risks and controversial nature of sociopolitical activism, CSR scholars should identify strategic levers that companies can use to reduce business risks while enhancing the social and business outcomes of corporate activism and investigate the underlying mechanisms for corporate sociopolitical activism to create positive social change. It is also worth examining how firms could best communicate their corporate activism initiatives and how corporate activism affects consumer reactions (e.g., consumer attitudes, relationships with the brand, and purchase decisions) and employee reactions (e.g., job satisfaction, retention rate, etc.).

A New Mode of CSR Research: Strengthening Theoretical Perspectives on the Social Impact of CSR

Decades of CSR research notwithstanding, scholars have mostly focused on the business case of CSR (i.e., how CSR could affect a firm’s financial performance) but have largely neglected the social impact of CSR (Barnett et al., 2020 ). As a result, whereas there are extensive insights as to whether, how, and when CSR contributes to the financial bottom line of a company, there are extremely limited insights as to whether, how, and when CSR activities produce their intended social impact. Together with worsening climate change, widening social and economic inequalities, recent crises such as the COVID-19 pandemic and the Russia-Ukraine war have accentuated and accelerated the need for CSR scholars to take a societal turn and focus on social issues and grand challenges such as poverty, social justice, human rights, healthy societies, and a sustainable environment. We call for a new mode of CSR research, urging quantitative CSR researchers to adopt a society-centric focus and examine the social and ecological impact of CSR. Understanding and quantifying the social impacts attributable to specific CSR initiatives is a necessary first step in better guiding firms’ resource allocation to CSR and the effective design of CSR programs. Along the same line, Barnett et al., ( 2020 , p. 955) advocate a design approach in CSR research, “Taking a design approach, CSR scholars transform from passive observers and assessors of organizations into active agents in designing and redesigning organizations to create a better world. Guiding managerial decision making toward the most efficient and effective means of achieving specific impacts—positive social changes—becomes the objective of CSR research.”

It is important to strengthen the theoretical underpinning when examining the social impact of CSR. We encourage researchers to adopt a diverse range of theoretical perspectives to deepen current understanding of whether, how, and when CSR could create social impact and benefit the targeted stakeholder groups. For example, resource-based view (Barney, 2001 ; Branco & Rodrigues, 2006 ) would be pertinent in linking a firm’s unique resources and capabilities to the social efficacy of its CSR initiatives; researchers can examine whether and how CSR initiatives that leverage a firm’s unique capabilities (e.g., technical expertise, marketing capabilities, human talents) are likely to produce greater social impact. Theories on social network and social capital (Burt, 1997 ; Inkpen & Tsang, 2005 ) can add conceptual depth when examining corporate alliances, cross-sector partnerships, and stakeholder collaborations aimed at addressing complex societal and environmental problems.

Theoretical perspectives are also essential when researchers attempt to capture, categorize, and quantify the different forms and various dimensions of CSR’s social impact. Barnett et al. ( 2020 ) use the literature on development economics to highlight the need to assess not only immediate outputs from CSR activities (e.g., number of beneficiaries served, emissions, and financial performance) and outcomes associated with CSR activities (i.e., correlational evidence on societal outcomes such as reduced emissions and improved work environment), but more importantly, causal impacts attributable to CSR activities (i.e., societal outcome improvement caused by CSR activities). Innovation is a key outcome of social impact due to its power in generating positive social change (Porter & Kramer, 2011 ), thus future research on social impact can draw upon theoretical perspectives on responsible innovation (Stilgoe et al., 2013 ) and sustainable innovation (Adams et al., 2016 ; Varadarajan, 2017 ) to predict, measure, and monitor the outcomes of social and sustainable innovation attributable to CSR activities. Finally, behavioral change is an essential aspect of social impact since for many social issues, ranging from health to diversity to environmental protection, it is often the behavioral change adopted by individual stakeholders that creates the most lasting impact in the effort to solve the issue. In this sense, theories from social psychology such as theory of planned behavior (Ajzen, 1991 ) and social cognitive theory (Bandura, 2001 ) are applicable theoretical lens for examining processes and outcomes of desired behavioral change.

We call for more quantitative CSR research to rigorously examine the antecedents, processes, and outcomes of the social impact of CSR activities and to draw more broadly and deeply from relevant disciplinary fields like development economics, sociology, social and cognitive psychology, social work, public health, and public policy. Deepening the theoretical perspectives for this new model of socially oriented CSR research would help us accumulate new insights and help firms design CSR initiatives for greater social impact.

Enhancing the Methodological Rigor of Quantitative CSR Research

Methodological rigor contributes to the credibility of research results and is critical to the overall quality of quantitative CSR research. CSR scholars should strengthen the rigor of methodology, including research design, construct measurement, data analyses, robustness testing, ruling out alternative explanations, and so on. Below we discuss two key issues in detail, construct measurement and the issue of endogeneity.

Construct Measurement

Since measurement is the lens through which we operationalize focal constructs (as well as all control variables), measurement accuracy should be paramount even in theory specification. Quantitative CSR research may suffer from low construct validity and a weak link between CSR constructs and their observed indicators. Many published articles in the quantitative CSR field do not provide sufficient evidence to draw strong conclusions about construct validity. Construct validity indicates the confidence that researchers have that the indicators used (i.e., measures) are good proxies of the targeted constructs (Aguinis & Vandenberg, 2014 ). In the absence of strong evidence of construct validity, substantive research results are generally inconclusive.

Poor construct measurement poses a serious threat to quantitative CSR research. Indeed, there are some critical and difficult issues that hinder the development, evaluation, and refinement of good measures of critical constructs in quantitative research methods. This includes the lack of precision of the underlying constructs, the use of single indicators and categorical measures to represent complex concepts, the inadequate assessment of reliability when self-reported scales are used, and insufficient attention to measurement levels and measurement invariance (Aguinis & Edwards, 2014 ; Cortina et al., 2017 ). To mitigate these measurement concerns, quantitative CSR researchers should more clearly define constructs, ensure that measures are conceptually related to their constructs, and carefully specify the nature and direction of relationships between concepts and measures (Aguinis & Edwards, 2014 ; Cortina et al., 2017 ). Finally, quantitative CSR research needs to establish more validity generalizations through meta-analyses and structural equation modeling (Cortina et al., 2017 ).

Causal Inferences and the Issue of Endogeneity

Causal claims are important and frequently made in quantitative CSR studies. However, to draw causal inferences, empirical studies must satisfy three conditions: (a) the cause must precede the effect temporally, (b) the cause and effect must be reliably associated, and (c) the relationship between the cause and effect must not be explained by other causes (Antonakis et al., 2010 ). The clearest way to establish causality is through randomized experiments. Unfortunately, random assignment is often impractical in CSR research, where studies are conducted in organizational settings or involve units of observation at higher levels of analysis than the individual, such as firms. Since CSR actions are not randomly assigned, nonexperimental studies are prevalent in quantitative CSR research. A major threat to the validity of these nonexperimental studies (e.g., those based on archival data or survey data) is endogeneity Researchers should address the issue of endogeneity with a combination of theoretical logic, research design, statistical analysis, and post hoc robustness tests.

Endogeneity can arise from various sources, such as omitted variables (i.e., unobserved heterogeneity), simultaneity (i.e., reverse causality or feedback loop), measurement error (i.e., systematic error or common method variance), or selection (i.e., self-selection or sample bias) (Wooldridge, 2010 ), which have various impacts and necessitate different remedies (Clougherty et al., 2016 ; Hill et al., 2021 ; Semadeni et al., 2014 ). Multiple methodological reviews show that statistical techniques used to deal with endogeneity, such as the instrumental variable method, are frequently misapplied or not adequately justified and explained (Wolfolds & Siegel, 2019 ). Moreover, even if multiple causes of endogeneity can affect the same estimated relationship in a single study, there is a need to clearly focus on specific causes of endogeneity, as there is no generic remedy for general endogeneity issues, but there is an extensive toolbox of methods adequate to deal with specific causes of endogeneity (Hill et al., 2021 ). Table 1 provides a summary of the different causes of endogeneity and the appropriate remedies.

Specifically, when endogeneity is caused by omitted variable bias, techniques such as control variables, fixed effects, sensitivity analysis, and instrumental variables may offer solutions to help remedy endogeneity (Wu et al., 2022 ). When the cause of endogeneity is simultaneity, dynamic panel techniques, instrumental variables, using exogenous events, or lagging the endogenous variable can be used to address endogeneity. For measurement error, the use of latent variable methods, instrumental estimation and CMV treatment are used to address endogeneity. Finally, Heckman method, differences in differences, and regression discontinuity are the more appropriate techniques when endogeneity is caused by selection biases. While it is impossible for any one study to fully mitigate all endogeneity concerns, we echo the recommendation by Hill et al. ( 2021 ) that, to sufficiently address endogeneity issues, researchers need to (i) offer a clear diagnosis of the endogeneity threat and explicitly establish whether and why a specific cause of endogeneity exists in a study, (ii) justify and clearly explain why the chosen technique is appropriate for addressing the specific source of endogeneity in the context of the focal study, and (iii) increase the transparency in the resulting prognosis and make precise claims about the conclusions regarding endogeneity treatment.

Employing Innovative Methods to Test Hypotheses

Different methods have their respective strengths and weaknesses. Field surveys and archival studies have higher external validity but tend to suffer from issues such as common method biases, inadequate construct measurement, and endogeneity. Randomized laboratory experiments ensure internal validity and shed light into causal links between constructs, yet they often have low external validity. Innovative methods have been employed to strengthen traditional laboratory experiments, field surveys, and/or archival studies. With the booming development of science and technology, we have seen increasing applications of innovative technologies in quantitative research methods, such as eye trackers, face readers, and cognitive neuroscience techniques. These high-tech approaches allow researchers to directly observe the cognitive, emotional and neural processes underlying individual reactions to CSR, shedding light on the micro-level mechanism of how individual stakeholders process CSR-related information and corroborating research findings based on self-reported measures. Below we discuss several state-of-art experimental study technologies that are suitable for CSR research.

Eye Tracking

Eye tracking is a tool to measure eye movements (Holmqvist et al., 2011 ; Meissner & Oll, 2019 ). Eye-tracking studies generally focus on determining where people distribute their attention (such as fixation points or gaze points); to be more specific, eye tracking is used to locate pupil positions and to calculate fixation times and durations with the help of digital images (Ashby et al., 2016 ). One of the most common basic principles of eye tracking is the “eye-mind assumption,” which asserts that people’s attention at certain information is controlled by their brain. Therefore, through monitoring eye movements, eye tracking can reveal what is going on in the brain. For instance, assuming a researcher wants to understand how stakeholders read a CSR report and which parts of the report hold their attention, traditional research methods, such as self-reported behavior, can be inaccurate and misleading. Using eye-tracking data, we can directly assess readers’ fixation duration and times on target areas, thus helping us better understand how stakeholders read a CSR report and differentially process various parts of the report content.

The eye is the window of the soul, and approximately 80% of the external information received by people comes from the visual channel via the eyes; meanwhile, the processes of people’s psychological activities are reflected through their eyes. In an experimental setup, eye-tracking systems allow researchers to record the movements of a participant’s eyes during behavioral processes, thus providing “insights into the cognitive processes underlying a wide variety of human behaviors” (Ashby et al., 2016 , p. 96). Eye-tracking technology is an intuitive and effective method that could be employed in lab experimental settings to reveal the micro-level cognitive processes of stakeholders’ reaction to CSR activities.

Neuroscience Tools

Neuroscience tools enable researchers to have a deeper and more direct understanding of brain activities during decision making (Robertson et al., 2017 ). Common neuroscience tools include fMRI (functional magnetic resonance imaging), EEG (electroencephalogram), and fNIRS (functional near-infrared spectroscopy). The basic principle of these neuroscience tools is that an individual's specific psychological activities will give rise to the activation and excitement of a certain brain area or neurons and thus changes in blood dynamics. Neuroscientific technology can help researchers observe the underlying neural and psychological mechanisms of individual reactions. For instance, deontic justice theory holds that individuals often feel principled moral obligations to uphold norms of justice; however, there is still no coherent framework for explaining how individuals produce and experience deontic justice. Cropanzano et al. ( 2017 ) advanced a theoretical model to provide further understanding into the underlying neural and psychological mechanisms of deontic justice with the help of neuroscience tools. If researchers want to explore what stakeholders think when they make judgments about firms’ CSR activities, the neuroscience techniques could be very useful.

Apart from changes in attention and the brain, facial expression variations also provide researchers with useful information, as facial expressions generally reflect an individual’s emotions and affective states. FaceReader is an advanced tool to automatically analyze people’s facial expressions and provides researchers with an objective evaluation of subjects’ affective states (Noldus, 2014 ). In many cases, scholars need to test how subjects react after reading some critical information about firms. Instead of designing a survey to measure individuals’ emotions and feelings in an indirect way, it is more direct and more reliable to observe their facial expression changes with the help of FaceReader.

Machine Learning and Analysis of Unstructured Data

Eye tracker, FaceReader, and a variety of neuroscience tools could be employed in experimental studies to reveal the cognitive and affective mechanisms of stakeholder reactions to CSR, further enhancing the internal validity of experimental studies. On the other hand, tools such as machine learning and analysis of unstructured data are very useful and allow researchers to systematically extract the patterns and relationships hidden in massive amounts of unstructured data.

Unstructured data are commonly understood as “information that either does not have a predefined data model or is not organized in a predefined manner” (Wikipedia, 2022 ). An estimated 80% of data held by firms today are unstructured data, and they are growing 15 times faster than structured data (Balducci & Marinova, 2018 ). Unstructured data are multifaceted and include verbal (e.g., text, audio) and nonverbal (e.g., image, facial expression, geographic/spatial location) data. As compared to structured data, unstructured data allow researchers to have more flexibility for theoretical discovery and uncover richer conceptual and managerial insights (Balducci & Marinova, 2018 ; Li et al., 2019 ). Table 2 provides a summary of different types of unstructured data and some illustrative examples of prior literature analyzing these unstructured data.

Machine learning can undertake complex analysis with massive amounts of unstructured data. Machine learning techniques in the natural language processing field include topic modeling and word embedding models. Specifically, topic modeling assumes that documents are generated by certain topics, and one topic consists of a set of key words and phrases. Topic modeling extracts latent themes contained in a set of documents and represents the main content in the texts. Latent dirichlet allocation (LDA) is one of the most robust methods in topic modeling (Blei et al., 2003 ), which is an unsupervised machine learning technique that automatically extracts potential topics without human-labeled texts. Topic modeling infers the probability distribution of keywords across topics and the distribution of topics across documents by analyzing the patterns of word occurrence in a voluminous corpus. Based on the outputs of LDA analysis, researchers need to interpret and label certain themes based on the top keywords comprising the topic distribution. Topic modeling can be utilized to analyze key characteristics of CSR practices from corporate disclosures such as annual reports and sustainability reports. For corporate unethical behaviors, Brown et al. ( 2020 ) employed a Bayesian topic modeling algorithm to analyze public firms’ 10-K narratives and produce a valid set of semantically meaningful topics to detect financial misreporting.

In addition to topic modeling, the word embedding model is based on the logic that words illustrate similar meanings when they co-occur with the same neighboring words (Harris, 1954 ). The model can encode words or phrases as numeric vectors through a number of iterations based on a large textual corpus, which provides an effective way to measure the semantics. The new technology of “ word2vec ” is a breakthrough in natural language processing to quantify word vectors (Mikolov et al., 2013 ). Additionally, word vectors allow us to explore the relationship between two words via simple vector arithmetic, such as the cosine similarity, and find the synonyms of seed words. Employing the word embedding model, scholars have analyzed various unstructured data to describe corporate culture (Li et al., 2021 ), measure CEOs’ personality traits (Harrison et al., 2019 ), and identify customer needs (Timoshenko & Hauser, 2019 ).

There are other ways to employ textual analysis to distill the essential facts and trends in the textual information and to reveal the hidden and meaningful information contained in these texts. Prior studies on textual analysis utilize lexical analysis and syntactic structure, such as textual tone, readability, vagueness, and concreteness (Du & Kun, 2021 ; Fabrizio & Kim, 2019 ; Muslu et al., 2019 ). For example, Fabrizio and Kim ( 2019 ) find that firms are likely to use more obfuscating language to disclose their negative environmental information to blur the negative content and increase the information processing costs of the recipient (Fabrizio & Kim, 2019 ). Muslu et al. ( 2019 ) find that high-quality CSR disclosure, calculated based on tone, readability, length, and the numeric and horizon content of CSR report narrative, are associated with more accurate analyst forecasts. Sentiment analysis is another useful approach to detect individuals’ affect or opinions from unstructured data (e.g., online product reviews, social media posts). Sentiment analysis detects the polarity of texts, assessing whether individuals are expressing any form of positive or negative sentiment toward an object. Etter et al. ( 2018 ) employ sentiment analysis of social media data to evaluate affective responses of individuals toward an organization. Overall, unstructured data and cutting-edge machine learning techniques provide exciting opportunities for CSR researchers to examine new topics and extend literature in innovative ways.

We hope this editorial offers new perspectives on how to conduct impactful and rigorous quantitative CSR research. The field of quantitative CSR research has grown dramatically over the last several decades, accumulating a great deal of insights, yet at the same time, it becomes harder to publish papers in this field due to increasing expectations for theoretical contributions and methodological rigor. We hope this editorial can spur more quantitative CSR research that examines complex problems facing businesses today, that expands the substantive domain of the field by increasing the breadth and depth of research topics, and that employs rigorous and innovative methods to test hypotheses.

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Du, S., El Akremi, A. & Jia, M. Quantitative Research on Corporate Social Responsibility: A Quest for Relevance and Rigor in a Quickly Evolving, Turbulent World. J Bus Ethics 187 , 1–15 (2023). https://doi.org/10.1007/s10551-022-05297-6

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  • Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The  aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

  • Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
  • Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

  • Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
  • Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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organization of the study in quantitative research example

Based on the data you want to collect, decide which method is best suited for your research.

  • Experimental research is primarily a quantitative method.
  • Interviews , focus groups , and ethnographies are qualitative methods.
  • Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).


Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

  • You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
  • You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

  • If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
  • If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
  • You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

  • Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
  • Double-check manual data entry for errors.
  • If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

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

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
  • You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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A Case study is: 

  • An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
  • Used to examine an identifiable problem confirmed through research.
  • Used to investigate an individual, group of people, organization, or event.
  • Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

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Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

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What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

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How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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Home » Background of The Study – Examples and Writing Guide

Background of The Study – Examples and Writing Guide

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Background of The Study

Background of The Study


Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

  • Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
  • Provide context: Provide a brief overview of the research area and highlight its significance in the field.
  • Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
  • Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
  • Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
  • Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
  • Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
  • Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

  • Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
  • Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
  • I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
  • Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
  • Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
  • State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
  • Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

  • Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
  • Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
  • Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
  • Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
  • State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
  • Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

  • Provide a clear understanding of the research problem and its context.
  • Identify the gap in knowledge that the study intends to fill.
  • Establish the significance of the research problem and its potential contribution to the field.
  • Highlight the key concepts, theories, and research findings related to the problem.
  • Provide a rationale for the research questions or hypotheses and the research design.
  • Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

  • Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
  • Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
  • Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
  • Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
  • Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

  • Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
  • Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
  • Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
  • Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
  • Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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  • Study Protocol
  • Open access
  • Published: 26 March 2024

Study protocol for the development, trial, and evaluation of a strategy for the implementation of qualification-oriented work organization in nursing homes

  • Corinna Burfeindt 1 , 2 ,
  • Ingrid Darmann-Finck 2 , 3 ,
  • Carina Stammann 5 ,
  • Constance Stegbauer 5 ,
  • Claudia Stolle-Wahl 4 ,
  • Matthias Zündel 4 &
  • Heinz Rothgang 1 , 2  

BMC Nursing volume  23 , Article number:  201 ( 2024 ) Cite this article

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Metrics details

Staffing ratios in nursing homes vary among the federal states of Germany, but there are no rational grounds for these variations. In a previous study, a new instrument for the standardized calculation of staffing requirements in nursing homes was developed ( Algorithm 1.0 ). The development was based on a new empirical data collection method that derives actual and target values for the time and number of care interventions provided. Algorithm 1.0 found an increased requirement of 36% of staff in German nursing homes. Based on these results, the German legislature has commissioned a model program to trial and evaluate a complex intervention comprising increased staffing combined with strategies for organizational development.

The mixed-methods study consists of (i) developing a concept for restructuring the work organization, (ii) the application of this concept combined with increased staffing in 10 nursing homes (complex intervention), and the further development of the concept using a participatory and iterative formal evaluation process. The intervention consists of (a) quantitative measures of increased staffing based on a calculation using Algorithm 1.0 and (b) qualitative measures regarding organizational development. The intervention will be conducted over one year. The effects of the intervention on job satisfaction and quality of care will be evaluated in (iii) a comprehensive prospective, controlled summative evaluation. The results will be compared with ten matched nursing homes as a control group. Finally, (iv) prototypical concepts for qualification-oriented work organization, a strategy for the national rollout, and the further development of Algorithm 1.0 into Algorithm 2.0 will be derived.

In Germany, there is an ongoing dynamic legislation process regarding further developing the long-term care sector. The study, which is the subject of the study protocol presented here, generates an evidence-based strategy for the staffing requirements for nursing homes.

Ethics and dissemination.

This study was approved by the Ethics Committee of the German Association of Nursing Science (Deutsche Gesellschaft für Pflegewissenschaft) on 02.08.2023 (amended on 20.09.2023). Research findings are disseminated through presentations at national and international conferences and publications in peer-reviewed scientific journals.

Trial registration number : German Clinical Trails Register DRKS00031773 (Date of registration 09.11.2023).

Peer Review reports


In most of Europe, the policy affecting the organization and provision of long-term care (LTC) has to face several socio-cultural and economic challenges [ 1 ]. Ageing societies, a shortage of skilled workers, and the aim to maintain a high quality of care are just some of them [ 2 , 3 ]. There is a demand for the evidence-based development of new approaches for the organization of LTC. However, there is not only a wide range of political prerequisites but also a variety of methodological approaches measuring staffing ratios, quality of care, and, for example, job satisfaction in LTC facilities, making it difficult to evaluate the direct correlation between these parameters [ 4 , 5 ].

An established way to assess the quality of care in LTC facilities is to apply quality indicators, and a growing number of countries report quality indicators publicly [ 6 , 7 ]. Frequently reported indicators are related to resident safety outcomes like pressure ulcers, falls, physical restraints, and weight loss [ 7 , 8 ]. In Germany, the public reporting system was revised in 2019 and now comprises the results of i) scientifically developed quality indicators [ 9 ] (Table  2 ), ii) external quality audits (Table  2 ), and iii) additional information about the nursing home (e.g., the accessibility of the care facility, the possibility of a trial stay or the staffing levels). Job satisfaction is essential to nursing home staff retention, and evidence indicates a correlation between job fluctuation and quality of care [ 10 , 11 ]. Stress and low staffing levels are the most prominent reasons for job dissatisfaction among LTC staff and are still growing [ 12 , 13 ].

This pilot program has three central aims. The first is the participatory development of a qualification-oriented work organization, trialing them combined with increased staffing (according to Algorithm 1.0 ) and evaluating the effects of this combination. The new work organization strategy is characterized by assigning work tasks individually according to the qualification of the nursing home staff (qualification orientation). The second aim is to derive a strategy for the national rollout of the implementation of qualification-oriented work organization from the evaluation findings. The third objective of the study is to refine Algorithm 1.0 under the condition of new work organization based on the data from the evaluation and parameterize it to yield Algorithm 2.0 .

Our working hypothesis is that increased staffing, combined with a new work organization that matches the staff's qualifications with the resident’s care needs, can improve job satisfaction and quality of care as defined in the methods section.

This study protocol describes a study conducted by legal mandate after a Europe-wide call for tenders. The study aims to derive a strategy affecting the work organization in German nursing homes. The legal background in Germany and the underlying data collection method are crucial to understanding the study subject to this study protocol.

Legal background in Germany

According to Paragraph 69, Sentence 1 of the German Social Code, Part 11, LTC insurance must provide needs-oriented care in Germany. That means that the provision of care must center the person’s care needs instead of, for example, centering the health care system’s resources. Determining care needs again is defined in Paragraph 14 of the German Social Code, Part 11. According to this Paragraph, the detection of care needs is based on the extent of (physical, cognitive, or psychical) independence impairments. A standardized instrument for assessing the need for LTC (‘ Pflegegutachten ’) was developed, described in Paragraph 15 of the German Social Code, Part 11. Applying this instrument, the assessors and evaluators on behalf of the LTC insurance funds (‘ Medizinischer Dienst’ ) classify every person needing long-term care into one of five different care grades (with care grade one indicating the lowest and care grade 5 indicating the highest level of care dependence). Paragraph 113c of the German Social Code, Part 11 again describes the financing of staffing ratios in nursing homes. These upper limits are calculated based on the care grades so that more staff can be financed when higher care grades are present in the respecting nursing home (note that the term ‘nursing home’ in Germany refers to inpatient long-term care). In Sects. 1–3, the Paragraph distinguishes between three qualification groups for the financing of nursing home staff: i) unqualified nurses (‘ Hilfskräfte’ ), ii) semi-qualified nurses (‘ Assistenzkräfte’ ), and iii) qualified nurses (‘ Fachkräfte’ ).

The legally defined and highly standardized procedure described in the Paragraphs mentioned above is valid in all the states (‘ Länder ’) of Germany. However, Paragraph 75 Sect. 3 of the German Social Code, Part 11 specifies that lower staffing limits must be negotiated on a state level. Due to the different financial resources of the countries, de facto staffing ratios in nursing homes vary among the federal states of Germany. However, there is no significant difference in the distribution of care grades between the states [ 14 ]. Considering that the mean time required to provide needs-oriented care derives from the care grade, staffing ratios should not vary, and there are no rational grounds for existing disparities [ 15 ].

Qualification Levels (QL) in LTC

As seen above, German legislation applies three qualification groups to finance nursing home staff. This is based on the previous study, ‘PeBeM1’ (see below), in which a system was developed that applies the broad definitions of the National Qualification Framework (DQR) derived from the European Qualification Framework (EQF) to nursing homes [ 16 , 17 ]. It creates specific definitions of Qualification Levels on a national level in the given setting (Qualification-Mix-Model; QMM). Furthermore, the QMM determines the minimum QL for the appropriate care provision for every distinct intervention included in an Intervention Catalogue [ 18 ]. The normative assignment of a targeted QL to the interventions was approved by the Quality Assurance Committee (‘ Qualitätsausschuss’ ) according to Paragraph 113b of the German Social Code, Part 11.

Table 1 shows the relationship between the internationally recognized European Qualification Frame (EQF), the QL applied in the PeBeM studies, and the qualification groups of Paragraph 113c of the German Social Code, Part 11.

PeBeM data collection method and Algorithm 1.0

In 2020, a new standardized data collection method was developed and applied within the study ‘Development of a scientifically based procedure for the standardized calculation of staffing requirements in long-term care (PeBeM 1)’. The aim of the study was the development of an instrument that was able to calculate the quantity (in terms of the number) and quality (in terms of the qualification) of required nursing home staff (PeBeM is an acronym for the German word Personalbemessung – calculation of staffing ratios). That instrument is called Algorithm   1.0 [ 15 ].

The PeBeM data collection method consists of three core elements:

Compiling a new assessment of the need for LTC (‘Pflegegutachten’). Within the PeBeM data collection method, experts from the respective assessors and evaluators, on behalf of the LTC insurance funds (i.e., Medizinische Dienste/medicproof GmbH), apply this assessment to gain actual information about the care and health conditions of nursing home residents.

Preparing a care intervention plan that follows the day's structure (‘daily intervention planning’, DIP). Scientists from the study team (who also have an exam in nursing) and nurses from the respective nursing homes tailor an individual DIP for every resident. All available and relevant information about the care-dependent person is brought together. The aim of this step is the detailed depiction of the dependents' individual care needs and the planning of all necessary care interventions for the data collection period.

The shadowing of the nursing staff. The care provision is observed in the nursing home for about one week. While working, the facility's nursing staff is accompanied by a data-collecting shadower (also qualified nursing staff) on a one-to-one basis. The shadowers document the interventions in real-time and the time needed on an electronic device (tablet computer). The QL of the nursing staff is documented automatically by the software. All interventions planned in the DIP appear on the tablet computer screen and must be documented regarding the actual. The system gives time stamps for the duration of documentation when the shadowers start and stop the observed intervention. If an intervention is not provided, the shadowers document a reason. Additionally, they rate the time spent (in terms of surcharges or deductions) and the number of interventions (that is, they state whether a provided intervention was unnecessary or a necessary intervention was not provided).

The PeBeM data collection method is the first standardized, evidence-based data collection method that combines empirical and normative elements and provides data with information about i) number, ii) duration, and iii) qualification on both the actual and the target level. The comparison of the targeted QL (required by the complexity of the residents’ care and health status) and the actual QL from the nursing home staff that provided the care intervention derives the level of QL fitting. These levels are i) fitting (i.e., the QL is as high as required), ii) overqualified (i.e., the QL is higher than required), or iii) underqualified (i.e., the QL is lower than required). Reducing the difference between IS and OUGHT values of number, duration, and qualification can be interpreted as improving the quality of care.

Algorithm 1.0

is a mathematical instrument that determines the required nursing home staff (in number and qualification) based on the number of residents and their care mix within a nursing home. The mean actual and targeted numbers of care interventions and time of provided care (per care dependent in a certain period) can be combined multiplicatively, and required staff numbers can be derived. The primary outcome of applying the algorithm is that for Germany, about 36% more nursing home staff are required to provide adequate needs-oriented care. The input for this calculation was the care mix of all nursing home residents in Germany. The plus of 36% full-time equivalents refers to a care-degree standardized reference nursing home with 100 residents [ 15 ]. However, the instrument addresses the overall number of required nursing home staff and specifies it according to the different QL described above. It was found that the lack of staff varies according to the QL of nursing home staff. In the legally given three qualification groups described above, the additional demand for staff in unqualified and semi-qualified nurses was 69,0%. In contrast, for qualified nurses with higher QLs, it was only 3.5% [ 15 ].

Based on the study results, the German legislature commissioned a model program to (i) trial and evaluate both measures (increased staffing and work organization), (ii) derive a concept for a national rollout, and (iii) use the findings from the evaluation to parameterize Algorithm 2.0. The contracting authority of this model program is the GKV-Spitzenverband ( Spitzenverband Bund der Krankenkassen – National Association of Statutory Health Insurance Funds).

Methods and analysis

Study design and participants.

The entire mixed-methods study is planned to run from December 2022 to May 2025 and comprises a complex intervention (running over one year) and its evaluation. The study is designed in four steps. Although in this article, the steps of the study are described consecutively, we should emphasize that in practice, they cannot be considered discrete and independent. The study steps are (i) the development of an initial implementation concept, (ii) the application of the initial concept and the co-creative, iterative development of customized concepts, (iii) a comprehensive summative evaluation, and finally, (iv) the derivation of prototypical concepts for qualification-oriented work organization which considers different starting points of nursing homes and thus offers different development paths for different types of nursing homes, the development of a strategy for the national rollout, and the parameterization of Algorithm 2.0 .

The intervention consists of increased staffing (based on Algorithm 1.0 ) in 10 nursing homes and restructuring the work organization (intervention group). The central aim of the restructuring of the work organization is qualification orientation. That means that the workflow organization considers the qualifications of the nursing home staff and the resident's care needs to optimize the matching between these parameters (QL-fitting).

Ten matched nursing homes as a control group do not undergo the two intervention measures but participate in the evaluation. Matching criteria are the federal state, number of beds, and ownership. The evaluation assesses (a) the quality of care, (b) the job satisfaction, and (c) the effects on the actual-target differences in number, duration, and QL fit of care provision as defined in step 3 (summative evaluation). QL-fit means that the required QL of the intervention (determined by the complexity of the intervention in interaction with the level of stability of the care situation of the nursing home resident) fits the QL of the nurse. Effects of the intervention concerning (a) and (b) will be measured using a difference-in-difference approach in the intervention and control group nursing home facilities. The difference in difference analysis can be applied when evaluating outcomes associated with healthcare policy implementation. This analysis's beneficial characteristic is the possibility of controlling for background changes [ 19 ]. The evaluation compares the pre- and post-intervention differences in outcomes in (a) and (b)—as operationalized below—between the treatment group (10 nursing homes retrieving the intervention) and the control group (10 nursing homes not retrieving the intervention). Endpoint (c) will only be investigated through a pre-post comparison with the intervention group, as the shadowing of the control group was regarded as too resource-intensive.

The study will involve 20 nursing homes in Germany (10 in the intervention group and 10 in the control group), nursing home staff, residents, about 120–150 data collectors (so-called ‘nurse shadowers’), and about 20 assessors and evaluators on behalf of the LTC insurance funds (Medizinische Dienste/medicproof GmbH). The respective work packages describe the participants' involvement in detail.

Eligibility criteria

Several targeted groups are involved in the study. Nursing home staff must fulfill partially varying inclusion criteria for participation in the different activities. For the survey , they must be involved in the care of the respective nursing home residents and working in the home before the start of the survey. For the shadowing, they must work in the shadowed living area (only in the intervention group). The focus groups must be employed in a participating nursing home from the beginning of the intervention (only in the intervention group). For the competency analysis, they must work in one of the nursing facilities of the intervention group.

Nursing home residents may participate in the survey if they can complete the questionnaire alone or with assistance. A proxy rating will be applied for nursing home residents with cognitive impairments. Participation in the shadowing is possible for nursing home residents if they live in the respective shadowed living area. Terminal residents and residents living in the respective nursing home for less than four weeks will be excluded from the shadowing and the survey.

The inclusion criteria for the shadowers participating in data acquisition (i.e., shadowing) are registration as a qualified (geriatric) nurse and participation in training provided by the study team. There are no further inclusion criteria for the shadowers' survey.


All nursing homes in Germany that submitted a written statement of interest in participating in the study before a deadline could apply. The study team preselected 30 facilities (concerning the state, number of beds, and ownership). These nursing homes have an average capacity of 76 beds (between 30 and 120 beds), are distributed in all 16 states, and between non-profit, public, and private organizations according to the distribution in Germany. Rural and urban areas were taken into account. After visiting the premises and talking to the staff, the research team forwarded the names of 20 nursing homes to the GKV-SV as the study's funder, which made the final selection of the ten nursing homes for the intervention group. The GKV-SV then concluded a subsidy agreement with the selected nursing home facilities. The nursing homes of the control group are selected by the study team and reviewed by the GKV-SV.

Participation in the study is voluntary and based on informed consent. Participants of the intervention group will be recruited at information events held by the study team in the participating nursing homes. At these information events, the study team aims to inform the staff, the residents, and relatives about the study's aims and procedure, enlist support, and recruit survey participants. Participants for the control group are informed and recruited by the respective nursing home management.

The study is structured into four study steps (Fig.  1 ).

figure 1

Conceptual framework of the mixed-methods study

Development of an initial concept

In the theoretical development phase, an initial concept for implementing a qualification-oriented work organization in nursing homes will be developed, starting with a comprehensive national and international literature analysis. Literature will be considered concerning nursing processes, education and training in nursing homes, and theoretical conceptualizations of change processes in organizations. The theoretical basis for the management measures derives from diverse sources in organizational development, personnel development, and change management theories. The management measures will include, for example, monitoring the QL fitting and developing change-process plans. The following components will be combined into the initial concept:

A nursing process that centers on the care needs of nursing home residents.

Education and training measures for different QLs. Formal training courses are planned for QL 4. Training courses are being developed at a low threshold in the QL 1 and 2 work process.

A theoretical model for the organizational change process aims for a qualification-oriented work organization. That means a strategy will be developed to assess the QL in nursing homes (actual and target) and adjust the workflow to increase QL fitting.

Application and co-creative, iterative development of customized concepts

As one single concept cannot be appropriate for all different nursing homes, in the practical trial and customization phase, the initial concept will be further developed into customized and empirically refined implementation concepts. The intervention will begin with applying the initial concept in the ten facilities of the intervention group. The intervention comprises a quantitative and a qualitative component: firstly, increased nursing home staffing based on the previously developed Algorithm 1.0 [ 15 ]. Secondly, qualitative measures which include organizational (work organization with the aim of QL-fitting) and personnel development (education and training). The iterative development process from the initial to customized concepts starts as the intervention begins. The method applied for further iterative development is formative evaluation. This will be conceptualized as an ongoing participatory process of co-creation by scientists and practitioners from nursing homes.

It starts with depicting the actual status to set an individual starting point for the change process. The assessment of the actual status of the nursing homes comprises:

Nursing home inspections regarding organizational structures and management of the homes (e.g., infrastructure, care and case mix, organizational concepts), person-related conditions (e.g., staff and nursing home residents), and service provision processes.

An assessment for the analysis of competencies on the individual level of the nursing homes’ staff. The analysis of competencies is composed of (i) a self-assessment of competencies for nursing home staff, (ii) a proxy assessment of competencies for management staff, and (iii) a comparison of the results in a personal development meeting. Educational needs can be derived from the comparison results and the agreements made during the talks. The instruments will be developed during the project [ 20 ]. The reliability of the self- and proxy assessments will be tested by calculating Cronbach’s alpha.

The formative evaluation process will comprise three instruments to evaluate the concept components' effectiveness, acceptance, and practicability. The instruments are:

Research diary: Research team members with direct contact with the facilities regularly fill out a standardized form for each nursing home (timely after contact, at least every two weeks). The form contains questions about events, experiences, and information that result from the contact and can provide recommendations for the further development of the concept.

Semi-structured expert interviews: Every two weeks, an interview will be conducted with a contact person at the facility (usually the home or nursing service management). Nursing home-specific findings from the research diaries will be used to customize the interview guides and to find solutions co-creatively. In addition to these individual topics, the interviews will concern, e.g., challenges associated with the implementation of the work organization, perceived changes, and the identification of resource-intensive work steps in the implementation.

Focus groups: Every two weeks, members of the study team, with direct contact with the facilities, combine the results of the research diary entries and the expert interviews to develop recommendations for further developing customized concepts.

The three elements are repeated continuously for one year, creating an iterative further development and customization process.

Summative evaluation

The customized concepts will be practiced, refined, and evaluated in the third study phase. The evaluation is planned as a prospective interventional study. In the intervention group, all elements of the data acquisition for the summative evaluation will take place as pre-post-comparison at two points in time (t 0 before the intervention and t 1 after the implementation of the changes in organization and staffing) at an interval of one year (note that the shadowing only takes place in one living area per care home). To control for general trends in the dynamic LTC setting and the effects of the revision of Paragraph 113 Section C of the German Social Code, Part 11 (that enacted a new finance of staffing ratios based on Algorithm 1.0 ) 10 nursing homes from the intervention group are matched with ten nursing homes in the control group by state, number of beds, and ownership. The control group undergoes the surveys of the summative evaluation but not the shadowing nor the two intervention measures. A (cluster) randomization was not possible due to the stipulations of the statutory order.

The summative evaluation assesses the effects of the intervention on (i) quality of care, (ii) job satisfaction, and (iii) changes in the difference between (a) the number of provided interventions, (b) the duration of the provided interventions, and (c) the extent of qualification-fitting. Table 2 provides an overview of the data collection methods for the endpoints of the summative evaluation.

Four different approaches are applied to assess the quality of care. A secondary data analysis of obligatory quality indicators will be conducted as a first step. In Germany, since 2022, these indicators have been reported publicly in every nursing home. As shown in Table  2 , two different instruments were used: firstly, there will be an analysis of the 15 publicly reported self-assessed quality indicators. Secondly, the publicly reported results from the qualification audits from assessors and evaluators on behalf of the LTC insurance funds (i.e., Medizinische Dienste/medicproof GmbH) will be analyzed. These external quality audits collect data on 15 items in 4 themes (Table  2 ).

Secondly, paper-based surveys of nursing home residents will be carried out based on the ASCOT questionnaire (Adult Social Care Outcomes Toolkit) on their subjective quality of life [ 21 , 22 ] and the EQ-5D about health-related quality of life in an evaluated German version [ 23 ]. The ASCOT is a care-related quality-of-life instrument with a high construct validity in the given setting [ 24 ]. To be able to record changes perceived by nursing home residents due to project-specific interventions (e.g., personnel and organizational changes in the care facility) that are not recorded in the ASCOT and the EQ-5D questionnaires, questions specially formulated for the project are added to the questionnaire (e.g., on satisfaction with organization and communication, perception of turnover of nursing home staff, etc.). In the t 1 survey, further questions are included to identify the effect of the change, e.g., ‘Has anything changed as a result of the restructuring in the last year?’. For cognitively impaired nursing home residents, a proxy rating is provided via the QUALIDEM questionnaire [ 25 , 26 ]. In German, the QUALIDEM is available in two versions according to the severity of cognitive decline (measured by the nursing home staff using the Global Deterioration Scale [ 27 ]). While QUALIDEM I can be applied in nursing home residents with mild cognitive decline (GDS Stadium 2–6), QUALIDEM II is used in residents with severe cognitive decline (GDS Stadium 7). The subscales of the QUALIDEM are shown in Table  2 . The written survey and the proxy rating are planned for all nursing home residents living for at least four weeks in one of the participating nursing homes in both the intervention and the control group. The eligibility criteria are stated above. The surveys will be carried out in all living areas of the respective nursing homes.

Thirdly, empirical data will be collected in one living area of each nursing home of the intervention group in an observational study (‘shadowing’). The methodology was developed in the first PeBeM study and described in the final report [ 15 ]. The three elements of the data collection method are described in the background section. To assess actual-target differences (and their changes from t 0 to t 1 ), the shadowing data will be analyzed about (i) the number of interventions provided, (ii) the duration of the interventions provided, and (iii) the extent of qualification fitting. The determination of the respecting values is shown in Table  3 .

Fourthly, a partly standardized online questionnaire will be developed to assess the shadower's perception of the cooperation and communication structures within the nursing homes. The survey will be created based on experience from the PeBeM1 project.

Online surveys of the nursing staff will be conducted to assess job satisfaction. The standardized instruments used are the EXQ (Employee Experience Questionnaire) and the StressBarometer [ 28 , 29 ]. The EXQ consists of four dimensions that address different aspects of job satisfaction. The two dimensions chosen for the study are i) overall job satisfaction and ii) organizational commitment. The other two dimensions addressing individual and collective engagement were excluded to minimize the time spent filling in the questionnaire and to increase the chances for participation in the survey. The EXQ was chosen because it is a standardized and evaluated instrument of good psychometrical quality (Cronbach’s alpha = 0.79—0.91, McDonald’s omega = 0.77 – 0.91), and it is easy to understand even if one is not a native speaker [ 28 ]. Furthermore, one of the dimensions directly addresses organizational commitment. As the intervention group in this study changes the work organization, these parameters are highly interesting for evaluating the intervention.

The StressBarometer was developed by the Federal Institute for Occupational Safety and Health (Bundesanstalt für Arbeitsschutz und Arbeitsmedizin – BauA) to record and assess psychological workloads within a job and was evaluated for many branches [ 29 ]. This instrument was chosen because it is easily understandable for non-native speakers. The utilization of the instrument in this study and the customization of the questionnaire (three subscales were selected) was permitted by the holder of rights (trade union ‘IG Metall’). The satisfaction in correlation to the restructuring of the work organization will be evaluated retrospectively at the facilities of the intervention group. The questions will be formulated during the project, taking into account the measures implemented for personnel and organizational development along with the implementation and service outcomes of the Proctor framework [ 30 ].

The focus groups, of which four are planned, are conducted after the other evaluation results have been analyzed. The focus groups aim to investigate (i) beneficial and inhibiting factors for the implementation process, (ii) positive and negative experiences during the process, and (iii) whether any practicable problem-solving scenarios can be taken into account when refining the algorithm. The guidelines for the focus groups will be based on the evaluation results and can, therefore, only be developed after the t 1 survey.

Prototypical concepts, strategy for a national rollout, and Algorithm 2.0

The study has three superordinated aims. The first is the participatory development of prototypical concepts for qualification-oriented work organization. According to individual nursing home characteristics like the care organization model, competency profiles of the staff, or the extent of digitalization, typed concept variants will be derived. The prototypical concepts thus consider different starting points of nursing homes and offer different development paths for various nursing homes. The definitive attributes that distinguish nursing homes with comparable preconditions will be analyzed using the iterative formal evaluation. These concepts, after rollout, can serve as templates for any nursing home from those to choose the most appropriate. Once one of the typed concepts is selected, any nursing home is free for individual adaptation. Due to the small sample size, the methodology for the analysis must be qualitative. However, the structure of the formative evaluation will take shape during the project. The final nursing home-typed concepts include procedures for a standardized census of the actual status as well as instruments and materials that depend on the prototype of the nursing home. The second final objective of the study aims to incorporate all the results and findings from project steps 1–4 and the contemporaneous participatory co-creative processes to parameterize Algorithm 2.0 . Data from the shadowing (particularly the number of staff and data on the targeted duration of specific tasks and procedures) are analyzed to refine the algorithm. New aspects compared to Algorithm   1.0 will be (i) the incorporation of QLs 5 + and other professions in the calculation and (ii) target time values for indirect care and occasional care interventions. The third final objective is to develop a strategy for the national rollout. Methodological and substantive aspects for developing the nationwide rollout must be created during the project. The method under consideration is focus groups.

Statistical analysis

No data will be collected during the development of an initial implementation concept, and no statistical analysis will be conducted. In the second work package, the actual status of the nursing homes, e.g., the infrastructure, the resident structure, the organization of the nursing homes, technical equipment, and the competencies of the staff, will be depicted. Information regarding the actual status will be analyzed descriptively.

The summative evaluation first analyzes the results of the mandatory external quality assessments descriptively. The analysis examines the development of the quality of care over the intervention period (t 0 , t 1 ) in a pre-post comparison, both for the pertinent nursing homes and the control group, and the differences in the trends between them (difference-in-difference approach). Secondly, a descriptive analysis of the surveys on nursing home residents will be carried out. The analysis of the job satisfaction survey is carried out for the two subscales of the EXQ according to the method specified in the instrument. The subscales of the StressBarometer are not analyzed according to the specified scoring for workload but only in a descriptive comparison of the aggregated subscales and the individual items between the intervention and the control group and, in particular, for the indirect measurement of change in the intervention group (pre-post comparison). The analysis will examine changes in job satisfaction in t 1 compared to t 0 and if these changes differ between the intervention and control groups. Stratified evaluations are planned according to occupational groups and QLs. The answers to the questions on direct change measurement (t 1 survey) will be analyzed quantitatively as far as possible; qualitative data will be summarized narratively if necessary. From the shadowing data of the intervention group, rates of the number and duration of the (non-)performance of the planned and the performed interventions are calculated (actual and targeted quantitative aspects of care). Furthermore, the change between t 0 and t 1 will be shown. Moreover, the analysis will examine the extent of qualification-oriented work organization and the change between t 0 and t 1 (actual and targeted qualitative aspects of care). The survey of the shadowers will be evaluated descriptively concerning the quality of care, possible causes of any deficits observed, and perceived changes due to the intervention.

The formative evaluation will be analyzed qualitatively due to the small sample size.

In Germany, in July 2023, a revision of Paragraph 113 Section C of the German Social Code, Part 11, was enacted to reform the refinancing of staffing in LTC. The calculation of staffing ratios applied in this paragraph is based on Algorithm 1.0 , which resulted from the PeBeM1 study. During the ongoing dynamic process of legislation regarding the further development of the LTC sector, the PeBeM3 study now investigates a combination of staffing calculation and work organization to generate an evidence-based strategy for elaborating the care system. Socio-cultural and economic challenges in the organization and provision of LTC affect most of Europe. Thus, developing new work organization and resource distribution strategies is relevant for further developing healthcare systems. Furthermore, a qualification-oriented work organization has yet to be developed and evaluated in the given setting. It could be transferred to and implemented in other healthcare systems. Therefore, the study's results will be considerable for nursing sciences – not just in Germany. For experts in different countries, the study will hopefully serve as a template to restructure LTC financing or work organization.


As the duration of the intervention is short for the immense changes in the organizational structure and processes, the positive effect of the intervention may be underestimated. Furthermore, the restricted budget allowed us to select only ten facilities in the intervention group. This implies that we could only involve some German states. As the final selection resides by the funder, selection bias could occur. In those ten facilities, the shadowing will be performed only in one of the living areas, so the transferability of the results to the whole facility may be restricted. The transferability of the customized concepts is limited as the number of participating nursing homes is restricted to ten. However, the prototypical concepts developed for the national rollout are open for individual adaptation and thus can be applied in every German nursing home.

Availability of data and materials

The resulting datasets will be available exclusively for scientific purposes from the principal and funding body GKV-Spitzenverband (Spitzenverband Bund der Krankenkassen—National Association of Statutory Health Insurance Funds) upon reasonable request.


Daily intervention planning ( tagesstrukturierte Interventionsplanung )

Employee Experience Questionnaire

GKV-Spitzenverband ( Spitzenverband Bund der Krankenkassen -National Association of Statutory Health Insurance Funds)

Long-term care

Acronym for the German word Personalbemessung , referring to the PeBeM1 study ‘Development of a scientifically based procedure for the standardized calculation of staffing requirements in long term-care’ and the PeBeM3, which is the subject of the study protocol presented here

Qualification Level ( Qualifikationsniveau )

Qualification mis model ( Qualifikations-Mix-Modell )

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Our special thanks go to the participating nursing homes, nursing home residents, nursing home staff, legal guardians, and Vicki May for proofreading the manuscript. Furthermore, we are grateful to the members of the study team in the collaborating institutes for the cooperation.

Open Access funding enabled and organized by Projekt DEAL. Spitzenverband Bund der Krankenkassen. The funder will decide on the final selection of the participating nursing homes.

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C.B. wrote the main manuscript text, prepared Fig.  1 and the tables. I.D.F., C.S.2, C.S.W., M.Z. and H.R. substantially designed the work. C.S.1. contributed to draft the summative evaluation section, and H.R. substantively revised the work. All authors reviewed the manuscript and approved it for submission.

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This study was approved by the Ethics Committee of the German Association of Nursing Science (Deutsche Gesellschaft für Pflegewissenschaft) on 02.08.2023 (amended on 20.09.2023), application no. 23–013. Participation in the study is voluntary and based on informed consent. Before participating, informed consent will be obtained from all the study participants. Research findings are disseminated through presentations at national and international conferences and publications in peer-reviewed scientific journals.

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Burfeindt, C., Darmann-Finck, I., Stammann, C. et al. Study protocol for the development, trial, and evaluation of a strategy for the implementation of qualification-oriented work organization in nursing homes. BMC Nurs 23 , 201 (2024). https://doi.org/10.1186/s12912-024-01883-3

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  • Organizational development

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Four antenatal care visits by four months of pregnancy and four vital tests for pregnant mothers: impact of a community-facility health systems strengthening intervention in Migori County, Kenya

  • Yussif Alhassan 2 ,
  • Lilian Otiso 1 ,
  • Linet Okoth 1 ,
  • Lois Murray 2 ,
  • Charlotte Hemingway 2 ,
  • Joseph M. Lewis 4 ,
  • Mandela Oguche 1 ,
  • Vicki Doyle 2 ,
  • Nelly Muturi 3 ,
  • Emily Ogwang 5 ,
  • Hellen C. Barsosio 6 &
  • Miriam Taegtmeyer 4 , 7  

BMC Pregnancy and Childbirth volume  24 , Article number:  224 ( 2024 ) Cite this article

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Early attendance at antenatal care (ANC), coupled with good-quality care, is essential for improving maternal and child health outcomes. However, achieving these outcomes in sub-Saharan Africa remains a challenge. This study examines the effects of a community-facility health system strengthening model (known as 4byFour) on early ANC attendance, testing for four conditions by four months of pregnancy, and four ANC clinic visits in Migori county, western Kenya.

We conducted a mixed methods quasi-experimental study with a before-after interventional design to assess the impact of the 4byFour model on ANC attendance. Data were collected between August 2019 and December 2020 from two ANC hospitals. Using quantitative data obtained from facility ANC registers, we analysed 707 baseline and 894 endline unique ANC numbers (attendances) based on negative binomial regression. Logistic regression models were used to determine the impact of patient factors on outcomes with Akaike Information Criterion (AIC) and likelihood ratio testing used to compare models. Regular facility stock checks were undertaken at the study sites to assess the availability of ANC profile tests. Analysis of the quantitative data was conducted in R v4.1.1 software. Additionally, qualitative in-depth interviews were conducted with 37 purposively sampled participants, including pregnant mothers, community health volunteers, facility staff, and senior county health officials to explore outcomes of the intervention. The interview data were audio-recorded, transcribed, and coded; and thematic analysis was conducted in NVivo.

There was a significant 26% increase in overall ANC uptake in both facilities following the intervention. Early ANC attendance improved for all age groups, including adolescents, from 22% (baseline) to 33% (endline, p  = 0.002). Logistic regression models predicting early booking were a better fit to data when patient factors were included (age, parity, and distance to clinic, p  = 0.004 on likelihood ratio testing), suggesting that patient factors were associated with early booking.The proportion of women receiving all four tests by four months increased to 3% (27/894), with haemoglobin and malaria testing rates rising to 8% and 4%, respectively. Despite statistical significance ( p  < 0.001), the rates of testing remained low. Testing uptake in ANC was hampered by frequent shortage of profile commodities not covered by buffer stock and low ANC attendance during the first trimester. Qualitative data highlighted how community health volunteer-enhanced health education improved understanding and motivated early ANC-seeking. Community pregnancy testing facilitated early detection and referral, particularly for adolescent mothers. Challenges to optimal ANC attendance included insufficient knowledge about the ideal timing for ANC initiation, financial constraints, and long distances to facilities.

The 4byFour model of community-facility health system strengthening has the potential to improve early uptake of ANC and testing in pregnancy. Sustained improvement in ANC attendance requires concerted efforts to improve care quality, consistent availability of ANC commodities, understand motivating factors, and addressing barriers to ANC. Research involving randomised control trials is needed to strengthen the evidence on the model’s effectiveness and inform potential scale up.

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Attending at least four antenatal care (ANC) visits is essential for good maternal and child health outcomes, especially when accompanied by good quality of care [ 1 ]. Testing and early management of common antenatal conditions reduce the risks of maternal mortality and morbidity, stillbirth, low birthweight, pre-term delivery and HIV transmission [ 2 , 3 , 4 , 5 ]. The WHO 2016 ANC guidelines recommend starting care in the first trimester of pregnancy (12 weeks) for full ANC benefits, including HIV, anaemia, syphilis, malaria tests (in endemic zones), and supplements [ 6 ].

In Migori county, western Kenya, where this study was conducted, ANC attendance remains suboptimal despite high malaria endemicity and high HIV prevalence [ 7 ]. The county performed poorly compared to national standards in most maternal, newborn, and child health indicators. According to the 2022 Kenya Demographic and Health Survey [ 8 ], only 59% of women (aged 15–49) attended the recommended four ANC visits, even as the WHO now recommends eight ANC contacts for all pregnant women [ 6 ]; only 31% of women (aged 15–49) self-presented early enough (within the first 12 weeks of gestation) to fully benefit from testing and treatment for common pregnancy-related conditions. The delayed uptake of ANC, coupled with inconsistent availability of testing commodities limit the benefits for those who do attend, leading to delayed diagnoses of HIV, syphilis, anaemia, and malaria [ 9 , 10 , 11 , 12 ]. Teenage pregnancy is a concerning issue in the region, with 1 in 5 pregnant women being adolescents, who are less likely to seek timely ANC [ 8 ].

In sub-Saharan Africa, various factors contribute to delayed ANC uptake and failure to achieve the recommended number of visits, such as low knowledge of ANC benefits, stigma, financial constraints, fear of judgment/mistreatment, delayed pregnancy recognition, and limited access to quality ANC services [ 13 , 14 ]. A baseline assessment conducted at the dispensary level in Siaya county, western Kenya, revealed low testing rates for malaria and anaemia (27.8%), and moderate rates for syphilis (4.3%) among ANC attendees in 2017, while HIV testing rates were almost universal (99%). However, the subsequent integration of point-of-care testing and consistent supply of testing commodities in the same sites in 2018 significantly improved completion rates for all four tests to over 95%, and ensured appropriate management for those requiring treatment [ 11 ]. This increase was achieved without disrupting existing antenatal HIV testing services or impacting waiting times or staff workload; however, late presentation remained concerning [ 15 , 16 ].

Despite more women in sub-Saharan Africa now presenting for ANC at least once during pregnancy [ 6 , 17 , 18 ], interventions have been limited in improving early initiation, achieving four or more visits, and improving service quality. Mbuagbaw et al. [ 19 ] conducted a systematic review on the effects of health system and community interventions on ANC coverage. They identified various interventions used in low- and middle-income countries, such as financial incentives, mass media campaigns, community mobilisation, information-education‐communication, home visits by community health workers, behaviour change strategies, and policy change initiatives. However, only a few of these interventions effectively increased ANC coverage, with no single approach standing out. Since 2013, the Kenyan government has implemented free maternity policies to enhance maternal health service utilisation. Evidence indicates mixed effects of these initiatives on maternal health services, underscoring the need to combine such interventions with others addressing demand-side barriers to care and challenges in service delivery [ 20 ]. Community health volunteers (CHVs) with basic literacy and government-approved training play a crucial role in delivering maternal and child health services in Kenya by providing health promotion advice and referring pregnant mothers to ANC services during home visits [ 10 ]. Supporting CHVs in their role can lead to increased ANC uptake. For example, providing community health workers with free home pregnancy tests in a randomised controlled trial in Madagascar significantly improved pregnancy care by enabling early pregnancy confirmation and antenatal counselling [ 21 ]. Similar interventions employing quality improvement (QI) approaches at the community level in Kenya have improved skilled delivery and ANC attendance rates [ 22 , 23 , 24 ].

Our study aims to contribute to the discussion on effective interventions to improve the uptake and quality of ANC. This paper reports on a community health system strengthening model (called 4byFour) to increase ANC utilisation and quality. The model combines buffer stock supply and point-of-care testing for ANC, community pregnancy testing, and quality improvement strategies at the community-facility level to improve the quality and coverage of ANC. We assessed the feasibility and effects of the model on early ANC attendance, four ANC visits, and testing for four conditions by four months in Migori county, western Kenya.

Study design

We employed a mixed-methods quasi-experimental study with a before-after design, utilising unmatched quantitative analysis to assess the effect of the 4byFour model on the uptake of ANC and testing by four months of pregnancy, based on routine facility register data. Exploratory qualitative data was collected to enhance understanding of the findings. Our design was guided by process evaluation principles for complex interventions [ 25 , 26 ], adopting a concurrent approach for triangulation through simultaneous collection of quantitative and qualitative data [ 27 ].

Study setting and timeline

The 4ByFour model was co-developed and piloted with QI teams in two ANC facilities and their linked 6 community health units in Migori county. Migori is a predominantly rural county in western Kenya with 8 sub-counties and approximately 117 community units serving a population of about 1.1 million in 2019 [ 28 ]. The county was purposively selected on the basis of high maternal morbidity and low proportion of women attending ANC in the first trimester of pregnancy (21%) [ 17 , 29 ]; and due to well-established links with the County Health Management Team and previous experience with community QI approaches in the sub-counties. Suna West sub-county was purposefully chosen by the county team for the pilot project because it had experienced previous QI programs. Site selection criteria included a high patient flow; a larger, and at least one smaller, site; as well as a site with previous QI experience. The research team conducted a situational analysis using a standard checklist in the sub-county to identify suitable sites. Arombe and God Kwer met the criteria with four and two referring community units respectively; each saw 90–120 ANC attendances per month; and both had functional community-facility QI teams. God Kwer was more rural than Arombe which was on a major road. Baseline data were collected between August-December 2019; the intervention was implemented in a phased approach with interruptions as a result of COVID-19 lockdowns between March and June 2020; endline data was collected between August and December 2020.

Description of intervention: the 4byFour model

The 4byFour model was a community health QI approach designed to address gaps in both the demand and supply sides of the health system. The model name 4byFour describes its target of four tests (syphilis, anaemia, malaria and HIV) by four months (of pregnancy) and four (ANC) visits for all women [ 30 ]. The model was co-developed and piloted with QI teams in two ANC facilities and their linked six community health units in Migori county. Project resources were directed towards strengthening integrated point-of-care testing at the facility, community pregnancy testing and strengthening the community-facility linkage through community-facility quality work improvement teams (WITs). Traditional facility-based QI approaches were adapted to the community level to ensure they were simple, jargon-free and could be understood and implemented by integrated teams of community health volunteers and health facility staff. This adapting of QI has been suggested to be the missing piece in QI efforts in LMICs [ 31 ]. Community-facility work improvement teams brought together community health volunteers (CHVs); community members; community health assistants (CHAs), who serve as supervisors of CHVs; ANC nurse staff and the facility-in-charge of the link primary care facility. The WITS reviewed data collected at community and facility level monthly, analysed it and used it to prioritise, implement and review appropriate interventions to improve ANC attendance during the intervention. CHVs and their supervisors were trained in pregnancy mapping and the distribution and interpretation of simple urine pregnancy tests at community level [ 21 ]. During the intervention period (Feb - Oct 2020), we provided buffer stocks of rapid diagnostic test kits to the study facilities to enhance their testing capacity and avoid shortages, without disrupting the county government and KEMSA’s supply system. These facilities were equipped with HemoCue machines for haemoglobin measurement, rapid diagnostic test kits for malaria (SD Bioline Malaria Ag p.f/Pan test), and HIV/Syphilis test. Buffer stocks were provided only in the case of stock outs identified through our monthly commodity checks. Laboratory and ANC staff came up with an agreed approach to ensure testing at the point-of-care during the ANC consultation to improve availability and reduce waiting time and to record results accurately in both laboratory and ANC registers. Standard practice was to record only positive malaria results in the ANC paper register and training was given to record both positive and negative malaria tests in a spare column of the register. Supportive supervision was carried out by the sub-county health management team members quarterly to review implementation, data quality and other gaps. The research implementation team provided monthly coaching and mentorship to the WITs.

Study populations and sample size

We included all sequential ANC attendances at the two facilities in our quantitative analysis. Using the Migori estimate prevalence of 21% of women attending ANC prior to 4 months [ 17 ] a significance level of 5% and a power of 80%, we needed to review at least 252 women’s data at baseline and endline to detect at least a 50% relative increase in the uptake of early ANC visits and testing.

Participants for the qualitative study included those directly involved as deliverers and/or beneficiaries of care i.e., pregnant mothers, community health volunteers (CHVs) and their supervisors, the Community Health Assistants (CHAs), facility staff, and senior officials of the Migori County Health Management Team. Pregnant mothers and facility staff were purposively selected from facilities where the quantitative data was abstracted, and sampled based on their experience of the intervention, willingness to participate and ability to provide consent. The CHAs, CHVs were linked to the study facilities and operated within the community health units of the facilities. The pregnant mothers were purposively sampled to represent adolescents (< 19 years) and older adults. They were approached in-person by the researchers as they visited the facility to access ANC or directly in the community. The county health officials, facility staff, and CHV/CHAs were invited (mostly by phone or in-person) to the study based on their role and interviewed if they consented. Sample size was determined by data saturation, deemed to have been reached when no new themes emerged from additional interviews [ 32 ].

Data collection and management


Baseline data were collected from August to December 2019 and endline data collected during the same period in 2020. Data collection was impacted by interruption in intervention implementation by COVID-19 lockdown. As part of routine data collection, each ANC attendee was assigned an ANC number by the healthcare worker who completed the register. The numbers were assigned sequentially to women on their first ANC visit, considering the number of women in attendance, and the month and year of their ANC visit. ANC numbers did not follow any conventions to guarantee uniqueness. Data on ANC attendance, ANC testing, age and parity were extracted from the paper-based routine ANC registers to Microsoft Excel by a research assistant. Electronic data sets were then reviewed by facility staff from both sites until agreement was reached on the accuracy of the data. To extract data on distance to facility, we consulted the CHVs to assign a distance in kilometres to each of the village names in the visitation records. Data were double checked for accuracy. We compared clinical details (parity, age and village name) for each ANC number. For ANC numbers with different clinical details, we reviewed original paper records to make a judgement on whether the clinical details differed and ANC numbers with different clinical details were excluded from the analysis, as were records with blank or ambiguous ANC numbers.


Data were collected through individual interviews to explore the issues in greater depth and enable participants to speak openly [ 33 ]. We conducted in-depth interviews IDIs with pregnant mothers, CHVs, and facility staff at local health facilities, and key informant interviews with senior county health officials at county health offices. The interviews were carried out between November and December 2020 by experienced qualitative researchers with knowledge of the local language, culture and health system. They were conducted face-to-face and in English or Luo; lasted for about 1 h; were audio recorded and complemented with written notes. Semi-structured topic guides were used to inform the interviews; they were piloted and revised iteratively as data collection evolved. Interviews explored issues about ANC attendance, data quality, QI interventions and participants’ perception of the effects and challenges of the 4byfour intervention.

Data analysis

Statistical analysis.

Analysis was conducted in R v4.1.1 [ 34 ]. Descriptive statistics are medians with interquartile ranges or proportions with exact binomial confidence intervals as appropriate. Difference in patient characteristics between baseline and endline was assessed with Fisher’s exact test (categorical variables) or Kruskal-Wallace test (continuous variables). Negative binomial regression was used to test the hypothesis that the number of unique attendees increased from baseline to endline. Regression models were fitted to the number of weekly new attendees separately for the two clinics. We assessed the proportion of pregnant women who had first ANC visit before 16 weeks gestation; who had all four tests before 16 weeks gestation and who had 4 ANC visits before 36 weeks gestation. Logistic regression modelling was used to correct for the following a priori selected covariates: study period, clinic, age, parity and distance to clinic. We modelled the impact of patient factors on outcomes. A model including study period (baseline or endline) and clinic only as a covariate for each outcome was compared to a model including study period, clinic and all patient covariates described above (age, parity and distance to clinic) using likelihood ratio testing and the Akaike Information Criterion (AIC). A p-value < 0.05 and a lower AIC for the model including patient factors was interpreted as meaning patient factors explain some variability in outcome. Analysis of receipt of four tests was restricted to endline participants (because no participant at baseline received all four tests), and the study period variable was not included.

Qualitative analysis

Interviews were transcribed using a denaturalised approach and checked for accuracy and completeness [ 35 ]. The Luo interviews were translated into English. Data was analysed in Nvivo12 based on thematic framework approach. We first developed a coding framework based on a review of a sample of the transcripts, which was piloted and revised. Using the coding framework each transcript was systematically analysed to identify relevant codes, categories, and themes. An initial analysis of the quantitative data enabled the analysis to capture relevant qualitative data needed to triangulate emerging quantitative findings, including the perceived reasons for the increase in early ANC attendance, access to ANC test, and barriers to uptake of 4 ANC visits. Emerging findings were discussed among authors, feedback was obtained and subsequently integrated into the analysis.

There were 787 unique ANC numbers at baseline and 949 at endline. Among these, 80 baseline and 55 endline ANC numbers were excluded because they included participants with the same numbers but with different clinical details. This resulted in 707 baseline and 894 endline participants included in the analysis. Table  1 presents the case mix at baseline for the two clinics. Arombe had a younger age profile, but the median parity [ 1 ] and gravidae [ 2 ] were the same at both clinics, with more multiparous women attending Godkwer. Most women booked their first visit after 27 weeks gestation, and this was more common in Arombe. A minority of women (28%) attended four or more visits, and this pattern was similar at both clinics.

Early ANC attendance

There was a statistically significant 26% increase in overall uptake of ANC across both clinics (Arombe 369 to 494 attendees IRR 1.5 [95% CI 1.1-2.0, p  = 0.008], Godkwer 338 to 400 IRR 1.3 [95% CI 1.0-1.7, p  = 0.048]) with more women attending for first visit before 16 weeks’ gestation: 22% (79/359) at baseline compared to 33% (119/365) at endline ( p  = 0.002) (Table  2 ). This increase was seen across all age groups including adolescents: 18% (21/109) of adolescents attended before 16 weeks at baseline and 32% (32/99) at endline ( p  = 0.025) (Table  2 ).

The increase remained after correcting for changing case mix from baseline to endline in a logistic regression model as shown in Table  3 (aOR 1.69 [95% CI 1.11–2.50], p  = 0.015). The logistic regression models including patient factors (age, parity and distance) were a better fit to the data (AIC 530.2 for patient-factor model vs. 537.8, p  = 0.004 on likelihood ratio testing) suggesting patient factors are associated with early booking, despite the fact that the confidence intervals of the estimates of odds ratios crossed 1.

A total of 37 participants took part in the qualitative interviews. The qualitative data suggested an improved understanding of the benefits of early ANC among women after CHV visit, resulting in enhanced motivation to present early for ANC. Pregnant women reported receiving ANC education from CHVs, and many demonstrated awareness of the benefits of early ANC. Participants reported increased early detection and referral of pregnant mothers due to the community pregnancy testing, resulting in early ANC initiation: “ previously, we could only refer obvious pregnant mothers, when the pregnancy is showing, about 30 weeks gestation…. Now we can identify them early and encourage them to start early. The [pregnancy] kits have really helped (CHV, Arombe). Several women said they were encouraged to attend ANC if a referral was backed by a positive pregnancy test: “You feel it is urgent [to attend ANC] if the CHV tests and finds that you are positive.” (Pregnant mother, < 18 years, Arombe). CHVs noted younger women, especially primigravida, were more receptive to the message of early ANC attendance compared with older women with previous pregnancy experience. The former appeared to be motivated by ANC testing and the need to keep their baby safe; they perceived a greater sense of insecurity and were more easily persuaded to visit ANC as a way of mitigating these risks. The latter felt they were experienced at pregnancy and childbirth. Some perceived the ANC test and iron supplements were not necessary since they had had them in their previous pregnancy.

“ The young women are eager to go; if you tell them they start clinic. But the older women feel like they can even give birth at home by themselves” (CHV, Masara).

While women were aware of the benefits of ANC attendance some did not know the ideal gestational time for first ANC visit and the benefits of early attendance. Many still believed ANC attendance was only needed when they were ill or had experienced health challenges in their previous pregnancy: “ Coming early depends on how you are feeling and might feel that you need to go to the clinic. …you are not feeling sick or anything therefore you feel there is no need to start early”. (Pregnant mother, 18 + years, GodKwer). Women presented late to avoid having to make many follow-on visits due to financial constraints and distance.

“Now that we have the kits, if you confirm her pregnancy at an early stage, they fear coming to the facility because they are required to attend clinics until delivery… some stay very far away from the facility like myself who uses fifty shillings for transport, they deem that as costly if started at an earlier stage.” (CHV, Masara).

Availability of ANC profile tests

The project’s buffer stock improved the erratic ANC test profile supply from the national system. From February to October 2020, the project supplied more HB cuvettes, HIV/Syp DUO Kits, and Rapid Syphilis Kits than the national system (Table  4 ). The project supplied fewer mRDTs, causing stockouts of 41 and 53 days in Arombe and Godkwer, respectively prior to Buffer stock distributions. A 20-day stockout of HB cuvettes occurred mainly in Arombe, while Godkwer had none partly due to the project’s buffer stock. The national system did not supply any Rapid Syphilis Kits, leaving the project as the only source of 100 kits; both facilities faced 120 days of stockout for this commodity (Table  4 ).

Four tests by four months

At baseline no women had received all four tests by four months (16 weeks) (Table  5 ). Following the intervention and supply of buffer stocks this had increased to 3% (29/894). The proportion of women receiving haemoglobin and malaria testing increased to 8% and 4% respectively. These were significant increases ( p  < 0.001) but remained low due to insufficient profile tests not covered by the buffer.

There was an overall increase in women testing driven by the increased malaria and haemoglobin testing (Fig. 1).

figure 1

Proportion of participants receiving ANC tests at any gestation stratified by clinic

We carried out a post-hoc analysis of receipt of 4 tests at any gestation and showed the same pattern. No women at any gestation were recorded as having received all 4 tests at baseline and 148/894 (17%) women were recorded as receiving 4 tests at any gestation at endline. Providing enough buffer stock could have boosted test uptake significantly. Patient factors of age, parity and distance from clinic were not associated with testing (AIC for patient-factor logistic regression model 417.8 vs. 415.3, p  = 0.315 on likelihood ratio testing, with odd ratios of effect size crossing 1 as before) (Table  6 ).

Our qualitative interviews revealed the importance of a reliable supply of ANC commodities. Stockout of ANC profile commodities not covered by our buffer stock was widely reported and attributed to erratic supply by the County government. Apart from the HIV/Syphilis duo kit, the malaria RDT, syphilis rapid tests, and HemoCue cuvettes which had been out of stock for periods ranging from 2 to over 6 months when checked.

“ What has not worked well for me is the supply of ANC kits.… there is no regular supply of these kits from the County government and there is nothing you can do about it. At least when there is 4byFour program going around I will not have some of this problem challenge. I wish the County government will take charge and learn from what 4byFour is doing (Facility staff, Arombe).

Integrated point-of-care testing was hampered by inadequate space to administer the test outside of a laboratory. Many MCH units were too small and lacked the privacy to carry out some of the tests at consultation, such as HIV and syphilis: “ Testing at the point-of-care is a good idea but the challenge for us is the space and lack of privacy” (Facility staff, GodKwer). Further, respondents reported limited availability of MCH and laboratory staff and training on point-of-care testing, leading to delays in testing turnaround time. Other concerns related to regular power blackouts with no backup which meant laboratory tests could not be conducted.

Monthly physical checks of stock for tests and recommended treatments for each condition revealed consistent supplies for HIV testing only, with inconsistent supply from the county stores of syphilis (of HIV/syphilis duo), malaria rapid tests and the absence of cuvettes for point-of-care haemoglobin tests (using HemoCue). Drug stockouts were common. While antiretrovirals were consistently available, simple treatments including iron and folate were often unavailable.

Four or more ANC visits

Our 4byFour model did not impact the proportion attending 4 ANC visits in pregnancy among those who would reach 36 weeks gestation during the study period (Table  7 ) and we found no association of 4 or more visits with patient factors (AIC 765.2 for patient factors model vs. 764.4, p  = 0.156).

Some providers perceived an increase in women making fourth or more ANC visits, attributed to starting ANC earlier. Other reasons included increased CHVs monitoring (and nudging) pregnant mothers and potential increased awareness of ANC benefits.

“ The uptake [of first ANC] has increased but the other… the 4th, 5th and 6th ANCs those have not been coming so much. Like when a mother has come for even 3 ANCs then they are not bothered to come for the next ones….” (CHA, GodKwer). “When they start in the first month, they get many appointments, so they are able to go many times before their delivery time is due. … we visit them and remind them.” (CHV, Arombe).

Interview data suggests women’s motivation decreased once they have finished taking the scheduled test and drugs in earlier visits. Women perceived no need to visit once the tests/supplements have been completed, especially if no health issues have been diagnosed. Additional factors included distance to the facility and lack of money for transport:

“… they also say that “when I go there, I am going to wait for so long and after all I have gone 3 times and I didn’t have complications, I have taken IFAS and I am fine” (Pregnant mother, 18 + years, GodKwer). “ It is too far, and I can’t be going every month. If I go first, second and then wait closer to delivery I go again…. I have no money to travel there all the time ” (Pregnant mother, < 18 years, Masara).

This study assessed the feasibility and effects of the 4byFour model on early ANC attendance, four ANC visits, and four ANC tests by four months in Migori county. The model integrated existing health system models and offers a unique methodology for applying them in real life settings, advancing from ‘improvement science’ to ‘implementation science’. We found the community components of the intervention, involving pregnancy mapping, enhanced health education and referral by CHVs, significantly increased early ANC attendance among women of all ages, including adolescents. The facility-level intervention, involving buffer stock supply and point-of-care testing, increased testing overall but only marginally for women receiving four ANC tests by four months as this was determined by early attendance. The model had no effect on the proportion of women attending four or more ANC visits. The study did not yield sufficient evidence to evaluate the contribution of community-facility work improvement teams on QI and ANC uptake.

The improvement in early ANC attendance associated with community pregnancy testing and enhanced counselling and referral of pregnant mothers by CHVs is consistent with other studies showing CHW interventions increase ANC attendance [ 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Similar to the findings of Comfort et al. in Madagascar [ 43 ], our study demonstrates that CHVs’ distribution of pregnancy tests not only improves early detection and referral for initiating ANC at facilities but also appeared to enhance the reputation and credibility of CHVs as primary care providers. Examining these secondary effects on CHWs and their roles, as well as the socio-cultural effects on clients and communities in future research, will enrich understanding of community-based pregnancy testing. Our data also reveals the limitations of solely increasing pregnancy testing access and acknowledges other barriers to ANC utilisation that require attention. Important demand side factors such as age, parity and distance affected early ANC attendance despite the interventions, as older, multi-parous women often did not see the need to present early for ANC having gone through previous pregnancies successfully and were conscious of costs and time involved in ANC visits. Similar to findings in Uganda [ 44 ] and Rwanda [ 45 ], a multi-country study in Ghana, Kenya and Malawi found parity and age had complex impacts on ANC initiation [ 46 ]. Primigravidae were more likely to seek care early once aware of their pregnancy but less likely to recognise early pregnancy [ 46 ]. Similar to our findings, several studies have found adolescents and unmarried women delay ANC attendance due to stigma, unwanted or unplanned pregnancy or the desire to terminate the pregnancy [ 47 ]. In some communities, superstitious beliefs limit women reporting for early ANC as they do not want to disclose pregnancy status before 12 weeks for fear of pregnancy loss or curse/witchcraft [ 47 , 48 , 49 ]. This indicates the importance of a sensitive approach by community health workers with community pregnancy testing, counselling and referral. Implementing pregnancy testing initiatives alongside efforts to address other demand and supply-side barriers is crucial for maximum impact.

The model’s failure to improve attendance for four or more ANC visits suggests that solely increasing early ANC initiation, while proven to enhance the odds of having four ANC visits in certain cases [ 50 ], is insufficient to ensure consistent or four ANC visit attendance. Accessibility challenges, such as distances to facilities and financial constraints, were widely reported to affect subsequent ANC visits after initiation and aligns with findings across LMIC contexts [ 44 , 45 ]. Behavioural factors, such as women’s limited understanding of the preventive value of ANC and the benefits of follow-on attendance (beyond the first ANC visit), were equally pertinent. Cultural, spiritual beliefs, personal issues, and variable ANC service quality in health facilities can impact ANC attendance [ 51 , 52 ]. Quality of care factors, including infrastructure, commodities, supplies, and health worker skills and attitudes, affect ANC visits in most LMIC contexts [ 53 ]. Inequalities in care quality have been noted in certain settings, indicating their potential impact on disparities in ANC attendance. A study in Kenya found the youngest, poorest, least educated, most disadvantaged, and most disempowered women are most likely to report poor experiences of care [ 54 ]. This suggests sustained patient-centred QI efforts are needed to address health inequalities and improve ANC attendance. While Kenya’s Linda Mama initiative offers free maternal and child health services, coverage is incomplete, and it does not cover transportation costs [ 55 ]. Decentralising ANC by training community health workers to provide low-risk antenatal care at the community level, such as distribution of IFAS and IPTp, and pregnancy testing, could reduce the distance barriers [ 41 ].

The low uptake of four test by four months partly results from poor ANC attendance in the first trimester, when most tests were done as per national guidelines. Additionally, we observed major procurement and supply chain issues for anaemia testing, malaria rapid tests and iron/folate supplements, which may have hindered the model’s impact on early and 4 ANC visits. County stockouts prevented four tests from being done, which discouraged women from attending subsequent ANC visits. Even when test commodities were available, other factors such as human resource shortages, lab testing, and inconsistent recording of malaria results limited the effect of the increased commodity availability for test uptake. Lab tests were affected by power blackouts, while point-of-care tests were affected by lack of privacy and confidentiality. HIV testing and antiretroviral therapy were consistently high and unaffected by the intervention, indicating their support from vertical programmes compared to other ANC elements. Stockouts of essential commodities are a significant challenge in ANC and highlight the fragmentation of supply systems along vertical disease programmes [ 56 ]. Several studies have reported that commodity stockouts discourage pregnant women from attending ANC in Africa [ 57 , 58 , 59 ], although there is limited evidence on the effects of buffer stock interventions on ANC attendance. Nonetheless, our provision of buffer stock for essential commodities improved ANC test uptake and quality care by smoothing out stock issues, demonstrating the critical importance of sustained commodity availability in ANC utilisation beyond donor funded projects and research. Buffer stock alone could have produced similar intervention outcomes. Thus, effective ANC requires integrated supply chains to ensure availability of core primary care essential commodities [ 60 , 61 ]. Core treatment for common conditions such as anaemia or malaria may be overlooked by top-down programmes from large multilateral organisations, as seen in studies in Tanzania, Zambia and elsewhere where the well-funded HIV program reduced ANC clinic attendance and testing of other conditions [ 50 , 62 , 63 ].

Similar to prior findings [ 64 ], our study identified significant data quality issues, including incomplete and inaccurate ANC registers, a lack of unique patient identification for tracking, data fragmentation among registers, and disconnected health data between community and facility levels. Digitised approaches to data collection at both community and facility levels could potentially address these challenges, but long-term sustainability beyond project funding is imperative [ 65 ]. The Kenyan MoH has recognised the potential of digitised health data to tackle data quality concerns, culminating in the launch of a costed strategy to guide a fully national electronic Community Health Information System (eCHIS), piloted in Kisumu County [ 66 ] and now being rolled out across the country. Establishing community-based ANC to complement facility-based digital ANC records and creating sustainable linkage between these platforms are essential steps to help Kenya achieve WHO’s ambitious goal of eight ANC contacts.

The QI approach of the 4byFour pilot was shown to work to improve CHV pregnancy testing, referral and linkage to health facilities (demand side). The intervention was based on a health system strengthening approach and focused on improving existing systems and resources to optimise ANC service delivery, rather than introduce new elements. During the 4byFour pilot, the local implementing partner, research team, county health team, community health volunteers and facilities worked together to co-design the intervention aiming to work within and maximise the existing capacity of the system to promote sustained quality improvement. However, it faced numerous sustainability challenges of testing procurement and supply chain, workforce capacity, and intersecting vertical programs demonstrating the need to effectively address both supply and demand side factors to effectively achieve ANC outcomes. Sustainability of QI interventions beyond project funding is essential to strengthen health systems and deliver lasting improvement in maternal and child health outcomes [ 67 ].

Strengths, limitations, and future research

This study offers valuable insights into the potential effects of combining various health system strengthening approaches on antenatal care attendance, while providing useful insights on the individual components of the model. However, it has some limitations. The before-after design limits our ability to rule out other factors that may have caused the observed changes from baseline to endline. Data quality issues from paper-based ANC registers extraction may compromise data reliability, despite data review by facility staff. Budget constraints hindered the buffer stock intervention from addressing all essential commodity stockouts, possibly affecting the model’s effectiveness. The cross-sectional design limits causal inferences from participants’ experiences. Future research using longitudinal and randomized controlled trials will enhance the evidence on the model’s impact. Moreover, a cost-effectiveness analysis and an examination of contextual factors influencing the model’s outcomes will be useful in informing future scale-up efforts. There is the need for innovative approaches to assess the potential effect of the QI component of the model on ANC uptake.

This study demonstrates the potential of the 4byFour model to improve ANC coverage in resource-poor health systems. The model increased ANC uptake, especially early ANC attendance among all age groups, including adolescents who usually engage less in care during pregnancy. The model also improved essential ANC testing for malaria, HIV, syphilis, and haemoglobin. Community pregnancy testing and buffer stock provision of ANC profile tests had particularly promising results. The findings suggest that the 4byFour model and its components, such as community pregnancy testing and buffer stock provision of ANC commodities, can be used to tackle low and delayed ANC uptake and quality issues. Sustained improvement in ANC attendance requires a concerted effort to improve quality of care and availability of ANC commodities, understand motivating factors and barriers to ANC, and promote incentives for horizontal investment in health system strengthening that prioritises integrated patient-centred care over fragmented verticalisation. Further research using longitudinal and randomised control trials is needed to strengthen the evidence on the model’s effectiveness and scale up.

Data availability

Data are available from the corresponding author on request.


Antenatal Care

Community Health Assistants

Community Health Volunteers (now known as Community Health Promoters)

Human Immunodeficiency Virus

Low-and Middle-Income Countries

  • Quality improvement

World Health Organization

Work Improvement Teams

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The authors express their gratitude to all the study participants. We are also grateful to the Migori County Health Management Team, particularly to Boniface Olalo, for facilitating lab training of CHVs. The work would not have been possible without the staff of Arombe and God Kwer Health Centres; and the able research assistants. We are particularly grateful to Mr Jared Odaro whose extra support to the data collection and learning event were much appreciated.

This project was funded through an MRC Public Health Intervention Development (PHIND) award.

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All authors contributed to the study. LO, MT, LM, YA, LO, MO, NM, CH and VD conceptualised and designed the study; LO, MT, LM and YA conducted literature review. YA, LM, MO, NM and JL supervised data collection and analysed the data which were interpreted by MT, LO, EO, VD, JL, CH, NM, MO, LO, YA LM and LO. YA, LO, LM, MT and JL drafted the manuscript, and all authors critically reviewed the draft. All authors read and approved the final manuscript.

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The study was conducted in compliance with the World Medical Association Helsinki Declaration on ethical conduct of research involving human subjects. All participants were informed about the purpose, risks, benefits and procedures of the study and written informed consent was obtained prior to data collection. Informed consent to participate was taken from parents/legal guardians of minor participants. The study was approved and granted ethical clearance from the Liverpool School of Tropical Medicine Research Ethics Committee (Research Protocol (19–077)), the AMREF Ethics Committee (AMREF – ESRC P707/2019) and the National Commission for Science Technology and Innovation (NACOSTI), (NACOSTI/P/19/2366).

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Alhassan, Y., Otiso, L., Okoth, L. et al. Four antenatal care visits by four months of pregnancy and four vital tests for pregnant mothers: impact of a community-facility health systems strengthening intervention in Migori County, Kenya. BMC Pregnancy Childbirth 24 , 224 (2024). https://doi.org/10.1186/s12884-024-06386-2

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The human side of generative AI: Creating a path to productivity

Ever since OpenAI’s ChatGPT exploded into public view in late 2022, the possibilities of generative AI (gen AI) have captured imaginations throughout the business world.

When it comes to crafting an effective talent strategy, organizations have focused mostly on how gen AI can increase productivity levels. This is understandable, given the trillions in value at stake . However, it may not be the most strategic approach. To match the right talent to jobs, leaders first must understand how gen AI is changing the way employees view their work experience. 1 Generative AI is a form of AI that can generate text, images, and other content in response to user prompts. The technology differs from previous versions of AI, in part, because of the scope of outputs it can create.

McKinsey recently surveyed a cross-section of employees as part of our continuing research into how organizations can improve workforce engagement, retention, and attraction (see sidebar, “About the research”). Respondents provided several intriguing insights that can help organizations as they build gen AI talent capabilities.

  • In any given organization, the pool of gen AI talent is likely broader than many leaders realize—and it’s poised to grow rapidly. This cohort isn’t limited to technical talent such as data scientists, software engineers, and machine learning specialists, important as those roles are. In fact, just 12 percent of our respondents fall into this tech-heavy category of traditional gen AI talent. The vast remainder of respondents, or 88 percent, are in nontechnical jobs that use gen AI for help with rote tasks. These jobs include middle managers, healthcare workers, educators, and administrators, among others (Exhibit 1).
  • Fifty-one percent of respondents in technical and nontechnical roles who identify as gen AI creators and heavy users of the technology say they plan to quit their jobs over the next three to six months. This is sobering news for those executive respondents in the survey who say they want to build gen AI talent in-house; it’s hard to reskill and upskill people when they are looking to leave.
  • Although those who self-identify as heavy users and creators of gen AI represent an in-demand employee group, these workers aren’t staying in jobs or attracted to them because of compensation. In fact, the survey shows that this group strongly emphasizes flexibility and relational factors such as meaningful work, caring leaders, and health and well-being  over pay.
  • Finally, and perhaps most surprising: heavy users and creators of gen AI overwhelmingly feel they need higher-level cognitive and social-emotional skills 2 Higher cognitive skills involve more complex thinking processes; social-emotional skills include effectively managing emotions, interpersonal relations, and personal responsibilities. to do their jobs, more than they need to build technological skills. As workers increasingly use gen AI to tackle more repetitive tasks, the human-centric skills of critical thinking and decision making will become ever more important.

These revelations have broad implications for employers as they try to attract and engage their workforces. Organizations are on the cusp of gen AI pushing either positive or negative change when it comes to the nature of work. Leaders have an opportunity to humanize that work  by deciding where, when, and how their teams use gen AI so that people are freed up from routine tasks to do more creative, collaborative, and innovative thinking. Gen AI talent agrees.

About the research

To continue to understand labor market trends related to employee retention, engagement, and attraction, we surveyed 12,802 workers—9,684 employees and 3,118 employers—across 16 industries in Canada (n = 3,183), the United Kingdom (n = 3,227), and the United States (n = 6,392). We focused on two key subpopulations of interest for leaders; these subpopulations represented 9.93 percent of the overall employee sample: generative AI (gen AI) creators at 1.75 percent (n = 169) and heavy users at 8.19 percent (n = 793). The other category—self-identified gen AI light users—comprised 18.18 percent (n = 1,761) of the sample, leading to a total of 28.12 percent (n = 2,723) of workers who self-identified as creators, heavy users, or light users. Nonusers were 71.88 percent (n = 6,961) of workers. We also surveyed more than 3,000 executives in companies across industries to find out how they expect to close their organization’s gen AI skills gaps over the next two years. The survey was conducted from July 28, 2023, to August 15, 2023.

In this article, we break down crucial segments of workers who are at the forefront of gen AI usage or creation and dig deeper into the job factors and skills they say they need. We then discuss how organizations can enhance productivity by crafting jobs that put people before tech—not the other way around. Companies that set a people-centric talent strategy will give themselves a competitive edge as more workers and jobs are affected by the changes gen AI brings.

The workforce: Who is in the gen AI mix?

If companies are to take advantage of the productivity gains from gen AI, they first must consider the broad range of skills required for its successful deployment across the enterprise .

While there are many categories of workers who can be described as gen AI talent, we focus on four distinct archetypes in our survey based on gen AI use:

Creators: These employees help build the gen AI models for their organizations and develop the tools and interfaces most of us use to interact with these models. Creators (2 percent of employees surveyed) tend to be predominantly software engineers, programmers, and machine learning scientists who develop the tools and interfaces most of us use to interact with gen AI.

Heavy users: These employees use gen AI to help them perform most of their core tasks or to enhance their work functions. Heavy users (8 percent of our sample) include a wide range of workers, from designers who use gen AI to expedite 3D modeling to data scientists who use gen AI to verify the accuracy of their coding language semantics.

Light users: Workers in this category use gen AI to perform less than 50 percent of their primary tasks. Representing about 18 percent of the sample, they include middle managers, educators, and communications professionals. For example, a manager might use gen AI to create meeting notes or to help delegate tasks, while a teacher may use it to innovate classroom activities. Journalists and writers researching topics might use gen AI to give them a baseline of facts or to help write a first draft.

Nonusers: These are individuals who are either unaffected by or unaware of the impact of gen AI on their jobs. Examples in our sample include nurse practitioners and healthcare workers engaged in direct patient care, as well as retail associates whose primary role is face-to-face interactions with customers. Although these employees currently represent about 70 percent of the survey, our expectation is that a majority of nonusers will become light or heavy users as the scope and usage of gen AI changes.

Never just tech

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

People over pay: The job factors that workers value most

The COVID-19 pandemic revealed that for many workers, what they want most from their work experience has fundamentally changed . Employees increasingly value relational elements such as caring leaders and coworkers, as well as support for health and well-being, more than compensation (though pay is always important). In 2021, we saw workers quitting in droves—in fact, 40 percent of respondents across jobs, industries, and geographies  said they planned to quit their jobs in the next three to six months. That figure has since dropped to 34 percent.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Certain worker segments, however, remain a greater flight risk. Of self-identified gen AI creators and heavy users, 51 percent of respondents to our latest survey say they plan to leave in the next three to six months.

Early creators and heavy adopters, in particular, wield power when it comes to job choice and shaping their careers. Many company leaders believe that workers in these groups are leaving at higher rates because they can find better compensation elsewhere. Yet an examination of the employee-value-proposition (EVP) factors that resonate most with these segments busts the myth, once again, that compensation is a primary motivator.

Our survey shows that creators and heavy users prioritize workplace flexibility more than total compensation, and that they are seeking a sense of belonging, care, and reliability within their work community. They stay in their jobs when they are given flexibility, and they leave when they aren’t. The other factors that make them stay are meaningful work, support for health and well-being, reliable and supportive coworkers, and a safe workplace environment. This experience is similar to what most workers want, with one glaring exception: compensation appears much further down the list (Exhibit 2).

McKinsey analysis shows that high disengagement and dissatisfaction rates can cost companies millions of dollars a year . Broadly speaking, addressing why workers stay or go is therefore paramount for companies as the use of gen AI grows.

When we dig deeper into self-identified heavy users and creators who are staying in their jobs, we find that a healthy 72 percent report feeling engaged at work, compared with 63 percent in our total survey sample. However, a worrying 55 percent report clinical levels of burnout, a much higher rate than the global sample of 32 percent. In other words, companies may not be getting the productivity and engagement  they expect from these workers.

These EVP elements also play a big part in steering workers into new positions. For the broader workforce, the top four factors for why people take a job are similar to why they stay. However, for workers who identify as heavy users and creators of gen AI, there is a stronger emphasis on relationships with managers and peers, and on a sense of community more broadly.

Specifically, half say that reliable and supportive people are crucial, and nearly half emphasize the importance of caring and inspiring leaders. Roughly two in five say that meaningful work and an inclusive community are core motivators, even above flexibility, which registered as of primary importance to those staying in their jobs. In contrast to the broader set of workers where compensation is the third most important attractor, for this subpopulation it again ranks seventh as a motivating factor. People won’t come just for the money, and they certainly won’t stay for it (Exhibit 3).

Most wanted: Cognitive and social-emotional skills

As gen AI interaction deepens (moving from nonuse to light use to heavy use), we see a consistent trend among both technical and nontechnical workers: they rate higher cognitive skills as more important than technological skills. Even among the technical workers who identify as gen AI creators, higher cognitive skills, at 59 percent, are rated as more important than technological skills, at 55 percent (Exhibit 4).

Regarding social-emotional skills, two interesting trends emerge. First, most technical talent sees social-emotional skills rise in importance as this group increases its usage of gen AI, while nontechnical talent reports the opposite trend. Second, creators who identify as technical talent report lower importance for social-emotional skills at a similar level to nonusers.

Taken together, it appears that as workers become more heavily involved with gen AI, their focus shifts away from social-emotional skills, unless they are in technical positions. It may be that workers are unaware of how their jobs will change in relation to managing and interacting with other people, particularly regarding the importance of developing crucial social-emotional skills.

The disconnect: Employers want to build gen AI talent mostly in-house

Many companies are striving for the most effective way to solve the supply–demand issue when it comes to gen AI talent. Our survey of executives found that most organizations plan to build their gen AI capabilities internally, through upskilling, reskilling, and redeploying talent, more than by external hiring and contracting (Exhibit 5). Naturally, given the spread of worker archetypes  in organizations and the workforce more broadly, some subpopulations, such as programmers and software engineers, may be best brought in through hiring while other types of workers, such as associates and customer experience specialists, will benefit more from upskilling and reskilling to bridge the gap.

The problem is that if companies want to build gen AI skills with the employees they already have, they need to retain the very people who, according to the survey, have indicated that they plan to leave in the next three to six months.

This gap between what employees say they want in a job and what employers are willing to offer them has been central to the workplace experience since the pandemic erupted. Our talent trends research has found that employees consistently want flexibility and meaningful work , and they want to feel valued and engaged.

When mapping self-identified heavy users and creators of gen AI onto which EVP factors matter most, we see that their emphasis on relational factors is largely the same as our broader survey sample. The need to care for family shows the largest increase in importance, while compensation registers the largest decrease.

Additionally, feeling valued by a manager, having access to development opportunities, and doing meaningful work also show a notable increase in importance. Advancement opportunities, on the other hand, are not as highly valued, suggesting that there are some unique conditions to being in a highly technical job, either through the creation of gen AI or through its heavy use (Exhibit 6).

There is little doubt that gen AI can help increase individual and workforce productivity; it may automate up to 30 percent of business activities across occupations by 2030.

How leaders can close the gap

There is little doubt that gen AI can help increase individual and workforce productivity; McKinsey research suggests it may well automate up to 30 percent of business activities across occupations by 2030 .

Leaders should explore answers to three fundamental questions about their workforces in light of the impact of gen AI:

How can we reimagine jobs to be more human centric? Begin by defining which tasks people should do, which tasks gen AI can do, and how humans should manage other people as well as gen AI usage itself. Technological skills such as coding will be the baseline for many jobs, but social-emotional skills and higher cognitive skills will be the differentiators for creative, collaborative work in the future. Perhaps this means more in-office meetings or other ways for people to engage in the most productive ways they can.

Workers who perform at high levels and inspire others—we call them “thriving stars”—help spur collaboration, innovation, and better decision making . However, they make up as little as 4 percent of organizations. Their scarcity makes it particularly important to place these employees in positions that will boost overall performance.

How can we redefine flexibility? As jobs change, companies will need to look at worker outcomes according to the results achieved, not by hours spent. The benchmark for output will have to shift. For instance, some written code may be longer, but it may not necessarily be better or more user friendly.

With the potential for gen AI to help make jobs more efficient, could an employee’s meaningful work in a given week be completed in as little as 20 hours ? And if that’s the case, is the 40-hour workweek still the benchmark? Rather than filling hours with tasks to get to a specific number in a given week, companies can focus on ways to emphasize the distinctive, creative part of a job that makes it meaningful. Jobs that create the space for the human touch can also help facilitate a more engaged and more productive workforce.

How do we emphasize the right kind of listening? This is a basic concept that many organizations seem to have trouble embracing: talking with employees rather than leading by assumption. Creating a constantly evolving dialogue can help with both problem solving and morale. This is particularly relevant as the gen AI talent pool expands.

Survey respondents overwhelmingly express enthusiasm about the integration of gen AI into their workplaces, though approximately 4 percent say they are concerned about job displacement (rising to 7 percent for workers aged 18 to 24). This undercurrent of worry presents an opportunity for leaders to engage workers about the potential changes gen AI will bring.

To illustrate how these shifts apply to the workforce today, we offer two examples of nontechnical gen AI talent: a communications specialist and a middle manager.

More time for innovation and collaboration

A communications specialist in a large corporation is currently a heavy user of gen AI. Her job has involved interviewing C-suite executives and synthesizing their ideas to create speeches, talking points, emails, and other communications for both internal and external audiences. Her performance has been measured by how many discrete communications she facilitates and the quality of the copy that is produced.

She used to send questions to executives ahead of time and then schedule a series of interviews, which would take several weeks to complete. Now, she can feed their recorded interviews into a gen AI chatbot and get a synthesis of their remarks in seconds.

The communications expert will still review and edit that text, but the overall process is much faster. Whereas before she spent 60 percent of her time synthesizing material, that task now takes only 10 percent of her time, freeing up bandwidth to think strategically about the message the speech is intended to convey and what form of communication would be most effective. She may also have more time to deepen relationships with industry reporters, which could benefit coverage of the company, and to help the chief human resources officer write that book she has been eager to start.

This gen-AI-related efficiency gain leads to increased productivity, more innovative thinking, and welcome face time with key constituencies—good for the employee, her team, and the organization. The value she adds to the job is now fundamentally different.

Managing people, managing gen AI

Now, consider a middle manager at a technology company who identifies as a nontechnical creator of gen AI. Currently, middle managers report spending  almost half of their time on individual-contributor and administrative tasks and only about a quarter of their time on people-related activities. In a gen-AI-enabled world, they could significantly reduce the number of hours spent on non-people-related activities and reallocate that time toward supporting direct reports and engaging in broader strategy concerns.

As teams start using gen AI to help free up their capacity, the middle manager’s job will evolve  to managing both people and the use of this technology to enhance their output. In other words, gen AI will become another member of the team to be managed. And just like a direct report who needs some intensive coaching to get up to speed, gen AI may need more guidance and involvement from managers—at least initially and perhaps for much longer.

Lastly, a core part of the manager’s role will be to ensure the humanization of work. As the nature of tasks and time spent change, and the focus shifts from process oriented to results oriented, managers will be a decisive factor in whether an organization allows gen AI to elevate people’s work. Keeping a finger on the pulse of their teams raises the likelihood that managers will do their part to create jobs that are less abstract and disconnected and more fulfilling and collaborative. To prepare people, managers can encourage employees to recognize the centrality of their insights and creative contributions with respect to the broader organization as gen AI use evolves.

The employer–employee disconnect has led to high levels of workforce discontent, which is affecting workers at the forefront of the gen AI push even more dramatically when it comes to burnout and attrition. Companies that want to capitalize on gen-AI-fueled productivity gains have an opportunity to address this rapidly expanding group’s concerns about the nature of work. Those that emphasize the importance of human skills over a simple race for increased output are likely to earn the loyalty of their workforces and higher performance over the long term.

Aaron De Smet is a senior partner in McKinsey’s New Jersey office, Sandra Durth is a partner in the Cologne office, Bryan Hancock is a partner in the Washington, DC, office, Marino Mugayar-Baldocchi is a research science expert in the New York office, and Angelika Reich is an alumna of the Vienna office.

The authors wish to thank Yueyang Chen and James Paguay for their contributions to this article.

This article was edited by Barbara Tierney, a senior editor in the New York office.

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