comparative analysis of quantitative and qualitative research

What is comparative analysis? A complete guide

Last updated

18 April 2023

Reviewed by

Jean Kaluza

Comparative analysis is a valuable tool for acquiring deep insights into your organization’s processes, products, and services so you can continuously improve them. 

Similarly, if you want to streamline, price appropriately, and ultimately be a market leader, you’ll likely need to draw on comparative analyses quite often.

When faced with multiple options or solutions to a given problem, a thorough comparative analysis can help you compare and contrast your options and make a clear, informed decision.

If you want to get up to speed on conducting a comparative analysis or need a refresher, here’s your guide.

Make comparative analysis less tedious

Dovetail streamlines comparative analysis to help you uncover and share actionable insights

• What exactly is comparative analysis?

A comparative analysis is a side-by-side comparison that systematically compares two or more things to pinpoint their similarities and differences. The focus of the investigation might be conceptual—a particular problem, idea, or theory—or perhaps something more tangible, like two different data sets.

For instance, you could use comparative analysis to investigate how your product features measure up to the competition.

After a successful comparative analysis, you should be able to identify strengths and weaknesses and clearly understand which product is more effective.

You could also use comparative analysis to examine different methods of producing that product and determine which way is most efficient and profitable.

The potential applications for using comparative analysis in everyday business are almost unlimited. That said, a comparative analysis is most commonly used to examine

Emerging trends and opportunities (new technologies, marketing)

Competitor strategies

Financial health

Effects of trends on a target audience

• Why is comparative analysis so important? 

Comparative analysis can help narrow your focus so your business pursues the most meaningful opportunities rather than attempting dozens of improvements simultaneously.

A comparative approach also helps frame up data to illuminate interrelationships. For example, comparative research might reveal nuanced relationships or critical contexts behind specific processes or dependencies that wouldn’t be well-understood without the research.

For instance, if your business compares the cost of producing several existing products relative to which ones have historically sold well, that should provide helpful information once you’re ready to look at developing new products or features.

• Comparative vs. competitive analysis—what’s the difference?

Comparative analysis is generally divided into three subtypes, using quantitative or qualitative data and then extending the findings to a larger group. These include

Pattern analysis —identifying patterns or recurrences of trends and behavior across large data sets.

Data filtering —analyzing large data sets to extract an underlying subset of information. It may involve rearranging, excluding, and apportioning comparative data to fit different criteria. 

Decision tree —flowcharting to visually map and assess potential outcomes, costs, and consequences.

In contrast, competitive analysis is a type of comparative analysis in which you deeply research one or more of your industry competitors. In this case, you’re using qualitative research to explore what the competition is up to across one or more dimensions.

For example

Service delivery —metrics like the Net Promoter Scores indicate customer satisfaction levels.

Market position — the share of the market that the competition has captured.

Brand reputation —how well-known or recognized your competitors are within their target market.

• Tips for optimizing your comparative analysis

Conduct original research

Thorough, independent research is a significant asset when doing comparative analysis. It provides evidence to support your findings and may present a perspective or angle not considered previously. 

Make analysis routine

To get the maximum benefit from comparative research, make it a regular practice, and establish a cadence you can realistically stick to. Some business areas you could plan to analyze regularly include:

Profitability

Competition

Experiment with controlled and uncontrolled variables

In addition to simply comparing and contrasting, explore how different variables might affect your outcomes.

For example, a controllable variable would be offering a seasonal feature like a shopping bot to assist in holiday shopping or raising or lowering the selling price of a product.

Uncontrollable variables include weather, changing regulations, the current political climate, or global pandemics.

Put equal effort into each point of comparison

Most people enter into comparative research with a particular idea or hypothesis already in mind to validate. For instance, you might try to prove the worthwhileness of launching a new service. So, you may be disappointed if your analysis results don’t support your plan.

However, in any comparative analysis, try to maintain an unbiased approach by spending equal time debating the merits and drawbacks of any decision. Ultimately, this will be a practical, more long-term sustainable approach for your business than focusing only on the evidence that favors pursuing your argument or strategy.

Writing a comparative analysis in five steps

To put together a coherent, insightful analysis that goes beyond a list of pros and cons or similarities and differences, try organizing the information into these five components:

1. Frame of reference

Here is where you provide context. First, what driving idea or problem is your research anchored in? Then, for added substance, cite existing research or insights from a subject matter expert, such as a thought leader in marketing, startup growth, or investment

2. Grounds for comparison Why have you chosen to examine the two things you’re analyzing instead of focusing on two entirely different things? What are you hoping to accomplish?

3. Thesis What argument or choice are you advocating for? What will be the before and after effects of going with either decision? What do you anticipate happening with and without this approach?

For example, “If we release an AI feature for our shopping cart, we will have an edge over the rest of the market before the holiday season.” The finished comparative analysis will weigh all the pros and cons of choosing to build the new expensive AI feature including variables like how “intelligent” it will be, what it “pushes” customers to use, how much it takes off the plates of customer service etc.

Ultimately, you will gauge whether building an AI feature is the right plan for your e-commerce shop.

4. Organize the scheme Typically, there are two ways to organize a comparative analysis report. First, you can discuss everything about comparison point “A” and then go into everything about aspect “B.” Or, you alternate back and forth between points “A” and “B,” sometimes referred to as point-by-point analysis.

Using the AI feature as an example again, you could cover all the pros and cons of building the AI feature, then discuss the benefits and drawbacks of building and maintaining the feature. Or you could compare and contrast each aspect of the AI feature, one at a time. For example, a side-by-side comparison of the AI feature to shopping without it, then proceeding to another point of differentiation.

5. Connect the dots Tie it all together in a way that either confirms or disproves your hypothesis.

For instance, “Building the AI bot would allow our customer service team to save 12% on returns in Q3 while offering optimizations and savings in future strategies. However, it would also increase the product development budget by 43% in both Q1 and Q2. Our budget for product development won’t increase again until series 3 of funding is reached, so despite its potential, we will hold off building the bot until funding is secured and more opportunities and benefits can be proved effective.”

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Qualitative Vs. Quantitative Research — A step-wise guide to conduct research

A research study includes the collection and analysis of data. In quantitative research, the data are analyzed with numbers and statistics, and in qualitative research, the data analyzed are non-numerical and perceive the meaning of social reality.

What Is Qualitative Research?

Qualitative research observes and describes a phenomenon to gain a deeper understanding of a subject. It is also used to generate hypotheses for further studies. In general, qualitative research is explanatory and helps understands how an individual perceives non-numerical data, like video, photographs, or audio recordings. The qualitative data is collected from diary accounts or interviews and analyzed by grounded theory or thematic analysis.

When to Use Qualitative Research?

Qualitative research is used when the outcome of the research study is to disseminate knowledge and understand concepts, thoughts, and experiences. This type of research focuses on creating ideas and formulating theories or hypotheses .

Benefits of Qualitative Research

• Unlike quantitative research, which relies on numerical data, qualitative research relies on data collected from interviews, observations, and written texts.
• It is often used in fields such as sociology and anthropology, where the goal is to understand complex social phenomena.
• Qualitative research is considered to be more flexible and adaptive, as it is used to study a wide range of social aspects.
• Additionally, qualitative research often leads to deeper insights into the research study. This helps researchers and scholars in designing their research methods .

Qualitative Research Example

In research, to understand the culture of a pharma company, one could take an ethnographic approach. With an experience in the company, one could gather data based on the —

• Field notes with observations, and reflections on one’s experiences of the company’s culture
• Open-ended surveys for employees across all the company’s departments via email to find out variations in culture across teams and departments
• Interview sessions with employees and gather information about their experiences and perspectives.

What Is Quantitative Research?

Quantitative research is for testing hypotheses and measuring relationships between variables. It follows the process of objectively collecting data and analyzing it numerically, to determine and control variables of interest. This type of research aims to test causal relationships between variables and provide generalized results. These results determine if the theory proposed for the research study could be accepted or rejected.

When to Use Quantitative Research?

Quantitative research is used when a research study needs to confirm or test a theory or a hypothesis. When a research study is focused on measuring and quantifying data, using a quantitative approach is appropriate. It is often used in fields such as economics, marketing, or biology, where researchers are interested in studying trends and relationships between variables .

Benefits of Quantitative Research

• Quantitative data is interpreted with statistical analysis . The type of statistical study is based on the principles of mathematics and it provides a fast, focused, scientific and relatable approach.
• Quantitative research creates an ability to replicate the test and results of research. This approach makes the data more reliable and less open to argument.
• After collecting the quantitative data, expected results define which statistical tests are applicable and results provide a quantifiable conclusion for the research hypothesis
• Research with complex statistical analysis is considered valuable and impressive. Quantitative research is associated with technical advancements like computer modeling and data-based decisions.

Quantitative Research Example

An organization wishes to conduct a customer satisfaction (CSAT) survey by using a survey template. From the survey, the organization can acquire quantitative data and metrics on the brand or the organization based on the customer’s experience. Various parameters such as product quality, pricing, customer experience, etc. could be used to generate data in the form of numbers that is statistically analyzed.

Data Collection Methods

1. qualitative data collection methods.

Qualitative data is collected from interview sessions, discussions with focus groups, case studies, and ethnography (scientific description of people and cultures with their customs and habits). The collection methods involve understanding and interpreting social interactions.

Qualitative research data also includes respondents’ opinions and feelings, which is conducted face-to-face mostly in focus groups. Respondents are asked open-ended questions either verbally or through discussion among a group of people, related to the research topic implemented to collect opinions for further research.

2. Quantitative Data Collection Methods

Quantitative research data is acquired from surveys, experiments, observations, probability sampling, questionnaire observation, and content review. Surveys usually contain a list of questions with multiple-choice responses relevant to the research topic under study. With the availability of online survey tools, researchers can conduct a web-based survey for quantitative research.

Quantitative data is also assimilated from research experiments. While conducting experiments, researchers focus on exploring one or more independent variables and studying their effect on one or more dependent variables.

A Step-wise Guide to Conduct Qualitative and Quantitative Research

• Understand the difference between types of research — qualitative, quantitative, or mixed-methods-based research.
• Develop a research question or hypothesis. This research approach will define which type of research one could choose.
• Choose a method for data collection. Depending on the process of data collection, the type of research could be determined.
• Analyze and interpret the collected data. Based on the analyzed data, results are reported.
• If observed results are not equivalent to expected results, consider using an unbiased research approach or choose both qualitative and quantitative research methods for preferred results.

Qualitative Vs. Quantitative Research – A Comparison

With an awareness of qualitative vs. quantitative research and the different data collection methods , researchers could use one or both types of research approaches depending on their preferred results. Moreover, to implement unbiased research and acquire meaningful insights from the research study, it is advisable to consider both qualitative and quantitative research methods .

Through this article, you would have understood the comparison between qualitative and quantitative research. However, if you have any queries related to qualitative vs. quantitative research, do comment below or email us.

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Qualitative comparative analysis

Qualitative Comparative Analysis (QCA) is a means of analysing the causal contribution of different conditions (e.g. aspects of an intervention and the wider context) to an outcome of interest.

QCA starts with the documentation of the different configurations of conditions associated with each case of an observed outcome. These are then subject to a minimisation procedure that identifies the simplest set of conditions that can account for all the observed outcomes, as well as their absence.

The results are typically expressed in statements expressed in ordinary language or as Boolean algebra. For example:

• A combination of Condition A and condition B or a combination of condition C and condition D will lead to outcome E.
• In Boolean notation this is expressed more succinctly as A*B + C*D→E

QCA results are able to distinguish various complex forms of causation, including:

• Configurations of causal conditions, not just single causes. In the example above, there are two different causal configurations, each made up of two conditions.
• Equifinality, where there is more than one way in which an outcome can happen. In the above example, each additional configuration represents a different causal pathway
• Causal conditions which are necessary, sufficient, both or neither, plus more complex combinations (known as INUS causes – insufficient but necessary parts of a configuration that is unnecessary but sufficient), which tend to be more common in everyday life. In the example above, no one condition was sufficient or necessary. But each condition is an INUS type cause
• Asymmetric causes – where the causes of failure may not simply be the absence of the cause of success. In the example above, the configuration associated with the absence of E might have been one like this: A*B*X + C*D*X →e  Here X condition was a sufficient and necessary blocking condition.
• The relative influence of different individual conditions and causal configurations in a set of cases being examined. In the example above, the first configuration may have been associated with 10 cases where the outcome was E, whereas the second might have been associated with only 5 cases.  Configurations can be evaluated in terms of coverage (the percentage of cases they explain) and consistency (the extent to which a configuration is always associated with a given outcome).

QCA is able to use relatively small and simple data sets. There is no requirement to have enough cases to achieve statistical significance, although ideally there should be enough cases to potentially exhibit all the possible configurations. The latter depends on the number of conditions present. In a 2012 survey of QCA uses the median number of cases was 22 and the median number of conditions was 6.  For each case, the presence or absence of a condition is recorded using nominal data i.e. a 1 or 0. More sophisticated forms of QCA allow the use of “fuzzy sets” i.e. where a condition may be partly present or partly absent, represented by a value of 0.8 or 0.2 for example. Or there may be more than one kind of presence, represented by values of 0, 1, 2 or more for example. Data for a QCA analysis is collated in a simple matrix form, where rows = cases and columns = conditions, with the rightmost column listing the associated outcome for each case, also described in binary form.

QCA is a theory-driven approach, in that the choice of conditions being examined needs to be driven by a prior theory about what matters. The list of conditions may also be revised in the light of the results of the QCA analysis if some configurations are still shown as being associated with a mixture of outcomes. The coding of the presence/absence of a condition also requires an explicit view of that condition and when and where it can be considered present. Dichotomisation of quantitative measures about the incidence of a condition also needs to be carried out with an explicit rationale, and not on an arbitrary basis.

Although QCA was originally developed by Charles Ragin some decades ago it is only in the last decade that its use has become more common amongst evaluators. Articles on its use have appeared in Evaluation and the American Journal of Evaluation.

For a worked example, see Charles Ragin’s What is Qualitative Comparative Analysis (QCA)? ,  slides 6 to 15 on The bare-bones basics of crisp-set QCA.

[A crude summary of the example is presented here]

In his presentation Ragin provides data on 65 countries and their reactions to austerity measures imposed by the IMF. This has been condensed into a Truth Table (shown below), which shows all possible configurations of four different conditions that were thought to affect countries’ responses: the presence or absence of severe austerity, prior mobilisation, corrupt government, rapid price rises. Next to each configuration is data on the outcome associated with that configuration – the numbers of countries experiencing mass protest or not. There are 16 configurations in all, one per row. The rightmost column describes the consistency of each configuration: whether all cases with that configuration have one type of outcome, or a mixed outcome (i.e. some protests and some no protests). Notice that there are also some configurations with no known cases.

Ragin’s next step is to improve the consistency of the configurations with mixed consistency. This is done either by rejecting cases within an inconsistent configuration because they are outliers (with exceptional circumstances unlikely to be repeated elsewhere) or by introducing an additional condition (column) that distinguishes between those configurations which did lead to protest and those which did not. In this example, a new condition was introduced that removed the inconsistency, which was described as  “not having a repressive regime”.

The next step involves reducing the number of configurations needed to explain all the outcomes, known as minimisation. Because this is a time-consuming process, this is done by an automated algorithm (aka a computer program) This algorithm takes two configurations at a time and examines if they have the same outcome. If so, and if their configurations are only different in respect to one condition this is deemed to not be an important causal factor and the two configurations are collapsed into one. This process of comparisons is continued, looking at all configurations, including newly collapsed ones, until no further reductions are possible.

[Jumping a few more specific steps] The final result from the minimisation of the above truth table is this configuration:

SA*(PR + PM*GC*NR)

The expression indicates that IMF protest erupts when severe austerity (SA) is combined with either (1) rapid price increases (PR) or (2) the combination of prior mobilization (PM), government corruption (GC), and non-repressive regime (NR).

This slide show from Charles C Ragin, provides a detailed explanation, including examples, that clearly demonstrates the question, 'What is QCA?'

This book, by Schneider and Wagemann, provides a comprehensive overview of the basic principles of set theory to model causality and applications of Qualitative Comparative Analysis (QCA), the most developed form of set-theoretic method, for research ac

This article by Nicolas Legewie provides an introduction to Qualitative Comparative Analysis (QCA). It discusses the method's main principles and advantages, including its concepts.

COMPASSS (Comparative methods for systematic cross-case analysis) is a website that has been designed to develop the use of systematic comparative case analysis  as a research strategy by bringing together scholars and practitioners who share its use as

This paper from Patrick A. Mello focuses on reviewing current applications for use in Qualitative Comparative Analysis (QCA) in order to take stock of what is available and highlight best practice in this area.

Marshall, G. (1998). Qualitative comparative analysis. In A Dictionary of Sociology Retrieved from https://www.encyclopedia.com/social-sciences/dictionaries-thesauruses-pictures-and-press-releases/qualitative-comparative-analysis

Expand to view all resources related to 'Qualitative comparative analysis'

• An introduction to applied data analysis with qualitative comparative analysis
• Qualitative comparative analysis: A valuable approach to add to the evaluator’s ‘toolbox’? Lessons from recent applications

'Qualitative comparative analysis' is referenced in:

• 52 weeks of BetterEvaluation: Week 34 Generalisations from case studies?
• Week 18: is there a "right" approach to establishing causation in advocacy evaluation?

Framework/Guide

• Rainbow Framework :  Check the results are consistent with causal contribution
• Data mining

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© 2022 BetterEvaluation. All right reserved.

• Open access
• Published: 07 May 2021

The use of Qualitative Comparative Analysis (QCA) to address causality in complex systems: a systematic review of research on public health interventions

• Benjamin Hanckel 1 ,
• Mark Petticrew 2 ,
• James Thomas 3 &
• Judith Green 4  

BMC Public Health volume  21 , Article number:  877 ( 2021 ) Cite this article

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Qualitative Comparative Analysis (QCA) is a method for identifying the configurations of conditions that lead to specific outcomes. Given its potential for providing evidence of causality in complex systems, QCA is increasingly used in evaluative research to examine the uptake or impacts of public health interventions. We map this emerging field, assessing the strengths and weaknesses of QCA approaches identified in published studies, and identify implications for future research and reporting.

PubMed, Scopus and Web of Science were systematically searched for peer-reviewed studies published in English up to December 2019 that had used QCA methods to identify the conditions associated with the uptake and/or effectiveness of interventions for public health. Data relating to the interventions studied (settings/level of intervention/populations), methods (type of QCA, case level, source of data, other methods used) and reported strengths and weaknesses of QCA were extracted and synthesised narratively.

The search identified 1384 papers, of which 27 (describing 26 studies) met the inclusion criteria. Interventions evaluated ranged across: nutrition/obesity ( n  = 8); physical activity ( n  = 4); health inequalities ( n  = 3); mental health ( n  = 2); community engagement ( n  = 3); chronic condition management ( n  = 3); vaccine adoption or implementation ( n  = 2); programme implementation ( n  = 3); breastfeeding ( n  = 2), and general population health ( n  = 1). The majority of studies ( n  = 24) were of interventions solely or predominantly in high income countries. Key strengths reported were that QCA provides a method for addressing causal complexity; and that it provides a systematic approach for understanding the mechanisms at work in implementation across contexts. Weaknesses reported related to data availability limitations, especially on ineffective interventions. The majority of papers demonstrated good knowledge of cases, and justification of case selection, but other criteria of methodological quality were less comprehensively met.

QCA is a promising approach for addressing the role of context in complex interventions, and for identifying causal configurations of conditions that predict implementation and/or outcomes when there is sufficiently detailed understanding of a series of comparable cases. As the use of QCA in evaluative health research increases, there may be a need to develop advice for public health researchers and journals on minimum criteria for quality and reporting.

Peer Review reports

Interest in the use of Qualitative Comparative Analysis (QCA) arises in part from growing recognition of the need to broaden methodological capacity to address causality in complex systems [ 1 , 2 , 3 ]. Guidance for researchers for evaluating complex interventions suggests process evaluations [ 4 , 5 ] can provide evidence on the mechanisms of change, and the ways in which context affects outcomes. However, this does not address the more fundamental problems with trial and quasi-experimental designs arising from system complexity [ 6 ]. As Byrne notes, the key characteristic of complex systems is ‘emergence’ [ 7 ]: that is, effects may accrue from combinations of components, in contingent ways, which cannot be reduced to any one level. Asking about ‘what works’ in complex systems is not to ask a simple question about whether an intervention has particular effects, but rather to ask: “how the intervention works in relation to all existing components of the system and to other systems and their sub-systems that intersect with the system of interest” [ 7 ]. Public health interventions are typically attempts to effect change in systems that are themselves dynamic; approaches to evaluation are needed that can deal with emergence [ 8 ]. In short, understanding the uptake and impact of interventions requires methods that can account for the complex interplay of intervention conditions and system contexts.

To build a useful evidence base for public health, evaluations thus need to assess not just whether a particular intervention (or component) causes specific change in one variable, in controlled circumstances, but whether those interventions shift systems, and how specific conditions of interventions and setting contexts interact to lead to anticipated outcomes. There have been a number of calls for the development of methods in intervention research to address these issues of complex causation [ 9 , 10 , 11 ], including calls for the greater use of case studies to provide evidence on the important elements of context [ 12 , 13 ]. One approach for addressing causality in complex systems is Qualitative Comparative Analysis (QCA): a systematic way of comparing the outcomes of different combinations of system components and elements of context (‘conditions’) across a series of cases.

The potential of qualitative comparative analysis

QCA is an approach developed by Charles Ragin [ 14 , 15 ], originating in comparative politics and macrosociology to address questions of comparative historical development. Using set theory, QCA methods explore the relationships between ‘conditions’ and ‘outcomes’ by identifying configurations of necessary and sufficient conditions for an outcome. The underlying logic is different from probabilistic reasoning, as the causal relationships identified are not inferred from the (statistical) likelihood of them being found by chance, but rather from comparing sets of conditions and their relationship to outcomes. It is thus more akin to the generative conceptualisations of causality in realist evaluation approaches [ 16 ]. QCA is a non-additive and non-linear method that emphasises diversity, acknowledging that different paths can lead to the same outcome. For evaluative research in complex systems [ 17 ], QCA therefore offers a number of benefits, including: that QCA can identify more than one causal pathway to an outcome (equifinality); that it accounts for conjectural causation (where the presence or absence of conditions in relation to other conditions might be key); and that it is asymmetric with respect to the success or failure of outcomes. That is, that specific factors explain success does not imply that their absence leads to failure (causal asymmetry).

QCA was designed, and is typically used, to compare data from a medium N (10–50) series of cases that include those with and those without the (dichotomised) outcome. Conditions can be dichotomised in ‘crisp sets’ (csQCA) or represented in ‘fuzzy sets’ (fsQCA), where set membership is calibrated (either continuously or with cut offs) between two extremes representing fully in (1) or fully out (0) of the set. A third version, multi-value QCA (mvQCA), infrequently used, represents conditions as ‘multi-value sets’, with multinomial membership [ 18 ]. In calibrating set membership, the researcher specifies the critical qualitative anchors that capture differences in kind (full membership and full non-membership), as well as differences in degree in fuzzy sets (partial membership) [ 15 , 19 ]. Data on outcomes and conditions can come from primary or secondary qualitative and/or quantitative sources. Once data are assembled and coded, truth tables are constructed which “list the logically possible combinations of causal conditions” [ 15 ], collating the number of cases where those configurations occur to see if they share the same outcome. Analysis of these truth tables assesses first whether any conditions are individually necessary or sufficient to predict the outcome, and then whether any configurations of conditions are necessary or sufficient. Necessary conditions are assessed by examining causal conditions shared by cases with the same outcome, whilst identifying sufficient conditions (or combinations of conditions) requires examining cases with the same causal conditions to identify if they have the same outcome [ 15 ]. However, as Legewie argues, the presence of a condition, or a combination of conditions in actual datasets, are likely to be “‘quasi-necessary’ or ‘quasi-sufficient’ in that the causal relation holds in a great majority of cases, but some cases deviate from this pattern” [ 20 ]. Following reduction of the complexity of the model, the final model is tested for coverage (the degree to which a configuration accounts for instances of an outcome in the empirical cases; the proportion of cases belonging to a particular configuration) and consistency (the degree to which the cases sharing a combination of conditions align with a proposed subset relation). The result is an analysis of complex causation, “defined as a situation in which an outcome may follow from several different combinations of causal conditions” [ 15 ] illuminating the ‘causal recipes’, the causally relevant conditions or configuration of conditions that produce the outcome of interest.

QCA, then, has promise for addressing questions of complex causation, and recent calls for the greater use of QCA methods have come from a range of fields related to public health, including health research [ 17 ], studies of social interventions [ 7 ], and policy evaluation [ 21 , 22 ]. In making arguments for the use of QCA across these fields, researchers have also indicated some of the considerations that must be taken into account to ensure robust and credible analyses. There is a need, for instance, to ensure that ‘contradictions’, where cases with the same configurations show different outcomes, are resolved and reported [ 15 , 23 , 24 ]. Additionally, researchers must consider the ratio of cases to conditions, and limit the number of conditions to cases to ensure the validity of models [ 25 ]. Marx and Dusa, examining crisp set QCA, have provided some guidance to the ‘ceiling’ number of conditions which can be included relative to the number of cases to increase the probability of models being valid (that is, with a low probability of being generated through random data) [ 26 ].

There is now a growing body of published research in public health and related fields drawing on QCA methods. This is therefore a timely point to map the field and assess the potential of QCA as a method for contributing to the evidence base for what works in improving public health. To inform future methodological development of robust methods for addressing complexity in the evaluation of public health interventions, we undertook a systematic review to map existing evidence, identify gaps in, and strengths and weakness of, the QCA literature to date, and identify the implications of these for conducting and reporting future QCA studies for public health evaluation. We aimed to address the following specific questions [ 27 ]:

1. How is QCA used for public health evaluation? What populations, settings, methods used in source case studies, unit/s and level of analysis (‘cases’), and ‘conditions’ have been included in QCA studies?

2. What strengths and weaknesses have been identified by researchers who have used QCA to understand complex causation in public health evaluation research?

3. What are the existing gaps in, and strengths and weakness of, the QCA literature in public health evaluation, and what implications do these have for future research and reporting of QCA studies for public health?

This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 29 April 2019 ( CRD42019131910 ). A protocol was prepared in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 statement [ 28 ], and published in 2019 [ 27 ], where the methods are explained in detail. EPPI-Reviewer 4 was used to manage the process and undertake screening of abstracts [ 29 ].

Search strategy

We searched for peer-reviewed published papers in English, which used QCA methods to examine causal complexity in evaluating the implementation, uptake and/or effects of a public health intervention, in any region of the world, for any population. ‘Public health interventions’ were defined as those which aim to promote or protect health, or prevent ill health, in the population. No date exclusions were made, and papers published up to December 2019 were included.

Search strategies used the following phrases “Qualitative Comparative Analysis” and “QCA”, which were combined with the keywords “health”, “public health”, “intervention”, and “wellbeing”. See Additional file  1 for an example. Searches were undertaken on the following databases: PubMed, Web of Science, and Scopus. Additional searches were undertaken on Microsoft Academic and Google Scholar in December 2019, where the first pages of results were checked for studies that may have been missed in the initial search. No additional studies were identified. The list of included studies was sent to experts in QCA methods in health and related fields, including authors of included studies and/or those who had published on QCA methodology. This generated no additional studies within scope, but a suggestion to check the COMPASSS (Comparative Methods for Systematic Cross-Case Analysis) database; this was searched, identifying one further study that met the inclusion criteria [ 30 ]. COMPASSS ( https://compasss.org/ ) collates publications of studies using comparative case analysis.

We excluded studies where no intervention was evaluated, which included studies that used QCA to examine public health infrastructure (i.e. staff training) without a specific health outcome, and papers that report on prevalence of health issues (i.e. prevalence of child mortality). We also excluded studies of health systems or services interventions where there was no public health outcome.

After retrieval, and removal of duplicates, titles and abstracts were screened by one of two authors (BH or JG). Double screening of all records was assisted by EPPI Reviewer 4’s machine learning function. Of the 1384 papers identified after duplicates were removed, we excluded 820 after review of titles and abstracts (Fig.  1 ). The excluded studies included: a large number of papers relating to ‘quantitative coronary angioplasty’ and some which referred to the Queensland Criminal Code (both of which are also abbreviated to ‘QCA’); papers that reported methodological issues but not empirical studies; protocols; and papers that used the phrase ‘qualitative comparative analysis’ to refer to qualitative studies that compared different sub-populations or cases within the study, but did not include formal QCA methods.

Flow Diagram

Full texts of the 51 remaining studies were screened by BH and JG for inclusion, with 10 papers double coded by both authors, with complete agreement. Uncertain inclusions were checked by the third author (MP). Of the full texts, 24 were excluded because: they did not report a public health intervention ( n  = 18); had used a methodology inspired by QCA, but had not undertaken a QCA ( n  = 2); were protocols or methodological papers only ( n  = 2); or were not published in peer-reviewed journals ( n  = 2) (see Fig.  1 ).

Data were extracted manually from the 27 remaining full texts by BH and JG. Two papers relating to the same research question and dataset were combined, such that analysis was by study ( n  = 26) not by paper. We retrieved data relating to: publication (journal, first author country affiliation, funding reported); the study setting (country/region setting, population targeted by the intervention(s)); intervention(s) studied; methods (aims, rationale for using QCA, crisp or fuzzy set QCA, other analysis methods used); data sources drawn on for cases (source [primary data, secondary data, published analyses], qualitative/quantitative data, level of analysis, number of cases, final causal conditions included in the analysis); outcome explained; and claims made about strengths and weaknesses of using QCA (see Table  1 ). Data were synthesised narratively, using thematic synthesis methods [ 31 , 32 ], with interventions categorised by public health domain and level of intervention.

Quality assessment

There are no reporting guidelines for QCA studies in public health, but there are a number of discussions of best practice in the methodological literature [ 25 , 26 , 33 , 34 ]. These discussions suggest several criteria for strengthening QCA methods that we used as indicators of methodological and/or reporting quality: evidence of familiarity of cases; justification for selection of cases; discussion and justification of set membership score calibration; reporting of truth tables; reporting and justification of solution formula; and reporting of consistency and coverage measures. For studies using csQCA, and claiming an explanatory analysis, we additionally identified whether the number of cases was sufficient for the number of conditions included in the model, using a pragmatic cut-off in line with Marx & Dusa’s guideline thresholds, which indicate how many cases are sufficient for given numbers of conditions to reject a 10% probability that models could be generated with random data [ 26 ].

Overview of scope of QCA research in public health

Twenty-seven papers reporting 26 studies were included in the review (Table  1 ). The earliest was published in 2005, and 17 were published after 2015. The majority ( n  = 19) were published in public health/health promotion journals, with the remainder published in other health science ( n  = 3) or in social science/management journals ( n  = 4). The public health domain(s) addressed by each study were broadly coded by the main area of focus. They included nutrition/obesity ( n  = 8); physical activity (PA) (n = 4); health inequalities ( n  = 3); mental health ( n  = 2); community engagement ( n  = 3); chronic condition management ( n  = 3); vaccine adoption or implementation (n = 2); programme implementation ( n  = 3); breastfeeding ( n  = 2); or general population health ( n  = 1). The majority ( n  = 24) of studies were conducted solely or predominantly in high-income countries (systematic reviews in general searched global sources, but commented that the overwhelming majority of studies were from high-income countries). Country settings included: any ( n  = 6); OECD countries ( n  = 3); USA ( n  = 6); UK ( n  = 6) and one each from Nepal, Austria, Belgium, Netherlands and Africa. These largely reflected the first author’s country affiliations in the UK ( n  = 13); USA ( n  = 9); and one each from South Africa, Austria, Belgium, and the Netherlands. All three studies primarily addressing health inequalities [ 35 , 36 , 37 ] were from the UK.

Eight of the interventions evaluated were individual-level behaviour change interventions (e.g. weight management interventions, case management, self-management for chronic conditions); eight evaluated policy/funding interventions; five explored settings-based health promotion/behaviour change interventions (e.g. schools-based physical activity intervention, store-based food choice interventions); three evaluated community empowerment/engagement interventions, and two studies evaluated networks and their impact on health outcomes.

Methods and data sets used

Fifteen studies used crisp sets (csQCA), 11 used fuzzy sets (fsQCA). No study used mvQCA. Eleven studies included additional analyses of the datasets drawn on for the QCA, including six that used qualitative approaches (narrative synthesis, case comparisons), typically to identify cases or conditions for populating the QCA; and four reporting additional statistical analyses (meta-regression, linear regression) to either identify differences overall between cases prior to conducting a QCA (e.g. [ 38 ]) or to explore correlations in more detail (e.g. [ 39 ]). One study used an additional Boolean configurational technique to reduce the number of conditions in the QCA analysis [ 40 ]. No studies reported aiming to compare the findings from the QCA with those from other techniques for evaluating the uptake or effectiveness of interventions, although some [ 41 , 42 ] were explicitly using the study to showcase the possibilities of QCA compared with other approaches in general. Twelve studies drew on primary data collected specifically for the study, with five of those additionally drawing on secondary data sets; five drew only on secondary data sets, and nine used data from systematic reviews of published research. Seven studies drew primarily on qualitative data, generally derived from interviews or observations.

Many studies were undertaken in the context of one or more trials, which provided evidence of effect. Within single trials, this was generally for a process evaluation, with cases being trial sites. Fernald et al’s study, for instance, was in the context of a trial of a programme to support primary care teams in identifying and implementing self-management support tools for their patients, which measured patient and health care provider level outcomes [ 43 ]. The QCA reported here used qualitative data from the trial to identify a set of necessary conditions for health care provider practices to implement the tools successfully. In studies drawing on data from systematic reviews, cases were always at the level of intervention or intervention component, with data included from multiple trials. Harris et al., for instance, undertook a mixed-methods systematic review of school-based self-management interventions for asthma, using meta-analysis methods to identify effective interventions and QCA methods to identify which intervention features were aligned with success [ 44 ].

The largest number of studies ( n  = 10), including all the systematic reviews, analysed cases at the level of the intervention, or a component of the intervention; seven analysed organisational level cases (e.g. school class, network, primary care practice); five analysed sub-national region level cases (e.g. state, local authority area), and two each analysed country or individual level cases. Sample sizes ranged from 10 to 131, with no study having small N (< 10) sample sizes, four having large N (> 50) sample sizes, and the majority (22) being medium N studies (in the range 10–50).

Rationale for using QCA

Most papers reported a rationale for using QCA that mentioned ‘complexity’ or ‘context’, including: noting that QCA is appropriate for addressing causal complexity or multiple pathways to outcome [ 37 , 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]; noting the appropriateness of the method for providing evidence on how context impacts on interventions [ 41 , 50 ]; or the need for a method that addressed causal asymmetry [ 52 ]. Three stated that the QCA was an ‘exploratory’ analysis [ 53 , 54 , 55 ]. In addition to the empirical aims, several papers (e.g. [ 42 , 48 ]) sought to demonstrate the utility of QCA, or to develop QCA methods for health research (e.g. [ 47 ]).

Reported strengths and weaknesses of approach

There was a general agreement about the strengths of QCA. Specifically, that it was a useful tool to address complex causality, providing a systematic approach to understand the mechanisms at work in implementation across contexts [ 38 , 39 , 43 , 45 , 46 , 47 , 55 , 56 , 57 ], particularly as they relate to (in) effective intervention implementation [ 44 , 51 ] and the evaluation of interventions [ 58 ], or “where it is not possible to identify linearity between variables of interest and outcomes” [ 49 ]. Authors highlighted the strengths of QCA as providing possibilities for examining complex policy problems [ 37 , 59 ]; for testing existing as well as new theory [ 52 ]; and for identifying aspects of interventions which had not been previously perceived as critical [ 41 ] or which may have been missed when drawing on statistical methods that use, for instance, linear additive models [ 42 ]. The strengths of QCA in terms of providing useful evidence for policy were flagged in a number of studies, particularly where the causal recipes suggested that conventional assumptions about effectiveness were not confirmed. Blackman et al., for instance, in a series of studies exploring why unequal health outcomes had narrowed in some areas of the UK and not others, identified poorer outcomes in settings with ‘better’ contracting [ 35 , 36 , 37 ]; Harting found, contrary to theoretical assumptions about the necessary conditions for successful implementation of public health interventions, that a multisectoral network was not a necessary condition [ 30 ].

Weaknesses reported included the limitations of QCA in general for addressing complexity, as well as specific limitations with either the csQCA or the fsQCA methods employed. One general concern discussed across a number of studies was the problem of limited empirical diversity, which resulted in: limitations in the possible number of conditions included in each study, particularly with small N studies [ 58 ]; missing data on important conditions [ 43 ]; or limited reported diversity (where, for instance, data were drawn from systematic reviews, reflecting publication biases which limit reporting of ineffective interventions) [ 41 ]. Reported methodological limitations in small and intermediate N studies included concerns about the potential that case selection could bias findings [ 37 ].

In terms of potential for addressing causal complexity, the limitations of QCA for identifying unintended consequences, tipping points, and/or feedback loops in complex adaptive systems were noted [ 60 ], as were the potential limitations (especially in csQCA studies) of reducing complex conditions, drawn from detailed qualitative understanding, to binary conditions [ 35 ]. The impossibility of doing this was a rationale for using fsQCA in one study [ 57 ], where detailed knowledge of conditions is needed to make theoretically justified calibration decisions. However, others [ 47 ] make the case that csQCA provides more appropriate findings for policy: dichotomisation forces a focus on meaningful distinctions, including those related to decisions that practitioners/policy makers can action. There is, then, a potential trade-off in providing ‘interpretable results’, but ones which preclude potential for utilising more detailed information [ 45 ]. That QCA does not deal with probabilistic causation was noted [ 47 ].

Quality of published studies

Assessment of ‘familiarity with cases’ was made subjectively on the basis of study authors’ reports of their knowledge of the settings (empirical or theoretical) and the descriptions they provided in the published paper: overall, 14 were judged as sufficient, and 12 less than sufficient. Studies which included primary data were more likely to be judged as demonstrating familiarity ( n  = 10) than those drawing on secondary sources or systematic reviews, of which only two were judged as demonstrating familiarity. All studies justified how the selection of cases had been made; for those not using the full available population of cases, this was in general (appropriately) done theoretically: following previous research [ 52 ]; purposively to include a range of positive and negative outcomes [ 41 ]; or to include a diversity of cases [ 58 ]. In identifying conditions leading to effective/not effective interventions, one purposive strategy was to include a specified percentage or number of the most effective and least effective interventions (e.g. [ 36 , 40 , 51 , 52 ]). Discussion of calibration of set membership scores was judged adequate in 15 cases, and inadequate in 11; 10 reported raw data matrices in the paper or supplementary material; 21 reported truth tables in the paper or supplementary material. The majority ( n  = 21) reported at least some detail on the coverage (the number of cases with a particular configuration) and consistency (the percentage of similar causal configurations which result in the same outcome). The majority ( n  = 21) included truth tables (or explicitly provided details of how to obtain them); fewer ( n  = 10) included raw data. Only five studies met all six of these quality criteria (evidence of familiarity with cases, justification of case selection, discussion of calibration, reporting truth tables, reporting raw data matrices, reporting coverage and consistency); a further six met at least five of them.

Of the csQCA studies which were not reporting an exploratory analysis, four appeared to have insufficient cases for the large number of conditions entered into at least one of the models reported, with a consequent risk to the validity of the QCA models [ 26 ].

QCA has been widely used in public health research over the last decade to advance understanding of causal inference in complex systems. In this review of published evidence to date, we have identified studies using QCA to examine the configurations of conditions that lead to particular outcomes across contexts. As noted by most study authors, QCA methods have promised advantages over probabilistic statistical techniques for examining causation where systems and/or interventions are complex, providing public health researchers with a method to test the multiple pathways (configurations of conditions), and necessary and sufficient conditions that lead to desired health outcomes.

The origins of QCA approaches are in comparative policy studies. Rihoux et al’s review of peer-reviewed journal articles using QCA methods published up to 2011 found the majority of published examples were from political science and sociology, with fewer than 5% of the 313 studies they identified coming from health sciences [ 61 ]. They also reported few examples of the method being used in policy evaluation and implementation studies [ 62 ]. In the decade since their review of the field [ 61 ], there has been an emerging body of evaluative work in health: we identified 26 studies in the field of public health alone, with the majority published in public health journals. Across these studies, QCA has been used for evaluative questions in a range of settings and public health domains to identify the conditions under which interventions are implemented and/or have evidence of effect for improving population health. All studies included a series of cases that included some with and some without the outcome of interest (such as behaviour change, successful programme implementation, or good vaccination uptake). The dominance of high-income countries in both intervention settings and author affiliations is disappointing, but reflects the disproportionate location of public health research in the global north more generally [ 63 ].

The largest single group of studies included were systematic reviews, using QCA to compare interventions (or intervention components) to identify successful (and non-successful) configurations of conditions across contexts. Here, the value of QCA lies in its potential for synthesis with quantitative meta-synthesis methods to identify the particular conditions or contexts in which interventions or components are effective. As Parrott et al. note, for instance, their meta-analysis could identify probabilistic effects of weight management programmes, and the QCA analysis enabled them to address the “role that the context of the [paediatric weight management] intervention has in influencing how, when, and for whom an intervention mix will be successful” [ 50 ]. However, using QCA to identify configurations of conditions that lead to effective or non- effective interventions across particular areas of population health is an application that does move away in some significant respects from the origins of the method. First, researchers drawing on evidence from systematic reviews for their data are reliant largely on published evidence for information on conditions (such as the organisational contexts in which interventions were implemented, or the types of behaviour change theory utilised). Although guidance for describing interventions [ 64 ] advises key aspects of context are included in reports, this may not include data on the full range of conditions that might be causally important, and review research teams may have limited knowledge of these ‘cases’ themselves. Second, less successful interventions are less likely to be published, potentially limiting the diversity of cases, particularly of cases with unsuccessful outcomes. A strength of QCA is the separate analysis of conditions leading to positive and negative outcomes: this is precluded where there is insufficient evidence on negative outcomes [ 50 ]. Third, when including a range of types of intervention, it can be unclear whether the cases included are truly comparable. A QCA study requires a high degree of theoretical and pragmatic case knowledge on the part of the researcher to calibrate conditions to qualitative anchors: it is reliant on deep understanding of complex contexts, and a familiarity with how conditions interact within and across contexts. Perhaps surprising is that only seven of the studies included here clearly drew on qualitative data, given that QCA is primarily seen as a method that requires thick, detailed knowledge of cases, particularly when the aim is to understand complex causation [ 8 ]. Whilst research teams conducting QCA in the context of systematic reviews may have detailed understanding in general of interventions within their spheres of expertise, they are unlikely to have this for the whole range of cases, particularly where a diverse set of contexts (countries, organisational settings) are included. Making a theoretical case for the valid comparability of such a case series is crucial. There may, then, be limitations in the portability of QCA methods for conducting studies entirely reliant on data from published evidence.

QCA was developed for small and medium N series of cases, and (as in the field more broadly, [ 61 ]), the samples in our studies predominantly had between 10 and 50 cases. However, there is increasing interest in the method as an alternative or complementary technique to regression-oriented statistical methods for larger samples [ 65 ], such as from surveys, where detailed knowledge of cases is likely to be replaced by theoretical knowledge of relationships between conditions (see [ 23 ]). The two larger N (> 100 cases) studies in our sample were an individual level analysis of survey data [ 46 , 47 ] and an analysis of intervention arms from a systematic review [ 50 ]. Larger sample sizes allow more conditions to be included in the analysis [ 23 , 26 ], although for evaluative research, where the aim is developing a causal explanation, rather than simply exploring patterns, there remains a limit to the number of conditions that can be included. As the number of conditions included increases, so too does the number of possible configurations, increasing the chance of unique combinations and of generating spurious solutions with a high level of consistency. As a rule of thumb, once the number of conditions exceeds 6–8 (with up to 50 cases) or 10 (for larger samples), the credibility of solutions may be severely compromised [ 23 ].

Strengths and weaknesses of the study

A systematic review has the potential advantages of transparency and rigour and, if not exhaustive, our search is likely to be representative of the body of research using QCA for evaluative public health research up to 2020. However, a limitation is the inevitable difficulty in operationalising a ‘public health’ intervention. Exclusions on scope are not straightforward, given that most social, environmental and political conditions impact on public health, and arguably a greater range of policy and social interventions (such as fiscal or trade policies) that have been the subject of QCA analyses could have been included, or a greater range of more clinical interventions. However, to enable a manageable number of papers to review, and restrict our focus to those papers that were most directly applicable to (and likely to be read by) those in public health policy and practice, we operationalised ‘public health interventions’ as those which were likely to be directly impacting on population health outcomes, or on behaviours (such as increased physical activity) where there was good evidence for causal relationships with public health outcomes, and where the primary research question of the study examined the conditions leading to those outcomes. This review has, of necessity, therefore excluded a considerable body of evidence likely to be useful for public health practice in terms of planning interventions, such as studies on how to better target smoking cessation [ 66 ] or foster social networks [ 67 ] where the primary research question was on conditions leading to these outcomes, rather than on conditions for outcomes of specific interventions. Similarly, there are growing number of descriptive epidemiological studies using QCA to explore factors predicting outcomes across such diverse areas as lupus and quality of life [ 68 ]; length of hospital stay [ 69 ]; constellations of factors predicting injury [ 70 ]; or the role of austerity, crisis and recession in predicting public health outcomes [ 71 ]. Whilst there is undoubtedly useful information to be derived from studying the conditions that lead to particular public health problems, these studies were not directly evaluating interventions, so they were also excluded.

Restricting our search to publications in English and to peer reviewed publications may have missed bodies of work from many regions, and has excluded research from non-governmental organisations using QCA methods in evaluation. As this is a rapidly evolving field, with relatively recent uptake in public health (all our included studies were after 2005), our studies may not reflect the most recent advances in the area.

Implications for conducting and reporting QCA studies

This systematic review has reviewed studies that deployed an emergent methodology, which has no reporting guidelines and has had, to date, a relatively low level of awareness among many potential evidence users in public health. For this reason, many of the studies reviewed were relatively detailed on the methods used, and the rationale for utilising QCA.

We did not assess quality directly, but used indicators of good practice discussed in QCA methodological literature, largely written for policy studies scholars, and often post-dating the publication dates of studies included in this review. It is also worth noting that, given the relatively recent development of QCA methods, methodological debate is still thriving on issues such as the reliability of causal inferences [ 72 ], alongside more general critiques of the usefulness of the method for policy decisions (see, for instance, [ 73 ]). The authors of studies included in this review also commented directly on methodological development: for instance, Thomas et al. suggests that QCA may benefit from methods development for sensitivity analyses around calibration decisions [ 42 ].

However, we selected quality criteria that, we argue, are relevant for public health research> Justifying the selection of cases, discussing and justifying the calibration of set membership, making data sets available, and reporting truth tables, consistency and coverage are all good practice in line with the usual requirements of transparency and credibility in methods. When QCA studies aim to provide explanation of outcomes (rather than exploring configurations), it is also vital that they are reported in ways that enhance the credibility of claims made, including justifying the number of conditions included relative to cases. Few of the studies published to date met all these criteria, at least in the papers included here (although additional material may have been provided in other publications). To improve the future discoverability and uptake up of QCA methods in public health, and to strengthen the credibility of findings from these methods, we therefore suggest the following criteria should be considered by authors and reviewers for reporting QCA studies which aim to provide causal evidence about the configurations of conditions that lead to implementation or outcomes:

The paper title and abstract state the QCA design;

The sampling unit for the ‘case’ is clearly defined (e.g.: patient, specified geographical population, ward, hospital, network, policy, country);

The population from which the cases have been selected is defined (e.g.: all patients in a country with X condition, districts in X country, tertiary hospitals, all hospitals in X country, all health promotion networks in X province, European policies on smoking in outdoor places, OECD countries);

The rationale for selection of cases from the population is justified (e.g.: whole population, random selection, purposive sample);

There are sufficient cases to provide credible coverage across the number of conditions included in the model, and the rationale for the number of conditions included is stated;

Cases are comparable;

There is a clear justification for how choices of relevant conditions (or ‘aspects of context’) have been made;

There is sufficient transparency for replicability: in line with open science expectations, datasets should be available where possible; truth tables should be reported in publications, and reports of coverage and consistency provided.

Implications for future research

In reviewing methods for evaluating natural experiments, Craig et al. focus on statistical techniques for enhancing causal inference, noting only that what they call ‘qualitative’ techniques (the cited references for these are all QCA studies) require “further studies … to establish their validity and usefulness” [ 2 ]. The studies included in this review have demonstrated that QCA is a feasible method when there are sufficient (comparable) cases for identifying configurations of conditions under which interventions are effective (or not), or are implemented (or not). Given ongoing concerns in public health about how best to evaluate interventions across complex contexts and systems, this is promising. This review has also demonstrated the value of adding QCA methods to the tool box of techniques for evaluating interventions such as public policies, health promotion programmes, and organisational changes - whether they are implemented in a randomised way or not. Many of the studies in this review have clearly generated useful evidence: whether this evidence has had more or less impact, in terms of influencing practice and policy, or is more valid, than evidence generated by other methods is not known. Validating the findings of a QCA study is perhaps as challenging as validating the findings from any other design, given the absence of any gold standard comparators. Comparisons of the findings of QCA with those from other methods are also typically constrained by the rather different research questions asked, and the different purposes of the analysis. In our review, QCA were typically used alongside other methods to address different questions, rather than to compare methods. However, as the field develops, follow up studies, which evaluate outcomes of interventions designed in line with conditions identified as causal in prior QCAs, might be useful for contributing to validation.

This review was limited to public health evaluation research: other domains that would be useful to map include health systems/services interventions and studies used to design or target interventions. There is also an opportunity to broaden the scope of the field, particularly for addressing some of the more intractable challenges for public health research. Given the limitations in the evidence base on what works to address inequalities in health, for instance [ 74 ], QCA has potential here, to help identify the conditions under which interventions do or do not exacerbate unequal outcomes, or the conditions that lead to differential uptake or impacts across sub-population groups. It is perhaps surprising that relatively few of the studies in this review included cases at the level of country or region, the traditional level for QCA studies. There may be scope for developing international comparisons for public health policy, and using QCA methods at the case level (nation, sub-national region) of classic policy studies in the field. In the light of debate around COVID-19 pandemic response effectiveness, comparative studies across jurisdictions might shed light on issues such as differential population responses to vaccine uptake or mask use, for example, and these might in turn be considered as conditions in causal configurations leading to differential morbidity or mortality outcomes.

When should be QCA be considered?

Public health evaluations typically assess the efficacy, effectiveness or cost-effectiveness of interventions and the processes and mechanisms through which they effect change. There is no perfect evaluation design for achieving these aims. As in other fields, the choice of design will in part depend on the availability of counterfactuals, the extent to which the investigator can control the intervention, and the range of potential cases and contexts [ 75 ], as well as political considerations, such as the credibility of the approach with key stakeholders [ 76 ]. There are inevitably ‘horses for courses’ [ 77 ]. The evidence from this review suggests that QCA evaluation approaches are feasible when there is a sufficient number of comparable cases with and without the outcome of interest, and when the investigators have, or can generate, sufficiently in-depth understanding of those cases to make sense of connections between conditions, and to make credible decisions about the calibration of set membership. QCA may be particularly relevant for understanding multiple causation (that is, where different configurations might lead to the same outcome), and for understanding the conditions associated with both lack of effect and effect. As a stand-alone approach, QCA might be particularly valuable for national and regional comparative studies of the impact of policies on public health outcomes. Alongside cluster randomised trials of interventions, or alongside systematic reviews, QCA approaches are especially useful for identifying core combinations of causal conditions for success and lack of success in implementation and outcome.

Conclusions

QCA is a relatively new approach for public health research, with promise for contributing to much-needed methodological development for addressing causation in complex systems. This review has demonstrated the large range of evaluation questions that have been addressed to date using QCA, including contributions to process evaluations of trials and for exploring the conditions leading to effectiveness (or not) in systematic reviews of interventions. There is potential for QCA to be more widely used in evaluative research, to identify the conditions under which interventions across contexts are implemented or not, and the configurations of conditions associated with effect or lack of evidence of effect. However, QCA will not be appropriate for all evaluations, and cannot be the only answer to addressing complex causality. For explanatory questions, the approach is most appropriate when there is a series of enough comparable cases with and without the outcome of interest, and where the researchers have detailed understanding of those cases, and conditions. To improve the credibility of findings from QCA for public health evidence users, we recommend that studies are reported with the usual attention to methodological transparency and data availability, with key details that allow readers to judge the credibility of causal configurations reported. If the use of QCA continues to expand, it may be useful to develop more comprehensive consensus guidelines for conduct and reporting.

Availability of data and materials

Full search strategies and extraction forms are available by request from the first author.

Abbreviations

Comparative Methods for Systematic Cross-Case Analysis

crisp set QCA

fuzzy set QCA

multi-value QCA

Medical Research Council

• Qualitative Comparative Analysis

randomised control trial

Physical Activity

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Acknowledgements

The authors would like to thank and acknowledge the support of Sara Shaw, PI of MR/S014632/1 and the rest of the Triple C project team, the experts who were consulted on the final list of included studies, and the reviewers who provided helpful feedback on the original submission.

This study was funded by MRC: MR/S014632/1 ‘Case study, context and complex interventions (Triple C): development of guidance and publication standards to support case study research’. The funder played no part in the conduct or reporting of the study. JG is supported by a Wellcome Trust Centre grant 203109/Z/16/Z.

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Hanckel, B., Petticrew, M., Thomas, J. et al. The use of Qualitative Comparative Analysis (QCA) to address causality in complex systems: a systematic review of research on public health interventions. BMC Public Health 21 , 877 (2021). https://doi.org/10.1186/s12889-021-10926-2

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The “qualitative” in qualitative comparative analysis (QCA): research moves, case-intimacy and face-to-face interviews

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• Sofia Pagliarin   ORCID: orcid.org/0000-0003-4846-6072 3 , 4 ,
• Salvatore La Mendola 2 &
• Barbara Vis 1  

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Qualitative Comparative Analysis (QCA) includes two main components: QCA “as a research approach” and QCA “as a method”. In this study, we focus on the former and, by means of the “interpretive spiral”, we critically look at the research process of QCA. We show how QCA as a research approach is composed of (1) an “analytical move”, where cases, conditions and outcome(s) are conceptualised in terms of sets, and (2) a “membership move”, where set membership values are qualitatively assigned by the researcher (i.e. calibration). Moreover, we show that QCA scholars have not sufficiently acknowledged the data generation process as a constituent research phase (or “move”) for the performance of QCA. This is particularly relevant when qualitative data–e.g. interviews, focus groups, documents–are used for subsequent analysis and calibration (i.e. analytical and membership moves). We call the qualitative data collection process “relational move” because, for data gathering, researchers establish the social relation “interview” with the study participants. By using examples from our own research, we show how a dialogical interviewing style can help researchers gain the in-depth knowledge necessary to meaningfully represent qualitative data into set membership values for QCA, hence improving our ability to account for the “qualitative” in QCA.

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1 Introduction

Qualitative Comparative Analysis (QCA) is a configurational comparative research approach and method for the social sciences based on set-theory. It was introduced in crisp-set form by Ragin ( 1987 ) and later expanded to fuzzy sets (Ragin 2000 ; 2008a ; Rihoux and Ragin 2009 ; Schneider and Wagemann 2012 ). QCA is a diversity-oriented approach extending “the single-case study to multiple cases with an eye toward configurations of similarities and differences” (Ragin 2000 :22). QCA aims at finding a balance between complexity and generalizability by identifying data patterns that can exhibit or approach set-theoretic connections (Ragin 2014 :88).

As a research approach, QCA researchers first conceptualise cases as elements belonging, in kind and/or degree, to a selection of conditions and outcome(s) that are conceived as sets. They then assign cases’ set membership values to conditions and outcome(s) (i.e. calibration). Populations are constructed for outcome-oriented investigations and causation is conceived to be conjunctural and heterogeneous (Ragin 2000 : 39ff). As a method, QCA is the systematic and formalised analysis of the calibrated dataset for cross-case comparison through Boolean algebra operations. Combinations of conditions (i.e. configurations) represent both the characterising features of cases and also the multiple paths towards the outcome (Byrne 2005 ).

Most of the critiques to QCA focus on the methodological aspects of “QCA as a method” (e.g. Lucas and Szatrowski 2014 ), although epistemological issues regarding deterministic causality and subjectivity in assigning set membership values are also discussed (e.g. Collier 2014 ). In response to these critiques, Ragin ( 2014 ; see also Ragin 2000 , ch. 11) emphasises the “mindset shift” needed to perform QCA: QCA “as a method” makes sense only if researchers admit “QCA as a research approach”, including its qualitative component.

The qualitative character of QCA emerges when recognising the relevance of case-based knowledge or “case intimacy”. The latter is key to perform calibration (see e.g. Ragin 2000 :53–61; Byrne 2005 ; Ragin 2008a ; Harvey 2009 ; Greckhamer et al. 2013 ; Gerrits and Verweij 2018 :36ff): when associating “meanings” to “numbers”, researchers engage in a “dialogue between ideas and evidence” by using set-membership values as “ interpretive tools ” (Ragin 2000 : 162, original emphasis). The foundations of QCA as a research approach are explicitly rooted in qualitative, case-oriented research approaches in the social sciences, in particular in the understanding of causation as multiple and configurational, in terms of combinations of conditions, and in the conceptualisation of populations as types of cases, which should be refined in the course of an investigation (Ragin 2000 : 30–42).

Arguably, QCA researchers should make ample use of qualitative methods for the social sciences, such as narrative or semi-structured interviews, focus groups, discourse and document analysis, because this will help gain case intimacy and enable the dialogue between theories and data. Furthermore, as many QCA-studies have a small to medium sample size (10–50 cases), qualitative data collection methods appear to be particularly appropriate to reach both goals. However, so far only around 30 published QCA studies use qualitative data (de Block and Vis 2018 ), out of which only a handful employ narrative interviews (see Sect.  2 ).

We argue that this puzzling observation about QCA empirical research is due to two main reasons. First, quantitative data, in particular secondary data available from official databases, are more malleable for calibration. Although QCA researchers should carefully distinguish between measurement and calibration (see e.g. Ragin, 2008a , b ; Schneider and Wagemann 2012 , Sect. 1.2), quantitative data are more convenient for establishing the three main qualitative anchors (i.e. the cross-over point as maximum ambiguity; the lower and upper thresholds for full set membership exclusion or inclusion). Quantitative data facilitate QCA researchers in performing QCA both as a research approach and method. QCA scholars are somewhat aware of this when discussing “the two QCAs” (large-n/quantitative data and small-n/more frequent use of qualitative data; Greckhamer et al. 2013 ; see also Thomann and Maggetti 2017 ).

Second, the use of qualitative data for performing QCA requires an additional effort from the part of the researcher, because data collected through, for instance, narrative interviews, focus groups and document analysis come in verbal form. Therefore, QCA researchers using qualitative methods for empirical research have to first collect data and only then move to their analysis and conceptualisation as sets (analytical move) and their calibration into “numbers” (membership move) for their subsequent handling through QCA procedures (QCA as a method).

Because of these two main reasons, we claim that data generation (or data construction) should also be recognised and integrated in the QCA research process. Fully accounting for QCA as a “qualitative” research approach necessarily entails questions about the data generation process, especially when qualitative research methods are used that come in verbal, and not numerical, form.

This study’s contributions are twofold. First, we present the “interpretative spiral” (see Fig.  1 ) or “cycle” (Sandelowski et al. 2009 ) where data gradually transit through changes of state: from meanings, to concepts to numerical values. In limiting our discussion to QCA as a research approach, we identified three main moves composing the interpretative spiral: the (1) relational (data generation through qualitative methods), (2) analytical (set conceptualisation) and (3) membership (calibration) moves. Second, we show how in-depth knowledge for subsequent set conceptualisation and calibration can be more effectively generated if the researcher is open, during data collection, to support the interviewee’s narration and to establish a dialogue—a relation—with him/her (i.e. the relational move). It is the researcher’s openness that can facilitate the development of case intimacy for set conceptualisation and assessment (analytical and membership moves). We hence introduce a “dialogical” interviewing style (La Mendola 2009 ) to show how this approach can be useful for QCA researchers. Although we mainly discuss narrative interviews, a dialogical interviewing style can also adapt to face-to-face semi-structured interviews or questionnaires.

The interpretative spiral and the relational, analytical and membership moves

Our main aim is to make QCA researchers more aware of “minding their moves” in the interpretative spiral. Additionally, we show how a “dialogical” interviewing style can facilitate the access to the in-depth knowledge of cases useful for calibration. Researchers using narrative interviews who have not yet performed QCA can gain insight into–and potentially see the advantages of–how qualitative data, in particular narrative interviews, can be employed for the performance of QCA (see Gerrits and Verweij 2018 :36ff).

In Sect.  2 we present the interpretative spiral (Fig.  1 ,) the interconnections between the three moves and we discuss the limited use of qualitative data in QCA research. In Sect.  3 , we examine the use of qualitative data for performing QCA by discussing the relational move and a dialogical interviewing style. In Sect.  4 , we examine the analytical and membership moves and discuss how QCA researchers have so far dealt with them when using qualitative data. In Sect.  5 , we conclude by putting forward some final remarks.

2 The interpretative spiral and the three moves

Sandelowski et al. ( 2009 ) state that the conversion of qualitative data into quantitative data (“quantitizing”) necessarily involves “qualitazing”, because researchers perform a “continuous cycling between assigning numbers to meaning and meaning to numbers” (p. 213). “Data” are recognised as “the product of a move on the part of researchers” (p. 209, emphasis added) because information has to be conceptualised, understood and interpreted to become “data”. In Fig.  1 , we tailor this “cycling” to the performance of QCA by means of the interpretative spiral.

Through the interpretative spiral, we show both how knowledge for QCA is transformed into data by means of “moves” and how the gathering of qualitative data consists of a move on its own. Our choice for the term “move” is grounded in the need to communicate a sense of movement along the “cycling” between meanings and numbers. Furthermore, the term “move” resonates with the communicative steps that interviewers and interviewee engage in during an interview (see Sect.  3 below).

Although we present these moves as separate, they are in reality interfaces, because they are part of the same interpretative spiral. They can be thought of as moves in a dance; the latter emerges because of the succession of moves and steps as a whole, as we show below.

The analytical and membership moves are intertwined-as shown by the central “vortex” of the spiral in Fig.  1 -as they are composed of a number of interrelated steps, in particular case selection, theory-led set conceptualisation, definition of the most appropriate set membership scales and of the cross-over and upper and lower thresholds (e.g. crisp-set, 4- or 6-scale fuzzy-sets; see Ragin 2000 :166–171; Rihoux and Ragin 2009 ). Calibration is the last move of the dialogue between theory (concepts of the analytical move) and data (cases). In the membership move, fuzzy sets are used as “an interpretative algebra, a language that is half-verbal-conceptual and half-mathematical-analytical” (Ragin 2000 :4). Calibration is hence a type of “quantitizing” and “qualitizing” (Sandelowski et al. 2009 ). In applied QCA, set membership values can be reconceptualised and recalibrated. This will for instance be done to solve true logical contradictions in the truth table and when QCA results are interpreted by “going back to cases”, hence overlapping with the practices related to QCA “as a method”.

The relational move displayed in Fig.  1 expresses the additional interpretative process that researchers engage in when collecting and analysing qualitative data. De Block and Vis ( 2018 ) show that only around 30 published QCA-studies combine qualitative data with QCA, including a range of additional data, like observations, site visits, newspaper articles.

However, a closer look reveals that the majority of the published QCA-studies using qualitative data employ (semi)structured interviews or questionnaires. Footnote 1 For instance, Basurto and Speer ( 2012 ) Footnote 2 proposed a step-wise calibration process based on a frequency-oriented strategy (e.g. number of meetings, amount of available information) to calibrate the information collected through 99 semi-structured interviews. Fischer ( 2015 ) conducted 250 semi-structured interviews by cooperating with four trained researchers using pre-structured questions, where respondents could voluntarily add “qualitative pieces of information” in “an interview protocol” (p. 250). Henik ( 2015 ) structured and carried out 50 interviews on whistle-blowing episodes to ensure subsequent blind coding of a high number of items (almost 1000), arguably making them resemble face-to-face questionnaires.

In turn, only a few QCA-researchers use data from narrative interviews. Footnote 3 For example, Metelits ( 2009 ) conducted narrative interviews during ethnographic fieldwork over the course of several years. Verweij and Gerrits ( 2015 ) carried out 18 “open” interviews, while Chai and Schoon ( 2016 ) conducted “in-depth” interviews. Wang ( 2016 ), in turn, conducted structured interviews through a questionnaire, following a similar approach as in Fischer ( 2015 ); however, during the interviews, Wang’s respondents were asked to reflexively justify the chosen questionnaire's responses, hence moving the structured interviews closer to narrative ones. Tóth et al. ( 2017 ) performed 28 semi-structured interviews with company managers to evaluate the quality and attractiveness of customer-provider relationships for maintaining future business relations. Their empirical strategy was however grounded in initial focus groups and other semi-structured interviews, composed of open questions in the first part and a questionnaire in the second part (Tóth et al. 2015 ).

Although no interview is completely structured or unstructured, it is useful to conceptualise (semi-)structured and less structured (or narrative) interviews as the two ends of a continuum (Brinkmann 2014 ). Albeit still relatively rare as compared to quantitative data, the more popular integration of (semi-)structured interviews into QCA might be due to the advantages that this type of qualitative data holds for calibration. The “structured” portion of face-to-face semi-structured interviews or questionnaires facilitates the calibration of this type of qualitative data, because quantitative anchor points can be more clearly identified to assign set membership values (see e.g. Basurto and Speer 2012 ; Fischer 2015 ; Henik 2015 ).

Hence, when critically looking at the “qualitative” character of QCA as a research approach, applied research shows that qualitative methods uneasily fit with QCA. This is because data collection has not been recognised as an integral part of the QCA research process. In Sect.  3 , we show how qualitative data, and in particular a dialogical interviewing style, can help researchers to develop case intimacy.

3 The relational move

Social data are not self-evident facts, they do not reveal anything in themselves, but researchers must engage in interpretative efforts concerning their meaning (Sandelowski et al. 2009 ; Silverman, 2017 ). Differently stated, quantitising and qualitising characterise both quantitative and qualitative social data, albeit to different degrees (Sandelowski et al. 2009 ). This is an ontological understanding of reality that is diversely held by post-positivist, critical realist, critical and constructivist approaches (except by positivist scholars; see Guba and Lincoln, 2005 :193ff). Our position is more akin to critical realism that, in contrast to post-modernist perspectives (Spencer et al. 2014 :85ff), holds that reality exists “out there” and that epistemologically, our knowledge of it, although imperfect, is possible–for instance through the scientific method (Sayer 1992 ).

The socially constructed, not self-evident character of social data is manifest in the collection and analysis of qualitative data. Access to the field needs to be earned, as well as trust and consent from participants, to gradually build and expand a network of participants. More than “collected”, data are “gathered”, because they imply the cooperation with participants. Data from interviews and observations are heterogeneous, and need to be transcribed and analysed by researchers, who also self-reflectively experience the entire process of data collection. QCA researchers using qualitative data necessarily have to go through this additional research process–or move-to gather and generate data, before QCA as a research approach can even start. As QCA researchers using qualitative data need to interact with participants to collect their data, we call this additional research process “relational move”.

While we limit our discussion to narrative interviews and select a few references from a vast literature, our claim is that it is the ability of the interviewer to give life to interviews as a distinct type of social interaction that is key for the data collection process (Chase 2005 ; Leech 2002 ; La Mendola 2009 ; Brinkmann. 2014 ). The ability of the interviewer to establish a dialogue with the interviewee–also in the case of (semi-)structured interviews–is crucial to gain access to case-based knowledge and thus develop the case intimacy later needed in the analytical and membership moves. The relational move is about a researcher’s ability to handle the intrinsic duality characterising that specific social interaction we define as an interview. Both (or more) partners have to be considered as necessary actors involved in giving shape to the “inter-view” as an ex-change of views.

Qualitative researchers call this ability “rapport” (Leech, 2002 :665), “contract” or “staging” (Legard et al., 2003 :139). In our specific understanding of the relational move through a “dialogical” Footnote 4 interviewing style, during the interview 1) the interviewer and the interviewee become the “listener” and the “narrator” (Chase, 2005 :660) and 2) a true dialogue between listener and narrator can only take place when they engage in an “I-thou” interaction (Buber 1923 /2008), as we will show below when we discuss selected examples from our own research.

As a communicative style, in a dialogical interview not only the researcher cannot disappear behind the veil of objectivity (Spencer et al. 2014 ), but the researcher is also aware of the relational duality–or “dialogueness”–inherent to the “inter-view”. Dialogical face-to-face interviewing can be compared to a choreography (Brinkman 2014 :283; Silverman 2017 :153) or a dance (La Mendola 2009 , ch. 4 and 5) where one of the partners (the researcher) is the porteur (“supporter”) of the interaction. As in a dancing couple, the listener supports, but does not lead, the narrator in the unfolding of her story. The dialogical approach to interviewing is hence non-directive, but supportive. A key characteristic of dialogical interviews is a particular way of “being in the interview” (see example 2 below) because it requires the researcher to consider the interviewee as a true narrator (a “thou”). Footnote 5

In a dialogical approach to interviews, questions can be thought of as frames through which the listener invites the narrator to tell a story in her own terms (Chase 2005 :662). The narrator becomes the “subject of study” who can be disobedient and capable to raise her own questions (Latour 2000 : 116; see also Lund 2014). This is also compatible with a critical realist ontology and epistemology, which holds that researchers inevitably draw artificial (but negotiable) boundaries around the object and subject of analysis (Gerrits and Verweij 2013). The case-based, or data-driven (ib.), character of QCA as a research approach hence takes a new meaning: in a dialogical interviewing style, although the interviewer/listener proposes a focus of analysis and a frame of meaning, the interviewee/narrator is given the freedom to re-negotiate that frame of meaning (La Mendola 2009 ; see examples 1 and 2 below).

We argue that this is an appropriate way to obtain case intimacy and in-depth knowledge for subsequent QCA, because it is the narrator who proposes meanings that will then be translated by the researcher, in the following moves, into set membership values.

Particularly key for a dialogical interviewing style is the question formulation, where interviewer privileges “how” questions (Becker 1998 ). In this way, “what” and “why” (evaluative) questions are avoided, where the interviewee is asked to rationally explain a process with hindsight and that supposedly developed in a linear way. Also typifying questions are avoided, where the interviewer gathers general information (e.g. Can you tell me about the process through which an urban project is typically built? Can you tell me about your typical day as an academic?). Footnote 6 “Dialogical” questions can start with: “I would like to propose you to tell me about…” and are akin to “grand tour questions” (Spradley 1979 ; Leech 2002 ) or questions posed “obliquely” (Roulston 2018 ) because they aim at collecting stories, episodes in a certain situation or context and allowing the interviewee to be relatively free to answer the questions.

An example taken from our own research on a QCA of large-scale urban transformations in Western Europe illustrates the distinct approach characterising dialogical interviewing. One of our aims was to reconstruct the decision-making process concerning why and how a certain urban transformation took place (Pagliarin et al. 2019 ). QCA has already been previously used to study urban development and spatial policies because it is sensitive to individual cases, while also accounting for cross-case patterns by means of causal complexity (configurations of conditions), equifinality and causal asymmetry (e.g. Byrne 2005 ; Verweij and Gerrits 2015 ; Gerrits and Verweij 2018 ). A conventional way to formulate this question would be: “In your opinion, why did this urban transformation occur at this specific time?” or “Which were the governance actors that decided its implementation?”. Instead, we formulated the question in a narrative and dialogical way:

Example 1 Listener [L]: Can you tell me how the site identification and materialization of Ørestad came about? Narrator [N]: Yes. I mean there’s always a long background for these projects. (…) it’s an urban area built on partly reclaimed land. It was, until the second world war, a seaport and then they reclaimed it during the second world war, a big area. (…) this is the island called Amager. In the western part here, you can see it differs completely from the rest and that’s because they placed a dam all around like this, so it’s below sea level. (…) [L]: When you say “they”, it’s…? [N]: The municipality of Copenhagen. Footnote 7 (…)

In this example, the posed question (“how… [it]… came about?”) is open and oriented toward collecting the specific story of the narrator about “how” the Ørestad project emerged (Becker 1998 ), starting at the specific time point and angle decided by the interviewee. In this example, the interviewee decided to start just after the Second World War (albeit the focus of the research was only from the 1990s) and described the area’s geographical characteristics as a background for the subsequent decision-making processes. It is then up to the researcher to support the narrator in funnelling in the topics and themes of interest for the research. In the above example, the listener asked: “When you say “they”, it’s…?” to signal to the narrator to be more specific about “they”, without however assuming to know the answer (“it’s…?”). In this way, the narrator is supported to expand on the role of Copenhagen municipality without directly asking for it (which is nevertheless always a possibility to be seized by the interviewer).

The specific “dialogical” way of the researcher of “being in the interview” is rooted in the epistemological awareness of the discrepancy between the narrator’s representation and the listener’s. During an interview, there are a number of “representation loops”. As discussed in the interpretative spiral (see Sect.  2 ), the analytical and membership moves are characterised by a number of research steps; similarly, in the relational move the researcher engages in representation loops or interpretative steps when interacting with the interviewee. The researcher holds ( a ) an analytical representation of her focus of analysis, ( b ) which will be re-interpreted by the interviewee (Geertz, 1973 ). In a dialogical style of interview, the researcher also embraces ( c ) her representation of the ( b ) interviewee's interpretation of ( a ) her theory-led representation of the focus of analysis. Taken together, ( a )-( b )-( c ) are the structuring steps of a dialogical interview, where the listener’s and narrator’s representations “dance” with one another. In the relational move, the interviewer is aware of the steps from one representation to another.

In the following Example 2 , the narrator re-elaborated (interpretative step b) the frame of meaning of the listener (interpretative step a) by emphasising to the listener two development stages of a certain project (an airport expansion in Barcelona, Spain), which the researcher did not previously think of (interpretative step c):

Example 2 [L]: Could you tell me about how the project identification and realisation of the Barcelona airport come about? [N]: Of the Barcelona airport? Well. The Barcelona airport is I think a good thermometer of something deeper, which has been the inclusion of Barcelona and of its economy in the global economy. So, in the last 30 years El Prat airport has lived through like two impulses of development, because it lived, let´s say, the necessary adaptation to a specific event, that is the Olympic games. There it lived its first expansion, to what we today call Terminal 2. So, at the end of the ´80 and early ´90, El Prat airport experienced its first big jump. (...) Later, in 2009 (...) we did a more important expansion, because we did not expand the original terminal, but we did a new, bigger one, (...) the one we now call Terminal 1. Footnote 8

If the interviewee is considered as a “thou”, and if the researcher is aware of the representation loops (see above), the collected information can also be helpful for constructing the study population in QCA. The population under analysis is oftentimes not given in advance but gradually defined through the process of casing (Ragin 2000 ). This allows the researcher to be open to construct the study population “with the help of others”, like “informants, people in the area, the interlocutors” (Lund 2014:227). For instance, in example 2 above, the selection of which urban transformations will form the dataset can depend on the importance given by the interviewees to the structuring impact of a certain urban transformation on the overall urban structure of an urban region.

In synthesis, the data collection process is a move on its own in the research process for performing QCA. Especially when the collected data are qualitative, the researcher engages in a relation with the interlocutor to gather information. A dialogical approach emphasises that the quality of the gathered data depends on the quality of the dialogue between narrator and listener (La Mendola 2009 ). When the listener is open to consider the interviewee as a “thou”, and when she is aware of the interpretative steps occurring in the interview, then meaningful case-based knowledge can be accessed.

Case intimacy is at best developed when the researcher is open to integrate her focus of analysis with fieldwork information and when s/he invites, like in a dance, the narrator to tell his story. However, a dialogical interviewing style is not theory-free, but it is “theory-independent”: the dialogical interviewer supports the narration of the interviewee and does not lead the narrator by imposing her own conceptualisations. We argue that such dialogical I-thou interaction during interviews fosters in-depth knowledge of cases, because the narrator is treated as a subject that can propose his interpretation of the focus of analysis before the researcher frames it within her analytical and membership moves.

However, in practice, there is a tension between the researcher's need to collect data and the “here-and-now interactional event of the interview” (Rapley, 2001 :310). It is inevitable that the researcher re-elaborates, to a certain degree, her  analytical framework during the interviews, because this enables the researcher to get acquainted with the object of analysis and to keep the interview content on target with the research goals (Jopke and Gerrits, 2019 ). But is it this re-interpretation of the interviewee's replies and stories by the listener during the interviews that opens the interviewer’s awareness of the representation loops.

4 The analytical and membership moves

Researchers engage in face-to-face interviews as a strategy for data collection by holding specific analytical frameworks and theories. A researcher seldom begins his or her undertakings, even in the exploratory phase, with a completely open mind (Lund 2014:231). This means that the researcher's representations (a and c, see above) of the narrator's representation(s) (b, see above) are related to the theory-led frames of inquiry that the researcher organises to understand the world. These frames are typically also verbal, as “[t]his framing establishes, and is established through, the language we employ to speak about our concerns” (Lund 2014:226).

In particular for the collection of qualitative data, the analytical move is composed of two main movements: during and after the data collection process. During the data collection process, when adopting a dialogical interviewing style, the researcher should mind keeping the interview theory-independent (see above). First, this means that the interviewee is not asked to get to the researcher’s analytical level. The use of jargon should be avoided, either in narrative or semi-structured interviews and questionnaires, because it would limit the narrator's representation(s) (b) within the listener's interpretative frames (a), and hence the chance for the researcher to gain in-depth case knowledge (c). Silverman ( 2017 :154) cautions against “flooding” interviewees with “social science categories, assumptions and research agendas”. Footnote 9 In example 1 above, the use of the words “governance actors” may have misled the narrator–even an expert–since its meaning might not be clear or be the same as the interviewer's.

Second, the researcher should neither sympathise with the interviewee nor judge the narrator’s statements, because this would transform the interview into another type of social interaction, such as a conversation, an interrogation or a confession (La Mendola 2009 ). The analytical move requires that the researcher does not confuse the interview as social interaction with his or her analysis of the data, because this is a specific, separate moment after the interview is concluded. Whatever material or stories a researcher receives during the interviews, it is eventually up to him or her to decide which representation(s) will be told (and how) (Stake 2005 :456). It is the job of the researcher to perform the necessary analytical work on the collected data.

After the fieldwork, the second stage of the analytical move is a change of state of the interviewees' replies and stories to subsequently “feed in” in QCA. The researcher begins to qualitatively assess and organise the in-depth knowledge, in the form of replies or stories, received by the interviewees through their narrations. This usually involves the (double-)coding of the qualitative material, manually or through the use of dedicated software. The analysis of the qualitative material organises the in-depth knowledge gained through the relational move and sustains the (re)definition of the outcome and conditions, their related attributes and sub-dimensions, for performing QCA.

In recognising the difficulty in integrating qualitative (interview) data into QCA procedures, QCA-researchers have developed templates, tables or tree diagrams to structure the analysed qualitative material into set membership scores (Basurto and Speer 2012 ; Legewie 2017 ; Tóth et al. 2017 ; see also online supplementary material). We call these different templates “ Supports for Membership Representation ” (SMeRs) because they facilitate the passage from conceptualisation (analytical move) to operationalisation into set membership values (membership move). Below, we discuss these templates by placing them along a continuum from “more theory-driven” to “more data-driven” (see Gerrits and Verweij 2018 , ch. 1). Although the studies included below did not use a dialogical approach to interviews, we also examine the SMeRs in terms of their openness towards the collected material. As explained above, we believe it is this openness–at best “dialogical”–that facilitates the development of case intimacy on the side of the researcher. In distinguishing the steps characterising both moves (see Sect.  2 above), below we differentiate the analytical and membership moves.

Basurto and Speer ( 2012 ) were the first develop and present a preliminary but modifiable list of theoretical dimensions for conditions and outcome. Their interview guideline is purposely developed to obtain responses to identify anchor points prior to the interviews and to match fuzzy sets. In our perspective, this contravenes the separation between the relational and analytical move: the researcher deals with interviewees as “objects” whose shared information is fitted to the researchers’ analytical framework. In their analytical move, Basurto and Speer define an ideal and a deviant case–both of them non-observable–to locate, by comparison, their cases and facilitate the assignment of fuzzy-set membership scores (membership move).

Legewie ( 2017 ) proposes a “grid” called Anchored Calibration (AC) by building on Goertz ( 2006 ). In the analytical move, the researcher first structures (sub-)dimensions for each condition and the outcome by means of concept trees. Each concept is then represented by a gradation, which should form conceptual continua (e.g. from low to high) and is organised in a tree diagram to include sub-dimensions of the conditions and outcome. In the membership move, to each “graded” concept, anchor points are assigned (i.e. 0, 0.25, 0.75, 1). The researcher then iteratively matches coded evidence from narrative interviews (analytical move) to the identified anchor points for calibration, thus assigning set membership scores (e.g. 0.33 or 0.67; i.e. membership move). Similar to Basurto and Speer ( 2012 ), the analytical framework of the researcher is given priority to and tightly structures the collected data. Key for anchored calibration is the conceptual neatness of the SMeR, which is advantageous for the researcher but that, following our perspective, allows a limited dialogue with the cases and hence the development of case intimacy.

An alternative route is the one proposed by Tóth et al. ( 2017 ). The authors devise the Generic Membership Evaluation Template (GMET) as a “grid” where qualitative information from the interviews (e.g. quotes) and from the researcher’s interpretative process is included. In the analytical move, their template clearly serves as a “translation support” to represent “meanings” into “numbers”: researchers included information on how they interpreted the evidence (e.g. positive/negative direction/effect on membership of a certain attribute; i.e. analytical move), as well as an explanation of why specific set membership scores have been assigned to cases (i.e. membership move). Tóth et al.’s ( 2017 ) SMeR appears more open to the interviewees’ perspective, as researchers engaged in a mixed-method research process where the moment of data collection–the relational move–is elaborated on (Tóth et al. 2015 ). We find their approach more effective for gaining in-depth knowledge of cases and for supporting the dialogue between theory and data.

Jopke and Gerrits ( 2019 ) discuss routines, concrete procedures and recommendations on how to inductively interpret and code qualitative interview material for subsequent calibration by using a grounded-theory approach. In their analytical move, the authors show how conditions can be constructed from the empirical data collected from interviews; they suggest first performing an open coding of the interview material and then continuing with a theoretical coding (or “closed coding”) that is informed by the categories identified in the previous open coding procedure, before defining set membership scores for cases (i.e. membership move). Similar to Tóth et al. ( 2017 ), Jopke and Gerrits’ ( 2019 ) SMeR engages with the data collection and the gathered qualitative material by being open to what the “data” have to “tell”, hence implementing a strategy for data analysis that is effective to gain in-depth knowledge of cases.

Another type of SMeR is the elaboration of summaries of the interview material by unit of analysis (e.g. urban transformations, participation initiatives, interviewees’ individual careers paths). Rihoux and Lobe ( 2009 ) propose the so-called short case descriptions (SCDs). Footnote 10 As a possible step within the interpretative spiral available to the researcher, short case descriptions (SCDs) are concise summaries that effectively synthesise the most important information sorted by certain identified dimensions, which will then compose the conditions, and their sub-dimensions, for QCA. As a type of SMeR, the summaries consist of a change of state of the qualitative material, because they provide “intermediate” information on the threshold between the coding of the interviews' transcripts and the subsequent assignment of membership scores (the membership move, or calibration) for the outcome and each condition. Furthermore, the writing of short summaries appears to be particularly useful to allow researchers that have already performed narrative interviews to evaluate whether to carry out QCA as a systematic method for comparative analysis. For instance, similar to what Tóth et al. ( 2017 :200) did to reduce interview bias, in our own research interviewees could cover the development of multiple cases, and the use of short summaries helped us compare information per each case across multiple interviewees and spot possible contradictions.

The overall advantage of SMeRs is helping researchers provide an overview of the quality and “patchiness” of available information about the cases per interview (or document). SMeRs can also help spot inconsistencies and contradictions, thus guiding researchers to judge if their data can provide sufficiently homogeneous information for the conditions and outcome composing their QCA-model. This is particularly relevant in case-based QCA research, where descriptive inferences are drawn from the material collected from the selected cases and the degree of its internal validity (Thomann and Maggetti 2017 :361). Additionally, the issue of the “quality” and “quantity” across the available qualitative data (de Block and Vis 2018 ) can be checked ex-ante before embarking on QCA.

For the membership move, the GMET, the AC, grounded theory coding and short summaries supports the qualitative assignment of set membership values from empirical interview data. SMeRs typically include an explanation about why a certain set membership score has been assigned to each case record, and diagrammatically arrange information about the interpretation path that researchers have followed to attribute values. They are hence a true “interface” between qualitative empirical data (“words/meaning”) and set membership values (“numbers”). Each dimension included in SMeRs can also be coupled with direct quotes from the interviews (Basurto and Speer 2012 ; Tóth et al. 2017 ).

In our own research (Pagliarin et al. 2019 ), after having coded the interview narratives, we developed concepts and conditions first by comparing the gathered information through short summaries—similar to short case descriptions (SCDs), see Rihoux and Lobe ( 2009 )—and then by structuring the conditions and indicators in a grid by adapting the template proposed by Tóth et al. ( 2017 ). One of the goals of our research was to identify “external factors or events” affecting the formulation and development of large-scale urban transformations. External (national and international) events (e.g. failed/winning bid for the Olympic Games, fall of Iron Curtain/Berlin wall) do not have an effect per se, but they stimulate actors locally to make a certain decision about project implementation. We were able to gain this knowledge because we adopted a dialogical interviewing style (see Example 3 below). As the narrator is invited to tell us about some of the most relevant projects of urban transformation in Greater Copenhagen in the past 25–30 years, the narrator is free to mention the main factors and actors impacting on Ørestad as an urban transformation.

Example 3 [L]: In this interview, I would propose that you tell me about some of the most relevant projects of urban transformation that have been materialized in Greater Copenhagen in the past 25–30 years. I would like you to tell me about their itinerary of development, step by step, and if possible from where the idea of the project emerged. [N]: Okay, I will try to start in the 80’s. In the 80’s, there was a decline in the city of Copenhagen. (…) In the end of the 80’s and the beginning of the 90’s, there was a political trend. They said, “We need to do something about Copenhagen. It is the only big city in Denmark so if we are going to compete with other cities, we have to make something for Copenhagen so it can grow and be one of the cities that can compete with Amsterdam, Hamburg, Stockholm and Berlin”. I think also it was because of the EU and the market so we need to have something that could compete and that was the wall falling in Berlin. (…) The Berlin Wall, yes. So, at that time, there was a commission to sit down with the municipality and the state and they come with a plan or report. They have 20 goals and the 20 goals was to have a bridge to Sweden, expanding of the airport, a metro in Copenhagen, investment in cultural buildings, investment in education. (…) In the next 5 years, from the beginning of the 90’s to the middle of the 90’s, there were all of these projects more or less decided. (…) The state decided to make the airport, to make the bridge to Sweden, to make… the municipality and the city of Copenhagen decides to make Ørestad and the metro together with the state. So, all these projects that were lined up on the report, it was, let’s decide in the next 5 years. [L]: So, there was a report that decided at the end of the 80’s and in the 90’s…? [N]: Yes, ‘89. (…) To make all these projects, yes. (…). [L]: Actually, one of the projects I would like you to tell me about is the Ørestad. R: Yes. It is the Ørestad. The Ørestad was a transformation… (…).

The factors mentioned by the interviewee corresponded to the main topics of interest by the researcher. In this example, we can also highlight the presence of a “prompt” (Leech 2002 ) or “clue” (La Mendola 2009 ). To keep the narrator on focus, the researcher “brings back” (the original meaning of rapporter ) the interviewee to the main issues of the inter-view by asking “So, there was a report…”.

Following the question formulation as shown in example 3, below we compare the external event(s) impacting the cases of Lyon Part-Dieu in France (Example 4 ) and Scharnhauserpark in Stuttgart in Germany (Example 5 ).

Example 4 [N]: So, Part-Dieu is a transformation of the1970s, to equip [Lyon] with a Central Business District like almost all Western cities, following an encompassing regional plan. This is however not local planning, but it is part of a major national policy. (…) To counterbalance the macrocephaly of Paris, 8 big metropolises were identified to re-balance territorial development at the national level in the face of Paris. (…) including Lyon. (…) The genesis of Part-Dieu is, in my opinion, a real-estate opportunity, and the fact to have military barracks in an area (…) 15 min away from the city centre (…) to reconvert in a business district. Footnote 11

Example 5 [N]: When the American Army left the site in 1992, the city of Ostfildern consisted of five villages. They bought the site and they said, “We plan and build a new centre for our village”, because these are five villages and this is in the very centre. It’s perfectly located, and when they started they had 30,000 inhabitants and now that it’s finished, they have 40,000, so a third of the population were added in the last 20 years by this project. For a small municipality like Ostfildern, it was a tremendous effort and they were pretty good at it. Footnote 12

In the examples above, Lyon Part-Dieu and Scharnhauserpark are unique cases and developed into an area with different functions (a business district and a mixed-use area), but we can identify a similar event: the unforeseen dismantling of military barracks. Both events were considered external factors punctually identifiable in time that triggered the redevelopment of the areas. Instead, in the following illustration about the “Confluence” urban renewal in Lyon, the identified external event relates to a global trend regarding post-industrial cities and the “patchwork” replacement of functions in urban areas:

Example 6 [N]: The Confluence district (…) the wholesale market dismantles and opens an opportunity at the south of the Presqu'Île, so an area extremely well located, we are in the city centre, with water all around because of the Saône and Rhône rivers, so offering a great potential for a high quality of life. However, I say “potential” because there is also a highway passing at the boundary of the neighbourhood. Footnote 13

Although our theoretical framework identified a set of exogenous factors affecting large-scale urban transformations locally, we used the empirical material from our interviews to conceptualise the closing of military barracks and the dismantling of the wholesale market as two different, but similar types of external events, and considered them to be part of the same “external events” condition. In set-theoretic terms, this condition is defined as a “set of projects where external (unforeseen) events or general/international trends had a large impact on project implementation”. The broader set conceptualisation of this condition is possibly not optimal, as it reflects the tension in comparative research to find a balance between capturing cases’ individual histories (case idiosyncrasies) and more concepts that are abstract “enough” to account for cross-case patterns (see Gerrits and Verwej 2018 ; Jopke and Gerrit 2019 ). This is a key challenge of the analytical move.

However, the core of the subsequent membership move is precisely to perform a qualitative assessment to capture these differences by assigning different set-membership values. In the case of Lyon Confluence, where the closing of the whole sale market as external event did happen but did only have a “general” influence on the area’s redevelopment, the case was given a set membership value of 0.33 to this condition. In contrast, the case of Lyon Part-Dieu was given a set membership score of 0.67 to the condition “external events” because a French military area was dismantled, but it was also combined with a national strategy of the French state to redistribute territorial development across France. According to our analysis of the collected qualitative material, it was an advantage that the military area was dismantled but the redevelopment of Part-Dieu would have probably been affected anyway by the overall national territorial strategy. Footnote 14 Finally, the case of Stuttgart Scharnhauserpark case was given full membership (1.00) to the condition, because the US army left the area–which is an indication of a “fully exogenous” event–that truly stimulated urban change in Scharnhauserpark. Footnote 15

Our calibration (membership move) of the three cases illustrated in Examples 4 , 5 and 6 shows that set membership values represent a concept, at times also relatively broad to allow comparison (analytical move), but that they do not replace the specific way (or “meaning”) through which the impact of external factors empirically instantiate in each of the cases discussed in the above examples.

In the interpretative spiral Fig.  1 , there is hence–despite our wishes–no perfect correspondence between meanings and numbers (quantitising) and numbers and meanings (qualitising; see Sandelowski et al. 2009 ). This is a consequence of the constructed nature of social data (see Sect.  2 ). When using qualitative data, fuzzy-sets are “ interpretive tools” to operationalise theoretical concepts (Ragin 2000 :162, original emphasis) and hence are approximations to reality. In other words, set memberships values are token s. Here, we agree with Sandelowski et al. ( 2009 ), who are critical of “the rhetorical appeal of numbers” (p. 208) and the vagaries of ordinal categories in questionnaires (p. 211ff).

Note that calibration by using qualitative data is not blurry or unreliable. On the contrary, its robustness is given by the quality of the dialogue established between researcher and interviewee and by the acknowledgement that the analytical and membership moves are types of representation –as fourth and fifth representation loops. It might hence be possible that QCA researchers using qualitative data have a different research experience of QCA as a research approach and method than QCA researchers using quantitative data.

5 Conclusion

In this study, we critically observed how, so far, qualitative data have been used in few QCA studies, and only a handful use narrative interviews (de Block and Vis 2018 ). This situation is puzzling because qualitative research methods can offer an effective route to gain access to in-depth case knowledge, or case intimacy, considered key to perform QCA.

Besides the higher malleability of quantitative data for set conceptualisation and calibration (here called “analytical” and “membership” moves), we claimed that the limited use of qualitative data in QCA applied research depends on the failure to recognise that the data collection process is a constituent part of QCA “as a research approach”. Qualitative data, such as interviews, focus groups or documents, come in verbal form–hence, less “ready” for calibration than quantitative data–and require a research phase on their own for data collection (here called the “relational move”). The relational, analytical and membership moves form an “interpretative spiral” that hence accounts for the main research phases composing QCA “as a research approach”.

In the relational move, we showed how researchers can gain access to in-depth case-based knowledge, or case intimacy, by adopting a “dialogical” interviewing style (La Mendola 2009 ). First, researchers should be aware of the discrepancy between the interviewee/narrator’s representation and the interviewer/listener’. Second, researchers should establish an “I-thou” relationship with the narrator (Buber 1923 /2010; La Mendola 2009 ). As in a dancing couple, the interviewer/listener should accompany, but not lead, the narrator in the unfolding of her story. These are fundamental routes to make the most of QCA’s qualitative potential as a “close dialogue with cases” (Ragin 2014 :81).

In the analytical and membership moves, researchers code, structure and interpret their data to assign crisp- and fuzzy-set membership values. We examined the variety of templates–what we call Supports for Membership Representation (SMeRs)–designed by QCA-researchers to facilitate the assignment of “numbers” to “words” (Rihoux and Lobe 2009 ; Basurto and Speer 2012 ; Legewie 2017 ; Tóth et al. 2015 , 2017 ; Jopke and Gerrits 2019 ).

Our study did not offer an overarching examination of the research process involved in QCA, but critically focussed on a specific aspect of QCA as a research approach. We focussed on the “translation” of data collected through qualitative research methods (“words” and “meanings”) into set membership values (“numbers”). Hence, in this study the discussion of QCA as a method has been limited.

We hope our paper has been a first contribution to identify and critically examine the “qualitative” character of QCA as a research approach. Further research could identify other relevant moves in QCA as a research approach, especially when non-numerical data are employed and regarding internal and external validity. Other moves and steps could also be identified or clearly labelled in QCA as a method, in particular when assessing limited diversity, skewedness (e.g. “data distribution” step) and the management of true logical contradictions (e.g. “solving contradictions” move). These are all different mo(ve)ments in the full-fledged application of QCA that allow researchers to make sense of their data and to connect “theory” and “evidence”.

As also noted by de Block and Vis ( 2018 ), QCA researchers are not always clear about what they exactly mean with “in-depth” or “open” interviews and how they informed the calibration process (e.g. Verweij and Gerrits, 2015 ), especially when also quantitative data and different coders were used (e.g. Chai and Schoon, 2016 ).

See online appendix.

We are aware that other studies combining narrative interviews and QCA have been carried out, but here we limit our discussion only to already published articles that we are aware of at the time of writing.

Without going into further details on this occasion, the term “dialogical” explicitly refers to the “dialogical epistemology” as discussed by Buber ( 1923 /2008) who distinguishes between an “I-thou” relation and an “I-it” experience. In this perspective, “dialogical” is considered as a synonym of “relational” (i.e. “I-thou” relation).

See footnote.4

The interviewer avoids posing evaluative and typifying questions to the narrator, but the former naturally works through evaluative and typifying research questions.

Copenhagen, Interview 5, September 1, 2016.

Barcelona, Interview 1, June 27, 2016. Translated from the original Spanish.

We take the risk to quote Silverman ( 2017 ) although in his article he warned about extracting and using quotes to support the researchers' arguments.

Gerrits and Verweij ( 2018 ) also emphasise the usefulness of thick case descriptions.

Lyon, Interview 4, October, 13 2016. Translated from the original French.

Stuttgart, Interview 1, July, 18 2016.

Lyon, Interview 1, October 11, 2016. Translated from the original French.

This consideration also relates to the interdependence, and not necessarily independence, of conditions in QCA, which is a topic that is beyond the scope of this study (see e.g. Jopke and Gerrits 2019 ).

For a discussion regarding the “absence” of possible factors from the interviewees' narrations, we refer readers to Sandelowski et al. ( 2009 ) and de Block and Vis ( 2018 ). In general, data triangulation is a good strategy to deal with partial and even contradictory information collected from multiple interviewees. For our own strategy regarding data triangulation, we also used an online questionnaire, additional literature and site visits (Pagliarin et al. 2019 ).

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Acknowledgements

The authors would like to thank the two reviewers who provided great insights and careful remarks, thus allowing us to improve the quality of the manuscript. During a peer-review process lasting for more than 2 years, we intensely felt the pushes and slows, and at times the impasses, of a fruitful dialogue on the qualitative and quantitative aspects of comparative analysis in the social sciences.

Open Access funding enabled and organized by Projekt DEAL. This research has been partially funded through the Consolidator Grant (ID: BSCGIO 157789), held by Prof. h. c. Dr. Anna M. Hersperger, provided by the Swiss National Science Foundation.

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Pagliarin, S., La Mendola, S. & Vis, B. The “qualitative” in qualitative comparative analysis (QCA): research moves, case-intimacy and face-to-face interviews. Qual Quant 57 , 489–507 (2023). https://doi.org/10.1007/s11135-022-01358-0

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Comparative Analysis of Qualitative And Quantitative Research

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There’s no hard and fast rule for qualitative versus quantitative research, and it’s often taken for granted. It is claimed here that the divide between qualitative and quantitative research is ambiguous, incoherent, and hence of little value, and that its widespread use could have negative implications. This conclusion is supported by a variety of arguments. Qualitative researchers, for example, have varying perspectives on fundamental problems (such as the use of quantification and causal analysis), which makes the difference as such shaky. In addition, many elements of qualitative and quantitative research overlap significantly, making it difficult to distinguish between the two. Practically in the case of field research, the Qualitative and quantitative approach can't be distinguished clearly as the study pointed. The distinction may limit innovation in the development of new research methodologies, as well as cause complication and wasteful activity. As a general rule, it may be desirable not to conceptualise research approaches at such abstract levels as are done in the context of qualitative or quantitative methodologies. Discussions of the benefits and drawbacks of various research methods, rather than general research questions, are recommended.

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American Politics The study of American Politics in the doctorate program in politics and international affairs provides a comprehensive overview as well as an in-depth analysis of American politics. Our faculty focus on various aspects of American politics, including theoretical foundations, federalism, institutions (Congress, the executive branch, the bureaucracy, the judiciary), political behavior (political parties, the media, interest groups, social movements, and elections), and public policy (foreign and domestic), and employ a range of methodological approaches such as historical development, legal doctrine, institutional rules, and quantitative analyses of the behavior of political actors and the mass public, to advance the student's research skills.  Our core class, Seminar in American Politics, for instance, surveys the key foundations, institutions, and behavior in American politics, introducing students to both qualitative and quantitative methodological approaches for analyzing and testing the changing trends and outcomes in American politics. Special topics courses provide opportunities to gain in-depth knowledge on new research on a range of themes, including political development, the social bases of politics, and the global impact of American politics. The faculty in American politics have made important contributions in the areas of race and ethnicity, the judiciary, the presidency, Florida government, civil liberties, health care, environmental justice, economic inequality, and animal rights. Our strengths lie in economic inequality, animal rights, the Presidency, Judicial Behavior, Race and Ethnicity, and State and Local Government. In these specific areas, we have published several cutting-edge books and articles in leading peer-reviewed journals, which examine the emergence and implementation of nonhuman animals' regime of rights, the changing directions of the U.S. Federal Reserve Bank and its impact on world politics, and alternative strategies for natural disasters in the United States. Our scholarship is thus distinctive for the ways in which it addresses American government and politics in a global context. This is how we seek to train our doctoral students on the rapidly changing, nuanced linkages between local, state, federal and global institutional politics.  

Political Theory Political Theory introduces students to the core normative issues in the study of political science. These normative issues provide the bedrock assumptions on which much of the study of political science depends. For example, while nearly everyone agrees that democracy is the best form of government, why do we place such faith in it? In addition, the long tradition of political thought offers multiple versions of democracy, each with its own strengths and limitations. How are we to identify the best version for our needs? Similarly, while we might extol non-violence in politics, is it always the best path for political movements? How are we to justify its alternatives? Clarifying our moral commitments, sharpening our conceptual tools, and outlining pathways for transforming theoretical knowledge into action requires philosophical, historical, and conceptual capabilities. The political theory faculty at the School of Interdisciplinary Global Studies trains students to develop these capabilities. To that end, political theory classes not only familiarize students with many of the canonical texts that were read by generations of prominent political thinkers (from Aristotle to Martin Luther King Jr), they also teach students to read these texts critically and with an eye towards contemporary political developments. As such, training in political theory is a critical supplement to graduate work at School of Interdisciplinary Global Studies. The faculty’s expertise in feminist theory, postcolonial theory, the role of emotions in politics, environmental political thought, and Indian political thought complements the terminal degrees offered in American Politics, Comparative Politics, and International Relations.

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Most of our successful applicants qualify for funding offered by the department or the Office of Graduate Studies. Funded doctoral students will receive a graduate assistantship that includes:

• a stipend for the academic year (9 months)
• a tuition waiver (not including school fees)
• the option of health insurance mostly paid by the department (the student only pays a small amount towards insurance).

All applicants for the doctoral degree are considered for a graduate assistantship - they do not need to complete a separate form.

The graduate assistantship is guaranteed for four years but is based on maintaining satisfactory annual academic progress. It requires each student to work 20 hours per week, in which case the student would be first assisting professors of the department with their teaching and class preparations and later, after having passed the doctoral comprehensive exams and completed teacher training seminars, teach a class at the University of South Florida. 

Please visit the graduate assistantships page for further information. The department also provides funding for conference travel or the presentation of research at conferences upon approval.

Information on eligibility for graduate assistantships can be found on the Graduate Assistantships Resource Center website. 

We also strive to fund our students in the fifth year, though this funding is not guaranteed. Depending on additional funds that become available, students may have the opportunity to extend their graduate assistantship to one, possibly two academic semesters. Students in the fifth year are also encouraged to seek external funding. For more information on this, please consult our Graduate Resources Page .

Outstanding candidates may also be nominated by the school’s director and/or graduate committee for prestigious and highly competitive university fellowships, including the Presidential Doctoral Fellowship , the Dorothy Auzenne Fellowship , and the University Graduate Fellowship. There is also the opportunity for minority students to be awarded a McKnight Fellowship, which provides annual tuition up to $5,000 for each of three academic years, plus an annual stipend of $12,000. The program also offers travel grants and other forms of financial support. For additional information on this fellowship opportunity, please visit the McKnight Fellowship's informational page.

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A Systematic Comparative Analysis of Doctor of Education (EdD) Programs

Unraveling inconsistencies and informing student choices.

Prospective doctoral students face a daunting challenge choosing between Doctor of Education (EdD) programs and Doctor of Philosophy (PhD) in Education due to programmatic ambiguity, inconsistency, and ill-defined career alignment (Carpenter, 1987; Perry, 2012; Shafer & Giblin, 2008). This qualitative study employed comparative analysis to explore the distinctions between 50 US EdD programs, including completion time, modality, credits, qualifying exam (QE) inclusion and requirements, and dissertation requirements. The theoretical framework used to investigate the root causes and potential outcomes of the EdD and PhD inconsistency included Foucault’s Power Theory (Aguirre Rojas, 2021) and Adam’s Equity Theory (Adams, 1963, 1965). Findings revealed significant differences between EdD programs and between EdD and PhD programmatic features. This data provides valuable insight for prospective students, informs EdD improvement, and urges consistency or standardization for clarity, integrity, and advancement in the field (Fisher et al., 2020; McMahon et al., 2020; Schafer & Giblin, 2008).

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Creswell, J. W., & Guetterman, T. C. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson

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DeWitt, S. (2016, October 14). PhD vs. EdD: Which terminal degree is right for you? Inside Higher Ed. https://www.insidehighered.com/blogs/gradhacker/phd-vs-edd

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Fisher, R., Brock, C. H., Frahm, T., Van Wig, A., & Gillis, V. R. (2020). Reflections on writing and identity: Exploring the role of qualifying exams in the sociocultural development of doctoral students. Studies in Continuing Education, 42(3), 365–380. https://doi.org/10.1080/0158037X.2019.1661237

Goodman, G. (2023, December 13). Experts discuss whether college is still worth it. Brookings. https://www.brookings.edu/articles/experts-discuss-whether-college-is-still-worth-it/

Kavakli, B. (2021, May 4). Transparency is no longer an option. It is a must. Forbes. https://www.forbes.com/sites/forbesbusinesscouncil/2021/05/04/transparency-is-no-longer-an-option-its-a-must/?sh=2f0162be75fe

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• Published: 30 April 2024

Victimisation in the life of persons with severe mental illness in Uganda: a pluralistic qualitative study

• Rwamahe Rutakumwa 1 ,
• Birthe Loa Knizek 2 ,
• Christine Tusiime 1 , 3 ,
• Richard Stephen Mpango 1 ,
• Carol Birungi 4 &
• Eugene Kinyanda 1 , 5  

BMC Psychiatry volume  24 , Article number:  329 ( 2024 ) Cite this article

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Introduction

Victimisation of persons with severe mental illness is recognised as an urgent global concern, with literature pointing to higher rates of violent victimisation of persons with severe mental illness than those of the general population. Yet, for low income countries, there is a huge gap in the literature on the risk, character and victims’ in-depth experiences of victimisation of persons with severe mental illness. We explore the lived experiences and meanings of victimisation of persons with severe mental illness in Uganda, and discuss their implications for care of the mentally ill.

A pluralistic qualitative study was undertaken to explore victimisation among patients with severe mental illness. Patients who had suffered victimisation were purposively sampled from Butabika National Referral Mental Clinic and Masaka Regional Referral Hospital, following confirmation of symptom remission. In-depth interviews were held with 18 participants, comprising 13 females and 5 males from low to moderate socioeconomic status. Interpretative phenomenological analysis and thematic content analysis were conducted.

Victimisation was exhibited in three main forms: (a) psychological, expressed in attitudes towards mentally ill family members as valueless and dispensable, and stigmatisation, (b) physical, as manifested in beatings, indoor confinement and tethering mostly by family members and (c) sexual victimisation, particularly rape. Also observed were victim’s various responses that pointed to the negative impact of victimisation, including a heightened risk of suicide, social withdrawal, a sense of hatefulness and a predisposition to more victimisation.

The family environment plays a predominant role in perpetrating victimisation of the mentally ill in some sub-Saharan African contexts such as Uganda. We propose a holistic framework for mental health interventions, incorporating biomedical but notably also social determinants of mental health, and targeted at improving familial relationships, social support and a sense of belongingness both within the family and the broader community.

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Victimisation of persons with severe mental illness (including bipolar disorder, schizophrenia and other psychotic disorders) has been recognised as an urgent global concern [ 1 ], with a review of literature unequivocally pointing to higher rates of violent victimisation of persons with severe mental illness than those of the general population [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. In a Dutch study, for example [ 4 ] the annual rate of prevalence of victimisation of outpatients with severe mental illness was 47% as compared to 32% of the general population. In a Greek study [ 10 ], 59% of persons living with severe mental illness reported having been victimised as compared to 46% of healthy controls. In the context of low and middle income countries (LMICs), there is a huge gap in the literature about the risk of violent victimisation of persons with severe mental illness [ 11 ]. Even so, findings from the few studies conducted in this region have been consistent with those from high income countries. For example, in an Ethiopian study, the prevalence rate of violent victimisation among persons with severe mental illness was 61% against 42% for those without the illness [ 11 ].

Certain risk factors have been identified in explaining predisposition to victimisation of persons with severe mental illness, including substance abuse, symptom severity, young age, unemployment, criminal history and homelessness [ 12 , 13 ]. Also well documented are the effects of victimisation, with the vice having been found to adversely impact the course of mental illness in the long term, further diminishing the quality of life of persons with severe mental illness and their families [ 2 , 14 , 15 ]. Relatedly, victimisation has been cited in exacerbating distress among the mentally ill [ 10 , 12 ] thereby aggravating the patients’ mental illness [ 12 , 16 ]. Furthermore, abuse of the mentally ill has been known to escalate the cost of healthcare and demand for services, putting a strain on the healthcare system [ 17 ], a phenomenon impacting sub-Saharan Africa even more given that mental health services in the region are still underdeveloped [ 18 , 19 , 20 ].

In spite of the apparently large volume of literature on victimisation of persons with severe mental illness, gaps in research on the subject still exist. First, much of the work on this subject has been conducted in high income countries, hence little is known about the situation in LMICs, where the sociocultural, structural and legal conditions impacting the risk of violence differ markedly from those in high income countries [ 11 ]. Indeed, this dearth of literature on LMICs was underscored by Tsigebrhan and colleagues [ 11 ] who as of 2014 were able to identify only one study in sub-Saharan Africa that focused on violent victimization of the mentally ill. Our current review of literature suggests very little change since then. Second, a lot more attention in these studies has been on persons with severe mental illness as perpetrators than as victims of the abuse [ 10 , 21 , 22 ]. Third, our review of literature suggests that majority of the publications are from studies using quantitative methods, implying that victims’ deeper experiences and interpretations of abuse, especially in the sub-Saharan African context, remain largely unexplored. Yet, this knowledge of the victims’ “life-world” [ 23 ] – that is, the way the phenomenon of victimisation appears to those experiencing it – is critical for designing fully informed, contextually appropriate interventions for addressing this important social problem. Fourth, literature has been scanty on the profile of abusers and how they relate to the victim [ 13 ].

Our purpose in this paper was to explore the lived experiences and meanings of victimisation of persons with severe mental illness in Uganda, including the history and the typical abuser, and to discuss their implications for care of the mentally ill.

This was a pluralistic qualitative sub-study to explore victimisation among patients with severe mental illness. The sub-study was an offshoot of a bigger project comprising two studies: (i) the Main Study which investigated the epidemiology of HIV infection and risky sexual behaviour among patients with severe mental illness (SMI) and (ii) the Clinical Trials Preparedness study which examined the feasibility and acceptability of undertaking clinical trials among patients with comorbid SMI/HIV [ 24 , 25 ].

Study setting

The study was conducted at Butabika National Psychiatric Referral Hospital and the Mental Health section of Masaka Regional Referral Hospital. Butabika Hospital is the only tertiary referral facility for psychiatric care in Uganda and handles patients from all over the country. In addition to the general psychiatric service, the hospital has specialised psychiatric units such as the forensic unit, alcohol and substance abuse unit, the child and adolescent unit and an infectious disease clinic (IDC clinic) that manages patients with comorbid SMI/HIV. Butabika Hospital also provides general outpatient services to the community through a daily out-patient clinic. Masaka Regional Referral Hospital, on the other hand, offers all services expected of a regional referral health facility, including psychiatric services for both adults and children. Psychiatric services at the Mental Health section of the hospital are provided by a team of psychiatric clinical officers and psychiatric nurses who receive quarterly support supervision from Butabika tertiary referral hospital.

The sample for this sub-study was drawn from a large pool of 1,201 participants who had been recruited for the Main Study and were attending mental health clinics at Butabika National Referral Mental Clinic and Masaka Regional Referral Hospital. The initial sample for the qualitative sub-study was purposive, comprising 409 (of the 1,201) participants who had met criteria for ‘ever suffered adulthood physical abuse’ and 263 participants from the same large sample who had ‘ever suffered adulthood sexual abuse’. In both cases, the participants had suffered the abuse before they acquired a mental illness or while they suffered a mental illness or both. From the purposive sample totalling 672 participants above, 18 participants were selected for the qualitative interviews on the day of their visitation at the mental health clinic following confirmation of symptom remission by an attending psychiatrist or psychiatric clinical officer. Selection of the participants was done in such a way as to ensure that the sample was fairly balanced in featuring both those with a history of physical and of sexual abuse. The total number of interviewees was determined when it became apparent that additional interviews were not yielding new information. No attempt was made to interview the primary caregiver, as this was not provided for in the study design.

Data collection procedures

In-depth interviews were conducted with the 18 participants reporting victimisation, which had been categorised as either physical or sexual. An interview guide was used to collect data. The guide was developed by the researcher who brainstormed and listed questions and topics based primarily on the study’s research questions, but also on scholarly literature on related previous work. Informed consent was obtained at least one week after individuals received the study information sheet in order to allow them sufficient time to read and internalise the content. Participants were interviewed at the health facility where they received care or any other venue of their choice by a trained research assistant who was either a psychiatric nurse or a clinical psychologist. The interview explored events leading to victimisation, how it occurred, the abuser, and the impact of such victimisation on the mental health of the participant. All the interviews were voice-recorded, upon obtaining consent from the respective participants.

Data management and analysis

Data were transcribed verbatim by the research assistant who conducted the interview and stored the data according to the MRC guidelines. We adopted a pluralistic analytical approach that combined interpretative phenomenological analysis (IPA) with thematic content analysis (TCA). As Frost [ 26 ] observes, the pluralistic approach enables flexibility “by building up multi-perspective layers of insight,” with each layer contributing to the understanding of the reality of those being studied. We used primarily IPA to explore the individuals’ lived experiences in depth, and to subsequently take the analysis to a broader level, where applicable, by using TCA to identify the broad themes that were emerging across the dataset. We conducted IPA as postulated by Smith and colleagues [ 27 ], in which the aim is to explore in detail participants’ life-worlds and the meanings they attach to these, in this case victimisation experiences of persons living with severe mental illness. The IPA has been found to be an appropriate method for analysing emotionally laden topics [ 28 ] pertinently including victimisation.

A detailed, case-by-case analysis was conducted by an experienced qualitative data analyst (first author) who read each of the transcripts iteratively to obtain a full picture of the participant’s experience of victimisation. While the analytical focus revolved around episodes of victimisation, both before and after onset of severe mental illness, analytical interest was also on issues that were found to have an influence on or were themselves influenced by victimisation. These were considered essential in constructing the participant’s entire story of victimisation and the meaning they made out of it, and included the participant’s life history, living conditions, family relationships, migration between households and livelihood activities. Within each individual’s narrative, nuances, patterns and themes were established. With the help of MS Excel, the analyst classified themes and sub-themes on a matrix, capturing each participant’s experiences (as reflected in verbatim quotes from the data) to illustrate the respective theme. These themes and sub-themes from each participant’s narrative/transcript were then discussed among team members before consensus was achieved. This was followed by the thematic content analysis phase, during which coding and analysis of the data was conducted across the individual cases with a view to identifying broader patterns and themes. This process also involved comparison of the themes from each individual’s narratives across the cases to establish consistencies and divergences, from which shared themes (those cutting across the different cases) were developed and illustrated by verbatim quotes from the data. The following three themes were discussed and eventually agreed upon by the research team as the key themes that constituted the essential character of the phenomenon of victimisation of persons with severe mental illness. They included psychological, physical and sexual victimisation.

Validity and reliability

Validity in qualitative research is about accuracy of the findings, while reliability speaks to consistency of the analytical procedures irrespective of the researcher or dataset involved [ 29 ]. We promoted internal validity by recruiting and interviewing study participants until such a time that data saturation was achieved, a point at which no new information was obtained from additional interviewing. Additionally, external validity or transferability (the degree to which the findings from qualitative research are generalizable or transferable to other contexts) – was enhanced by providing a “thick description” [ 30 ] or full account of our methodological procedures, participants and findings. Thick description would allow the reader to assess the extent to which our inferences and conclusions are transferable to other settings. Relatedly, reliability was upheld through a systematic documentation of the research process from data collection to analysis, thereby creating an audit trail to enable other researchers to make informed judgments about the soundness of the process, and to replicate these as needed.

As mentioned earlier, three forms of abuse were evident from the data, including psychological (emotional), physical and sexual victimisation. Psychological victimisation – defined as “the infliction of anguish, pain, or distress through verbal or non-verbal acts… [and] includes but is not limited to verbal assaults, insults, threats, intimidation, humiliation, and harassment” [ 31 ] – was usually exhibited in the demeaning and rejection of persons with severe mental illness. Physical victimisation manifested in the form of beatings including forced indoor confinement and tethering, while sexual victimisation basically took the form of rape at the hands of either a member of the public or, notably, a relative.

In Table  1 we summarise the broader demographic characteristics of our participants. Overall, 12 of the 18 study participants reported victimisation while suffering from severe mental illness. One participant reported having experienced all the above three forms of victimisation; seven participants reported two of the three forms of victimisation; and five participants reported one of the three forms of victimisation. Furthermore, a total of eight participants reported a history of victimisation, with four of these subsequently being victimised in their later years while suffering from severe mental illness. Ten participants reported victimisation as having impacted their lives. It is worth noting that participants in this study were not stratified by gender, as we did not aim at the outset to explore gender – only five of a total of 18 study participants were male. This notwithstanding, some gender-based difference in the experience of victimisation could still be gleaned from the data, whereby only female participants reported sexual victimisation.

In presenting our key findings below, we primarily focus in depth on specific cases and later, on findings from our analysis across the cases. The specific cases were carefully selected to highlight the full spectrum of victimisation as reported by our study participants. Our purpose in taking this mainly case-intensive approach was to shed light on the context of victimisation, not just the act of victimisation, that is to say the circumstances or events leading to and following victimisation as the act of victimisation in itself is hardly sufficient in understanding the entire phenomenon of victimisation. But this detailed focus on a select few individual cases was also in keeping with phenomenology’s “idiographic sensibility” that is, a mindfulness of the uniqueness of participants’ life-worlds and, therefore, the need for an in-depth exploration of each case as a way of gaining a full understanding of the case, and only then seeking to establish patterns and themes across the cases [ 32 , 33 ]. Moreover, the in-depth, case-intensive approach afforded us the ability to capture victimisation both in the context of severe mental illness and, where applicable, across the life course. The key themes and subthemes emerging from the data are summarised in Table  2 (below), and subsequently presented in more depth.

Key findings from an interpretative phenomenological analysis

Psychological victimisation.

Our analysis reveals that psychological victimisation was almost invariably blamed on the family – out of seven participants reporting this form of victimisation, six pointed to family as a source, with only two citing members of the public. Psychological victimisation manifested mainly in two ways. One of these was in attitudes towards mentally ill family members as valueless and dispensable. Two cases stood out in illustrating this type of psychological victimisation. One of the cases is that of Participant P1. This participant was a young woman in her second marriage. The marriage followed shortly after an earlier tumultuous marriage during which P1 gave birth to a baby at 15 years and conceived again eight months after delivery. This marriage would eventually end owing to her disagreement with the husband over his proposal to abort the second pregnancy. Although she was living with her parents at the time of this interview, P1 reported having developed mental illness during her second marriage, when she started “ walk[ing] through the streets of XX and all people got to know that [she] was mentally sick .” She clarified having relocated from her matrimonial home purposely for the treatment of her mental health condition, and that she routinely left the matrimonial home every time she experienced a relapse of her mental illness. The participant shared about her run-ins with the current husband who at times verbally assaulted her with reminders of her mental illness, which “ made [her] feel so bad .” It is these incidents, as P1 narrates, that eventually culminated in her husband asking her to go to her parents’ home for treatment during her latest illness episode. Yet, even at her parents’ home P1 still had distressful encounters with the caregiving mother:

Sometimes in the morning my next dose may be due, and I must take morning tablets. I may be in need of something to eat. She says there is nothing. So, instead of taking tablets twice a day, I only take for the night after getting something to eat…. Even when am sick she just tells me, ‘Go, buy poison and kill yourself’. Not that I always have it [money], but I may have Shillings10,000; she says ‘use it and buy poison and kill yourself’…. She means it! She tells me ‘Go, and buy… if you are to kill or not to kill yourself, don’t tell me. I have said go buy and drink it’… ‘if we surrendered you as a sacrifice, there is no problem’… I only want you people to talk to my mother on how to handle patients with mental illness. She gives me stress. She can tell me a word at a time when am sick and I hear something telling me to go and throw myself into a car [traffic].

As depicted in the above narrative, victimisation was apparent in terms of the patient’s psychological pain arising from a sense that her mental health condition was not being understood by the caregiver. Also notable in the excerpt is the victim’s allusion to the potential impact of the victimisation on prospects for her recovery and general wellbeing. This is particularly so when she expresses her perception of her mother rejecting and taunting her to end her life as a risk factor for suicide on account of its likelihood of triggering or coinciding with her hallucinations (the urge to throw herself into oncoming traffic).

What we read from the preceding excerpt is that victimisation can be uniquely impactful among some persons with severe mental illness. This is particularly so considering that the patient is dealing with a pre-existing health condition (severe mental illness) that primarily undermines their resilience or ability to adaptively navigate daily threats to their mental wellbeing. It was evident in this case that when such a patient encounters victimisation in any form, the impact may not always be attributable entirely to such victimisation but rather, to a synergy of factors, with victimisation only being an immediate cause.

Another case that illustrated attitudes of dispensability of mentally ill family members is that of P2, a female participant. This participant had been married for years and has children, but parted ways with the spouse on account of her mental illness. She also transferred her children to the care of her mother when she developed mental illness. At the time of this interview, P2 was living independently, and earned a living by engaging in trade. She shared about her life, highlighting significant historical events that could have led to her mental illness as well as her current experience with the mental illness and victimisation. Citing the former spouse as the abuser, she narrates about a fallout with him as a result of her mental health condition.

He [former spouse] married that woman like four years back…. Because right now they have three children. When I found him in the house, he told me: ‘omusajja tabeera n’abalalu’ meaning that ‘a man cannot live with a mad [mentally sick] person [wife]’. He told me to immediately leave his house. When he talked to me like that, I felt so stressed, and I relapsed and was brought back to hospital. Because of having very many thoughts about my husband, I would keep on relapsing; I was thinking about my children, I was not working at that time, it was the man [spouse] who used to look after us – so that made me relapse. After receiving treatment, I improved and was discharged. But on reaching home, now my children were looking up to me for food and all other requirements, which made me relapse again.

In this narrative, we observe that the participant was not only mistreated as worthless and dispensable because of her mental health condition but further asked to permanently leave her matrimonial home. Reflecting on this experience with the husband, the participant notably highlights her perception of a mutually reinforcing relationship between victimisation and mental illness. In this case, she attributes her victimisation – the psychological abuse of being denigrated by the husband and being evicted from her matrimonial home – to her mental illness, while at the same time blaming the victimisation for her (relapse in) mental illness. Victimisation, therefore, appeared to be perceived by the victim as resulting in a difficult-to-escape vicious cycle of mental illness. As well, the narrative brings to light the participant’s/victim’s sense of the multiple social factors that undermine recovery from mental illness, including poverty and related failure to take care of her offspring.

It is important to note that P2 not only encountered victimisation while suffering from severe mental illness; she also reported a history of sexual victimisation (rape) by the same man who would eventually marry her, only for him to ask her to leave their matrimonial home when she developed severe mental illness. In the following excerpt, for example, the participant narrates this history of victimisation and its impact, notably citing her mental illness as one such impact of being victimised.

When I think about that rape right now, I feel so bad…. I grew up not loving men. I was still a virgin. I had never loved any man [when I was raped]…[crying] … in fact [ever since that rape] I have never felt happy in my whole life [crying]. Not even on Christmas day have I ever been happy. That is the reason as to why I even became born again [crying]. Even after getting mental illness, I continued thinking about such abuses…. Those thoughts caused my mental illness because since that time I have never felt happy…. I still experience the thoughts about the abuse I suffered. [And] the person who would have comforted me would have been my husband, but we separated…. Whenever I think about this I cry.

Also remarkable is that the participant seemed to perceive the psychological victimisation – exhibited in her rejection by her former spouse – in more complex terms, not just in terms of the distress from being abandoned by a loved one because of a mental illness, which itself caused a series of relapses as reflected in her earlier narrative. The victimisation was also perceived in terms of denial by the spouse of a crucial form of (emotional) support that would have moderated the enduring psychological impact of the victimisation she had suffered in the earlier years (including rape) at the hands of the same man before the onset of her mental illness. We note, therefore, that the psychological pain the participant/victim suffered appeared to have been compounded by the ubiquity of the same man throughout her history of victimisation. This was not only the spouse who was rejecting her because of her mental illness, but he was the same man at whose hands she suffered rape several years before the onset of her mental illness. Indeed, we read from the victim’s account her sense that the very minimum the man behind her long history of victimisation, which culminated in mental illness, should have done was to provide emotional support.

Another way in which psychological victimisation manifested was stigmatisation. Two cases are featured to illustrate this type of abuse. One of which is that of Participant P3, a self-reliant, single young man who earned a living from selling produce from his own garden. This participant appeared to still be in denial about his condition when he disclosed: “ they [health workers] deceived me that it is mental illness…. I’m not very sure about my disorder .” Nonetheless, P3 reported having been on medication for his mental health condition but admitted occasionally not adhering, which had caused relapses. It was noteworthy that the participant’s non-adherence had been reportedly driven by concerns about the drugs’ sedative effects (excessive sleep), which often kept him away from income-generating activities. We observed from P3’s narrative that this motivation to work hard arose at least in part from the poor relationship he had had with his relatives and, consequently, the frustration that no one was there to help him. He expresses this frustration with a proposal for how health workers could help with relatives.

It would be good for a health worker after treating someone with mental illness to also take time and call up his or her relatives and advise them on how to look after such a person. If a mentally ill person asks for new clothes, the caretakers should be able to provide them with such needs. If you [health workers] counsel them on how to handle mentally sick persons even those cases of beating up mentally sick persons will not happen.

While P3 responded rather adaptively to the anguish from the perceived lack of support from relatives by working hard to be self-sufficient and not have to seek support from elsewhere, he helplessly lamented the apparent psychological victimisation he encountered from community members who habitually stigmatised him because of his mental illness. These sometimes described him overtly in his presence as a mentally disturbed person.

You know where I stay people take me as a mentally disturbed person, that affects me a lot, some talk when you are hearing, and you really feel bad. I even had to forego attending church because of the same, I have spent nearly a year without going to church…. I am a Christian and I used to go to church since childhood, but it got to a point when I felt like God had forgotten me and as such I did not need to go to church.

As with all other cases, our interest in this case was in the experience but also the impact of such victimisation. We characterised this impact in two ways. First, we note that in withdrawing from church engagements the participant might have delinked and lost contact with some community members who constituted a potential source of social support networks. Second, and more profoundly, we observe that another impact of this victimisation might have been in P3 being prompted to question the essence of his very existence when, as highlighted in the preceding excerpt, he expressed having felt forgotten by God. This point will be better appreciated in light of the exceptionally high degree of religiosity in Africa [ 34 ]. In such a setting, therefore, where a relationship with God is usually of existential importance, the decision by P3 to stop attending church because of the victimisation was interpreted as the culmination of an existential crisis in which he felt abandoned not only by family and community but also, perhaps more importantly, by God.

The second case illustrating victimisation by way of stigmatisation was that of Participant P1. As cited earlier, P1 was a young, married woman who had relocated to her parents’ home and was living with the parents. Like her counterpart, P3, this participant was one of few cases who experienced victimisation both within the family and the broader community contexts. In addition to the victimisation she suffered at the hands of a family member who treated her as valueless and dispensable, P1 narrated about her encounters of victimisation by way of stigmatisation. She cited some man within the community who tried without success to take advantage of her vulnerability due to mental illness by renewing his previously unsuccessful sexual advances towards her after she developed mental illness.

What I see especially among men who are adults [is that] they usually use this opportunity – the fact that you are sick with mental illness [and that] maybe you will have low self-esteem and accept him, especially if you had refused him before…. Because he may think you have already lost hope. There is one that sweet talked me and I refused. Then he got my phone number and called, and he said, “no wonder you got mad.” So, I said to myself that let me not get discouraged because of his words, or quarrel with him.

The case of P1 was particularly intriguing, in that unlike the normal expectation that such victimisation would worsen their mental health condition or at least undermine their recovery process by keeping them in a constant state of stress and hopelessness, the participant in this case seemed to have a sense of purpose, that is, to beat the odds and maintain the path to her recovery from mental illness. She did this by fighting back against her abusers in resisting sexual victimisation and not allowing the stigma to impact her mental health condition negatively.

Physical victimisation

Unlike psychological victimisation, physical victimisation was almost equally blamed on the family and the public. Six of nine participants reporting physical victimisation cited a family member as the culprit, while four of the nine blamed the abuse on members of the community/public who included a traditional healer. As indicated earlier, physical victimisation manifested in beatings, indoor confinement and tethering. We present two cases to illustrate this type of victimisation. The first is that of Participant P4, a male participant aged over 55 years. This participant reported having never married, and that neither had he been involved in a relationship with a woman in a very long time. He also cited a history of victimisation by his parents and his teachers during childhood. Yet these experiences notwithstanding, the participant reportedly would eventually grow up to become a successful farmer who grew crops for both home consumption and the market. However, P4 lamented about his experience of physical victimisation, particularly by his own brother, when he developed mental illness.

I was tied up in chains and left to stay indoors for a period of two months. I ended up destroying my own house because I was scared of dying in the house…. It was my elder brother. He beat me up and this finger is lame as a result of being beaten by him. He took my property and tied me up in chains for quite some time. He would not provide me with drinking water and would give very little food, just enough for survival, which he would pour on a banana leaf placed on the floor. And I would ease myself in the same place.

This participant’s experiences of victimisation stood out in several ways. First, as evident in the above excerpt, the character of the physical victimisation itself was complex, involving not just beatings but also indoor confinement using chains, as well as denial of nutrition and hydration. Second, contrary to what might have been expected, the abusive brother appeared to have made no effort to have P4 access health care, in effect contributing to the deterioration of P4’s mental but also physical health. This might explain another peculiar occurrence, namely that it would take the initiative of P4’s friends rather than of family members for him to be able to access health care.

I had my friends from church who understood that I had a mental problem, so they brought me here at [name of health facility]. During those days, our doctor used to come from Butabika on every first Tuesday of a month. When he assessed me, he advised them to take me to Butabika for further assessments. They then took me to Butabika where I spent close to one month receiving treatment.

Reflecting on his experiences of victimisation following his mental illness, P4 appeared to be most concerned about the impact the abuse had had on his financial/economic security. He narrated about the economic setbacks he has suffered because of the illness.

All my plans for my financial welfare were ruined by relatives and the community members. I had a plantation of sugar canes, bananas, and many fruits such as jackfruits which were ransacked by people after knowing that I was receiving treatment from Butabika Hospital…. I have suffered a lot!! People always take my property. Whatever I try to do at home people steal it. They steal things like food crops and fruits. So that has made me lag behind in terms of financial stability.

Although financial victimisation did not feature prominently in our data, it was clear from the preceding excerpt that this form of abuse by notably family members, in synergy with the physical victimisation, might have to a considerable extent adversely impacted the trajectory of P4’s recovery and life in general. This is in light of the fact that, as the participant revealed, his property or resources that he would have needed to transition back to mental stability and normal life, including marrying and having children, was lost to his family members.

The second case featuring physical victimisation was that of Participant P5, an unmarried woman in her late 30s. Having dropped out of school because her parents could not afford school fees, this participant would later marry formally and start a family but did not have a child after almost a decade in marriage. The marriage eventually ended, and the former husband married another woman. At the time of this interview P5 was living with her grandmother. On how she earned a living, P5 shared about her involvement in cultivation on land owned by the grandmother, but also highlighted the difficulty she and her grandmother were going through in an effort to make ends meet.

The participant reported no history of victimisation, adding that her experience of being victimised was within the context of her severe mental illness in which the husband was the abuser. Indeed, as she revealed, while the termination of her marriage was against the background of years of marital disharmony over her inability to conceive, the husband’s decision to end the marriage was in fact prompted by P5’s mental illness. She described the abuse by the husband preceding the termination of the marriage:

He [husband] used a stick to beat me up…. He was asking me why I had burnt the clothes, he decided to beat me up. In fact, he first tied me up with a rope and then beat me up with a stick. After beating me up he then abused me sexually; he had tied my legs.

The participant went further to explain:

[When I fell sick] he should have looked after me, he should not have beaten me up. He beat me up and abused me sexually which made me hate him. At that time, I was not understanding properly – I had a small lamp which accidentally started a fire on a mosquito net and by the time I tried to put off the fire most clothes had been burnt except the bed sheets. As I finished rescuing the children most of the clothes had been burnt, but surprisingly when he came back instead of appreciating my efforts he just beat me up and abused me sexually.

In the preceding excerpt, the participant laments the failure by the husband to understand her condition as someone with mental illness, noting that she did not deserve the violence from the husband whose children she had rescued. She also highlights the immediate impact of her victimisation by the husband, including “ hatefulness ” towards him. Although she later added that she had got over this traumatic phase of her life and moved on, her lamentation in the same excerpt was indicative of the enduring psychological impact the victimisation had on her.

Sexual victimisation

Of the three forms of victimisation presented in this paper, sexual victimisation was the least reported, with only five of the 18 study participants disclosing having encountered this form of victimisation. All the victims of sexual victimisation were female. It was remarkable that two of these suffered the abuse at the hands of a family member. We present below Participant P6, one of the five cases of sexual victimisation. P6 pointed to a history of victimisation, intimating that as a young girl she was sexually victimised by a man who used to visit her aunt’s home where she lived. She was a middle-aged woman with adult children, and pregnant at the time of the interview. This participant’s situation was rather nuanced, in that she disclosed having separated with her husband and no more sexual contact with him for the last few months, but that the two still lived together. Moreover, the husband had already expressed a strong will to divorce P6 because he was reportedly “ fed up with [her] behaviour .”

However, further analysis of data revealed that the baby that P6 was carrying might not have been the husband’s. Indeed, while the husband by implication acknowledged his paternity of the unborn child when he accompanied P6 to hospital and expressed to the health workers that he “ wanted us to stop on that number of children ,” P6 disclosed to the interviewer that the unborn child was her pastor’s. She further narrated the process leading to her sexual encounter with the pastor, revealing that she had sought counselling from him on how she might get her husband to accept conceiving one more child, but that instead the pastor visited her at home and engaged her in unprotected sex.

The participant also disclosed having had an earlier extramarital affair with another man when she and the husband lived separate lives but still shared a home. When rationalising the extramarital sexual relationship with the man, P6 depicted the husband as a man who was gradually abandoning his responsibilities and commitment to his wife. This depiction hinted at the thought process leading to her decision to accept the new man’s sexual advances, suggesting that her acceptance was, at least in part, the result of her perception of suboptimal care from her husband and the desire to make up for the shortfall in her care needs.

You see, for him he would use the trick of providing me with something to eat so I accepted [a sexual relationship with him] because I would be hungry, and I would want eats…. I would complain to him how my husband was making us to stay hungry during the day.

In this participant’s case, we observe the participant’s sense of victimisation by the husband by way of withholding assistance and marital commitment to his wife. But, despite what appeared like a consensual relationship as projected in the preceding excerpt, the participant also perceives victimisation by the man who was sexually exploiting her because of her vulnerability arising from her mental health condition and nutritional deprivation:

Sometimes he would want to force me – like when I was seven months pregnant, he forced me to have sex with him yet I had abdominal pains but he kept insisting on me having sex with him…. I felt very bad…. [He] used to force me against my own will…. Whenever I would ask him why he did it he would tell me that it was because he loved me so much, yet he had a wife, and I also had a husband.

In the context of P6’s severe mental illness and gaps in family support for mentally ill family members, we consider the victimisation by the two men who were sexually exploiting P6 as having been enabled by her husband’s abuse. This was reflected in her justification for her involvement with the men.

Interviewer: What do you think pushes you to have sex with other men, yet you are married? Respondent: I think it is because they are helping you in one way or the other…. I think in one way or another those people are helpful to the mentally sick person.

In this case, the husband’s withholding of assistance to P6 appears to have created a support gap of which the men were taking advantage.

Key findings from a thematic content analysis

Failure to be understood by family members.

One of the key findings from our thematic analysis of data across the different cases was the participants’/patients’ concern at the failure to be understood by family members. This concern was reflected in the narratives of all the cases presented except one (P3). Also of note was that the failure to be understood was cross-cutting, affecting both those patients in spousal and in blood relationships. We observed that the failure to be understood constituted an important source of stress on the part of the patients. It was also noted that all the cases reflecting this form of victimisation, except one, were females. Failure to be understood by family members emerged as a key factor undermining prospects for recovery, as already presented, often leading to physical violence and denial of basic necessities (food and water) by family members. Cases in point, presented in detail earlier, include P1, who appealed to the health workers to educate caregivers about how to handle mentally ill relatives. They also include P2, who lamented about the rejection by the spouse and his denial of even emotional support, in spite of him having raped her as a young girl and taken her for a wife. The other case is that of P5 who reported about the husband beating her up supposedly for not taking enough care to prevent a house fire when in fact, as she reasoned, she did her best given her mental health condition.

Differential experiences of victimization of women with severe mental illness

Another notable observation from our thematic analysis were the differential experiences of victimisation of women with severe mental illness. These differential experiences were based on the kind of relationship: we noticed that while all the mentally ill women who endured victimisation at the hands of blood relatives were still kept in the family fold and continued to receive some social care from the abusive family members, the women who suffered victimisation at the hands of a spouse were rejected by their spouses and made to leave their matrimonial homes. Cases in point, presented in detail earlier, include P1, who was victimised and ultimately asked by the spouse to leave their matrimonial home. However, when P1 moved in and started living with her mother, she still suffered victimisation at the hands of the mother but was notably tolerated. Another case is that of P2 who was also asked by the spouse to leave their matrimonial home after being told bluntly that a man cannot live with a mad wife, even though the man had raped her as a young girl and eventually took her for a wife. We note that the case of P6 yet again demonstrates differential experiences of victimisation of women with severe mental illness, with those in matrimonial homes more likely to encounter harsher treatment within the family than those living with blood relatives. Akin to the cases of P1 and P2, we observed in P6’s case perhaps the beginnings of the process that might lead to eventual termination of her marriage. This observation may be further appreciated in light of (i) her disclosure of the husband having stated that he wanted a divorce because he was tired of her behaviour, and (ii) the fact that the husband was restricting her food intake, which prompted her to accept extramarital sexual advances from other men. Our presumption was that the two still lived together following the husband’s calculated decision to delay the divorce process because she was carrying his baby.

In this paper, we set out to explore the lived experiences and meanings of victimisation of persons living with severe mental illness, including the history and the typical abuser, and to discuss their implications for care of the mentally ill. To explore these, we used an interpretative phenomenological approach during data collection, but adopted a pluralistic analysis featuring both interpretative phenomenological analysis (IPA) and thematic content analysis (TCA). Our aim was to leverage the strength of both analytical approaches, conducting IPA for depth and TCA for breadth in our understanding of the subject matter. With 12 of this study’s 18 participants reporting victimisation while suffering from mental illness, our findings build up on the large body of work that has been done globally [ 2 , 3 , 4 , 5 , 11 ] in pointing to the high prevalence of victimisation among the mentally ill.

Literature on victimisation of the mentally ill within the mental health care system has been scarce, with the incidence of victimisation of patients in the mental health care settings remaining largely obscure [ 35 ]. However, the reverse has been true in contexts outside of the mental health care system in both high income countries [ 36 ] and LMICs [ 11 , 37 ] where the perpetrators within the community have been extensively described, including intimate partners and other family members. Even then, our study contributes to the literature by illuminating the predominance of the family environment in perpetrating victimisation of the mentally ill in some sub-Saharan African contexts. This is especially so given that we were unable to identify any sub-Saharan African study reporting similar findings, but also in light of the depiction in the literature of the protective character of the family social system in the African setting [ 38 ]. Relatedly, our findings point to the importance of lack of social support within family and the victimisation associated with this in determining the recovery trajectory of persons with severe mental illness. Both the female patient who expressed the enduring psychological pain of being physically assaulted by the husband because of a house fire, and another who agonised about being habitually taunted by the caregiver to take her own life because she was too demanding, for example, alluded to this victimisation as negatively impacting their recovery. This finding echoes those from a systematic review by Rani and colleagues [ 39 ].

Our findings have also revealed some patterns of experience among women with mental illness that point to some notable intra-gender heterogeneities. The findings showed that mentally ill women living in matrimonial relationships were more prone to extreme forms of victimisation than those living with blood relatives. To the spouse, a woman’s mental illness was intolerable and justified her ejection from the matrimonial home, whereas to blood relatives, she was tolerated and, despite reports of victimisation of the mentally ill adult, the relatives always made room for her as a family member. Previous studies [ 40 , 41 , 42 , 43 ] have investigated the lived experiences of caregivers of the mentally ill but have also noted the “surprising” paucity of literature on this subject given the frequent occurrence of mental illness [ 40 ]. Our findings build on this literature by bringing to light the heterogeneities in the victimisation experiences of women, with important implications for care of the mentally ill.

On the other hand, by revealing men’s intolerance towards their mentally ill female spouses our findings lend credence to what Hailemariam and colleagues [ 44 ], in their rural Ethiopian study on gender, mental illness and marriage, categorise as “gendered experiences of marriageability.” By this the authors refer to the unique challenge mentally ill women encounter being in a spousal relationship owing to their incapacity to conform to gendered sociocultural obligations within marriage. Similarly, our findings are consistent with the preceding authors’ in pointing out the inability of a woman to sustain her marriage in the wake of her mental illness. Hailemariam and colleagues [ 44 ] have sought to explain this phenomenon, particularly in the sub-Saharan African context, from a normative cultural perspective. They observe the gendered double standards by which marital separation is culturally acceptable due to a wife’s mental illness, but unacceptable when it is the husband with the mental illness, because in the latter case society expects the woman to take care of the ill husband.

With regard to coping ability, our findings highlighted gendered experiences, with men depicting higher resilience and coping abilities. Nonetheless, we identified behaviour among men that might have resulted in loss of social support from at least some community members, as reflected in total withdrawal from community/church engagement on account of being overtly labelled a mentally ill person. This finding echoes those from previous studies [ 45 , 46 ] in pointing to the importance of stigma in mental illness, insofar as complicating the recovery process even for those who may be responding well to treatment and regaining normal functioning.

Our findings also illuminated the important role of basic human needs – food, water, sex and others – in the management of mental illness and shaping the recovery trajectory. While these basic/physiological needs are fundamental to human survival and general well-being [ 47 ] our study shows that these are even more key to the stability and recovery of persons with severe mental illness. This was illustrated by the woman who endured sometimes painful extramarital sex in exchange for food, as her husband was restricting her access to food. Our findings are consistent with those from previous work such as that of Williams and colleagues [ 48 ] in pointing to the importance of meeting basic needs in improving the quality of life of persons with severe mental illness. Relatedly, the failure to be understood was repeatedly highlighted in different patients’ narratives as a source of stress, raising concerns about poor coping and treatment outcomes. This echoes findings from Gaillard and colleagues’ [ 49 ] study where misunderstood mental health patients expressed frustrations of being “an object to be fixed, treated like a child.” The authors recommended efforts to heighten understanding and improve therapeutic relationships as a means of enhancing care for the mentally ill.

Implications for care

Strategies for addressing victimisation of persons with severe mental illness in Uganda need to be sensitive to the local realities, mainly the sociocultural practices and beliefs. They also need to reflect an understanding of mentally ill women as a heterogeneous group, with dissimilar experiences based on family environment and relationships. Necessarily, these strategies would adopt a holistic framework for mental health interventions, incorporating not just biomedical but also social determinants of mental health. An intervention framework incorporating social determinants would be predicated on the understanding that the environment in which individuals live and work influences their health outcomes [ 50 ]. Thus, interventions for addressing victimisation of the mentally ill would focus on improving familial relationships, and social support and a sense of belongingness both within the family and the broader community, as these and related interventions have been found to be protective against adverse mental health outcomes [ 51 , 52 ]. Indeed, these protective factors are recognised as being embedded in the African way of life [ 52 ]. For their part, mental health professionals would need to standardise “victimisation” in their tools for the assessment and psychosocial management of persons with severe mental illness.

However, our findings support those from previous work [ 53 , 54 ] in revealing that some environments in sub-Saharan Africa, especially in the rural settings, are not necessarily always protective with respect to mental health. With this in mind, we propose realistic interventions that take advantage of and strengthen the protective aspects of the cultural and family support system, while seeking to change those aspects with deleterious effects on the recovery and wellbeing of those with mental illness – such as beliefs/norms around dealing with a mentally ill wife.

Victimisation of persons with severe mental illness is an important social problem in sub-Saharan African settings such as Uganda, where the family environment plays a predominant role in its perpetration. Victimisation of the mentally ill has been found to impact negatively on their recovery and general wellbeing, by triggering victims’ harmful responses including a heightened risk of suicide, social withdrawal, a sense of hatefulness and worthlessness and a predisposition to more victimisation. We propose a holistic framework for mental health interventions, incorporating not just biomedical but also social determinants of mental health, and targeted at improving familial relationships, social support and a sense of belongingness both within the family and the broader community.

Limitations of the study

A limitation of this study was that we did not aim at the outset to explore gender, and consequently our sample was not stratified to ensure gender balance. With only five of a total of 18 study participants being male we feel constrained in making substantive inferences on gender. We therefore propose similar studies in Uganda and the broader sub-Saharan African region that explore gender in more depth, especially given that we were still able to observe from our data some gender-based differences in the experience of victimisation.

Data availability

The datasets generated and/or analysed during the current study are accessible at https://doi.org/10.17037/DATA.00002840 .

Abbreviations

Severe Mental Illness

Low and Middle Income Countries

Human Immunodeficiency Virus

Infectious Disease Clinic

Interpretative Phenomenological Analysis

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Acknowledgements

We acknowledge the contribution of managers of the two study sites (Butabika National Psychiatric Referral Hospital and Masaka Regional Referral Hospital) in granting permission for undertaking the study in these facilities. We also acknowledge the contribution of MRC/UVRI & LSHTM which funded and facilitated the study. Furthermore, our appreciation extends to staff of the outpatient departments of the facilities where the study was conducted, as well as the study participants for their time.

This study was funded by MRC core funding to the Mental Health Project of MRC/UVRI and LSHTM under the headship of Professor Eugene Kinyanda to undertake the ‘HIV clinical trials preparedness studies among patients with Severe Mental ILlnEss in HIV endemic Uganda (SMILE Study).

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R.R. analysed data, developed the idea for the topic of the manuscript, drafted the original manuscript and subsequent revisions. B.L.K., C.T., R.S.M., C.B., E.K. contributed in reviewing the different versions of the manuscript. C.T., R.S.M. and C.B. conducted the research. E.K. provided overall leadership and secured financial support for the project leading to this publication. All authors read and approved the final manuscript.

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Rutakumwa, R., Knizek, B.L., Tusiime, C. et al. Victimisation in the life of persons with severe mental illness in Uganda: a pluralistic qualitative study. BMC Psychiatry 24 , 329 (2024). https://doi.org/10.1186/s12888-024-05720-4

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Received : 05 November 2023

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DOI : https://doi.org/10.1186/s12888-024-05720-4

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