comparative analysis qualitative research

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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

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• An introduction to applied data analysis with qualitative comparative analysis
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• Transl Behav Med
• v.4(2); 2014 Jun

Using qualitative comparative analysis to understand and quantify translation and implementation

Heather kane.

RTI International, 3040 Cornwallis Road, Research Triangle Park, P.O. Box 12194, Durham, NC 27709 USA

Megan A Lewis

Pamela a williams, leila c kahwati.

Understanding the factors that facilitate implementation of behavioral medicine programs into practice can advance translational science. Often, translation or implementation studies use case study methods with small sample sizes. Methodological approaches that systematize findings from these types of studies are needed to improve rigor and advance the field. Qualitative comparative analysis (QCA) is a method and analytical approach that can advance implementation science. QCA offers an approach for rigorously conducting translational and implementation research limited by a small number of cases. We describe the methodological and analytic approach for using QCA and provide examples of its use in the health and health services literature. QCA brings together qualitative or quantitative data derived from cases to identify necessary and sufficient conditions for an outcome. QCA offers advantages for researchers interested in analyzing complex programs and for practitioners interested in developing programs that achieve successful health outcomes.

INTRODUCTION

In this paper, we describe the methodological features and advantages of using qualitative comparative analysis (QCA). QCA is sometimes called a “mixed method.” It refers to both a specific research approach and an analytic technique that is distinct from and offers several advantages over traditional qualitative and quantitative methods [ 1 – 4 ]. It can be used to (1) analyze small to medium numbers of cases (e.g., 10 to 50) when traditional statistical methods are not possible, (2) examine complex combinations of explanatory factors associated with translation or implementation “success,” and (3) combine qualitative and quantitative data using a unified and systematic analytic approach.

This method may be especially pertinent for behavioral medicine given the growing interest in implementation science [ 5 ]. Translating behavioral medicine research and interventions into useful practice and policy requires an understanding of the implementation context. Understanding the context under which interventions work and how different ways of implementing an intervention lead to successful outcomes are required for “T3” (i.e., dissemination and implementation of evidence-based interventions) and “T4” translations (i.e., policy development to encourage evidence-based intervention use among various stakeholders) [ 6 , 7 ].

Case studies are a common way to assess different program implementation approaches and to examine complex systems (e.g., health care delivery systems, interventions in community settings) [ 8 ]. However, multiple case studies often have small, naturally limited samples or populations; small samples and populations lack adequate power to support conventional, statistical analyses. Case studies also may use mixed-method approaches, but typically when researchers collect quantitative and qualitative data in tandem, they rarely integrate both types of data systematically in the analysis. QCA offers solutions for the challenges posed by case studies and provides a useful analytic tool for translating research into policy recommendations. Using QCA methods could aid behavioral medicine researchers who seek to translate research from randomized controlled trials into practice settings to understand implementation. In this paper, we describe the conceptual basis of QCA, its application in the health and health services literature, and its features and limitations.

CONCEPTUAL BASIS OF QCA

QCA has its foundations in historical, comparative social science. Researchers in this field developed QCA because probabilistic methods failed to capture the complexity of social phenomena and required large sample sizes [ 1 ]. Recently, this method has made inroads into health research and evaluation [ 9 – 13 ] because of several useful features as follows: (1) it models equifinality , which is the ability to identify more than one causal pathway to an outcome (or absence of the outcome); (2) it identifies conjunctural causation , which means that single conditions may not display their effects on their own, but only in conjunction with other conditions; and (3) it implies asymmetrical relationships between causal conditions and outcomes, which means that causal pathways for achieving the outcome differ from causal pathways for failing to achieve the outcome.

QCA is a case-oriented approach that examines relationships between conditions (similar to explanatory variables in regression models) and an outcome using set theory; a branch of mathematics or of symbolic logic that deals with the nature and relations of sets. A set-theoretic approach to modeling causality differs from probabilistic methods, which examines the independent, additive influence of variables on an outcome. Regression models, based on underlying assumptions about sampling and distribution of the data, ask “what factor, holding all other factors constant at each factor’s average, will increase (or decrease) the likelihood of an outcome .” QCA, an approach based on the examination of set, subset, and superset relationships, asks “ what conditions —alone or in combination with other conditions—are necessary or sufficient to produce an outcome .” For additional QCA definitions, see Ragin [ 4 ].

Necessary conditions are those that exhibit a superset relationship with the outcome set and are conditions or combinations of conditions that must be present for an outcome to occur. In assessing necessity, a researcher “identifies conditions shared by cases with the same outcome” [ 4 ] (p. 20). Figure  1 shows a hypothetical example. In this figure, condition X is a necessary condition for an effective intervention because all cases with condition X are also members of the set of cases with the outcome present; however, condition X is not sufficient for an effective intervention because it is possible to be a member of the set of cases with condition X, but not be a member of the outcome set [ 14 ].

Necessary and sufficient conditions and set-theoretic relationships

Sufficient conditions exhibit subset relationships with an outcome set and demonstrate that “the cause in question produces the outcome in question” [ 3 ] (p. 92). Figure  1 shows the multiple and different combinations of conditions that produce the hypothetical outcome, “effective intervention,” (1) by having condition A present, (2) by having condition D present, or (3) by having the combination of conditions B and C present. None of these conditions is necessary and any one of these conditions or combinations of conditions is sufficient for the outcome of an effective intervention.

QCA AS AN APPROACH AND AS AN ANALYTIC TECHNIQUE

The term “QCA” is sometimes used to refer to the comparative research approach but also refers to the “analytic moment” during which Boolean algebra and set theory logic is applied to truth tables constructed from data derived from included cases. Figure  2 characterizes this distinction. Although this figure depicts steps as sequential, like many research endeavors, these steps are somewhat iterative, with respecification and reanalysis occurring along the way to final findings. We describe each of the essential steps of QCA as an approach and analytic technique and provide examples of how it has been used in health-related research.

QCA as an approach and as an analytic technique

Operationalizing the research question

Like other types of studies, the first step involves identifying the research question(s) and developing a conceptual model. This step guides the study as a whole and also informs case, condition (c.f., variable), and outcome selection. As mentioned above, QCA frames research questions differently than traditional quantitative or qualitative methods. Research questions appropriate for a QCA approach would seek to identify the necessary and sufficient conditions required to achieve the outcome. Thus, formulating a QCA research question emphasizes what program components or features—individually or in combination—need to be in place for a program or intervention to have a chance at being effective (i.e., necessary conditions) and what program components or features—individually or in combination—would produce the outcome (i.e., sufficient conditions). For example, a set theoretic hypothesis would be as follows: If a program is supported by strong organizational capacity and a comprehensive planning process, then the program will be successful. A hypothesis better addressed by probabilistic methods would be as follows: Organizational capacity, holding all other factors constant, increases the likelihood that a program will be successful.

For example, Longest and Thoits [ 15 ] drew on an extant stress process model to assess whether the pathways leading to psychological distress differed for women and men. Using QCA was appropriate for their study because the stress process model “suggests that particular patterns of predictors experienced in tandem may have unique relationships with health outcomes” (p. 4, italics added). They theorized that predictors would exhibit effects in combination because some aspects of the stress process model would buffer the risk of distress (e.g., social support) while others simultaneously would increase the risk (e.g., negative life events).

Identify cases

The number of cases in a QCA analysis may be determined by the population (e.g., 10 intervention sites, 30 grantees). When particular cases can be chosen from a larger population, Berg-Schlosser and De Meur [ 16 ] offer other strategies and best practices for choosing cases. Unless the number of cases relies on an existing population (i.e., 30 programs or grantees), the outcome of interest and existing theory drive case selection, unlike variable-oriented research [ 3 , 4 ] in which numbers are driven by statistical power considerations and depend on variation in the dependent variable. For use in causal inference, both cases that exhibit and do not exhibit the outcome should be included [ 16 ]. If a researcher is interested in developing typologies or concept formation, he or she may wish to examine similar cases that exhibit differences on the outcome or to explore cases that exhibit the same outcome [ 14 , 16 ].

For example, Kahwati et al. [ 9 ] examined the structure, policies, and processes that might lead to an effective clinical weight management program in a large national integrated health care system, as measured by mean weight loss among patients treated at the facility. To examine pathways that lead to both better and poorer facility-level weight loss, 11 facilities from among those with the largest weight loss outcomes and 11 facilities from among those with the smallest were included. By choosing cases based on specific outcomes, Kahwati et al. could identify multiple patterns of success (or failure) that explain the outcome rather than the variability associated with the outcome.

Identify conditions and outcome sets

Selecting conditions relies on the research question, conceptual model, and number of cases similar to other research methods. Conditions (or “sets” or “condition sets”) refer to the explanatory factors in a model; they are similar to variables. Because QCA research questions assess necessary and sufficient conditions, a researcher should consider which conditions in the conceptual model would theoretically produce the outcome individually or in combination. This helps to focus the analysis and number of conditions. Ideally, for a case study design with a small (e.g., 10–15) or intermediate (e.g., 16–100) number of cases, one should aim for fewer than five conditions because in QCA a researcher assesses all possible configurations of conditions. Adding conditions to the model increases the possible number of combinations exponentially (i.e., 2 k , where k = the number of conditions). For three conditions, eight possible combinations of the selected conditions exist as follows: the presence of A, B, C together, the lack of A with B and C present, the lack of A and lack of B with C present, and so forth. Having too many conditions will likely mean that no cases fall into a particular configuration, and that configuration cannot be assessed by empirical examples. When one or more configurations are not represented by the cases, this is known as limited diversity, and QCA experts suggest multiple strategies for managing such situations [ 4 , 14 ].

For example, Ford et al. [ 10 ] studied health departments’ implementation of core public health functions and organizational factors (e.g., resource availability, adaptability) and how those conditions lead to superior and inferior population health changes. They operationalized three core public functions (i.e., assessment of environmental and population public health needs, capacity for policy development, and authority over assurance of healthcare operations) and operationalized those for their study by using composite measures of varied health indicators compiled in a UnitedHealth Group report. In this examination of 41 state health departments, the authors found that all three core public health functions were necessary for population health improvement. The absence of any of the core public health functions was sufficient for poorer population health outcomes; thus, only the health departments with the ability to perform all three core functions had improved outcomes. Additionally, these three core functions in combination with either resource availability or adaptability were sufficient combinations (i.e., causal pathways) for improved population health outcomes.

Calibrate condition and outcome sets

Calibration refers to “adjusting (measures) so that they match or conform to dependably known standards” and is a common way of standardizing data in the physical sciences [ 4 ] (p. 72). Calibration requires the researcher to make sense of variation in the data and apply expert knowledge about what aspects of the variation are meaningful. Because calibration depends on defining conditions based on those “dependably known standards,” QCA relies on expert substantive knowledge, theory, or criteria external to the data themselves [ 14 ]. This may require researchers to collaborate closely with program implementers.

In QCA, one can use “crisp” set or “fuzzy” set calibration. Crisp sets, which are similar to dichotomous categorical variables in regression, establish decision rules defining a case as fully in the set (i.e., condition) or fully out of the set; fuzzy sets establish degrees of membership in a set. Fuzzy sets “differentiate between different levels of belonging anchored by two extreme membership scores at 1 and 0” [ 14 ] (p.28). They can be continuous (0, 0.1, 0.2,..) or have qualitatively defined anchor points (e.g., 0 is fully out of the set; 0.33 is more out than in the set; 0.66 is more in than out of the set; 1 is fully in the set). A researcher selects fuzzy sets and the corresponding resolution (i.e., continuous, four cutoff points, six cutoff) based on theory and meaningful differences between cases and must be able to provide a verbal description for each cutoff point [ 14 ]. If, for example, a researcher cannot distinguish between 0.7 and 0.8 membership in a set, then a more continuous scoring of cases would not be useful, rather a four point cutoff may better characterize the data. Although crisp and fuzzy sets are more commonly used, new multivariate forms of QCA are emerging as are variants that incorporate elements of time [ 14 , 17 , 18 ].

Fuzzy sets have the advantage of maintaining more detail for data with continuous values. However, this strength also makes interpretation more difficult. When an observation is coded with fuzzy sets, a particular observation has some degree of membership in the set “condition A” and in the set “condition NOT A.” Thus, when doing analyses to identify sufficient conditions, a researcher must make a judgment call on what benchmark constitutes recommendation threshold for policy or programmatic action.

In creating decision rules for calibration, a researcher can use a variety of techniques to identify cutoff points or anchors. For qualitative conditions, a researcher can define decision rules by drawing from the literature and knowledge of the intervention context. For conditions with numeric values, a researcher can also employ statistical approaches. Ideally, when using statistical approaches, a researcher should establish thresholds using substantive knowledge about set membership (thus, translating variation into meaningful categories). Although measures of central tendency (e.g., cases with a value above the median are considered fully in the set) can be used to set cutoff points, some experts consider the sole use of this method to be flawed because case classification is determined by a case’s relative value in regard to other cases as opposed to its absolute value in reference to an external referent [ 14 ].

For example, in their study of National Cancer Institutes’ Community Clinical Oncology Program (NCI CCOP), Weiner et al. [ 19 ] had numeric data on their five study measures. They transformed their study measures by using their knowledge of the CCOP and by asking NCI officials to identify three values: full membership in a set, a point of maximum ambiguity, and nonmembership in the set. For their outcome set, high accrual in clinical trials, they established 100 patients enrolled accrual as fully in the set of high accrual, 70 as a point of ambiguity (neither in nor out of the set), and 50 and below as fully out of the set because “CCOPs must maintain a minimum of 50 patients to maintain CCOP funding” (p. 288). By using QCA and operationalizing condition sets in this way, they were able to answer what condition sets produce high accrual, not what factors predict more accrual. The advantage is that by using this approach and analytic technique, they were able to identify sets of factors that are linked with a very specific outcome of interest.

Obtain primary or secondary data

Data sources vary based on the study, availability of the data, and feasibility of data collection; data can be qualitative or quantitative, a feature useful for mixed-methods studies and systematically integrating these different types of data is a major strength of this approach. Qualitative data include program documents and descriptions, key informant interviews, and archival data (e.g., program documents, records, policies); quantitative data consists of surveys, surveillance or registry data, and electronic health records.

For instance, Schensul et al. [ 20 ] relied on in-depth interviews for their analysis; Chuang et al. [ 21 ] and Longest and Thoits [ 15 ] drew on survey data for theirs. Kahwati et al. [ 9 ] used a mixed-method approach combining data from key informant interviews, program documents, and electronic health records. Any type of data can be used to inform the calibration of conditions.

Assign set membership scores

Assigning set membership scores involves applying the decision rules that were established during the calibration phase. To accomplish this, the research team should then use the extracted data for each case, apply the decision rule for the condition, and discuss discrepancies in the data sources. In their study of factors that influence health care policy development in Florida, Harkreader and Imershein [ 22 ] coded contextual factors that supported state involvement in the health care market. Drawing on a review of archival data and using crisp set coding, they assigned a value of 1 for the presence of a contextual factor (e.g., presence of federal financial incentives promoting policy, unified health care provider policy position in opposition to state policy, state agency supporting policy position) and 0 for the absence of a contextual factor.

Construct truth table

After completing the coding, researchers create a “truth table” for analysis. A truth table lists all of the possible configurations of conditions, the number of cases that fall into that configuration, and the “consistency” of the cases. Consistency quantifies the extent to which cases that share similar conditions exhibit the same outcome; in crisp sets, the consistency value is the proportion of cases that exhibit the outcome. Fuzzy sets require a different calculation to establish consistency and are described at length in other sources [ 1 – 4 , 14 ]. Table  1 displays a hypothetical truth table for three conditions using crisp sets.

Sample of a hypothetical truth table for crisp sets

1 fully in the set, 0 fully out of the set

QCA AS AN ANALYTIC TECHNIQUE

The research steps to this point fall into QCA as an approach to understanding social and health phenomena. Analysis of the truth table is the sine qua non of QCA as an analytic technique. In this section, we provide an overview of the analysis process, but analytic techniques and emerging forms of analysis are described in multiple texts [ 3 , 4 , 14 , 17 ]. The use of computer software to conduct truth table analysis is recommended and several software options are available including Stata, fsQCA, Tosmana, and R.

A truth table analysis first involves the researcher assessing which (if any) conditions are individually necessary or sufficient for achieving the outcome, and then second, examining whether any configurations of conditions are necessary or sufficient. In instances where contradictions in outcomes from the same configuration pattern occur (i.e., one case from a configuration has the outcome; one does not), the researcher should also consider whether the model is properly specified and conditions are calibrated accurately. Thus, this stage of the analysis may reveal the need to review how conditions are defined and whether the definition should be recalibrated. Similar to qualitative and quantitative research approaches, analysis is iterative.

Additionally, the researcher examines the truth table to assess whether all logically possible configurations have empiric cases. As described above, when configurations lack cases, the problem of limited diversity occurs. Configurations without representative cases are known as logical remainders, and the researcher must consider how to deal with those. The analysis of logical remainders depends on the particular theory guiding the research and the research priorities. How a researcher manages the logical remainders has implications for the final solution, but none of the solutions based on the truth table will contradict the empirical evidence [ 14 ]. To generate the most conservative solution term, a researcher makes no assumptions about truth table rows with no cases (or very few cases in larger N studies) and excludes them from the logical minimization process. Alternately, a researcher can choose to include (or exclude) rows with no cases from analysis, which would generate a solution that is a superset of the conservative solution. Choosing inclusion criteria for logical remainders also depends on theory and what may be empirically possible. For example, in studying governments, it would be unlikely to have a case that is a democracy (“condition A”), but has a dictator (“condition B”). In that circumstance, the researcher may choose to exclude that theoretically implausible row from the logical minimization process.

Third, once all the solutions have been identified, the researcher mathematically reduces the solution [ 1 , 14 ]. For example, if the list of solutions contains two identical configurations, except that in one configuration A is absent and in the other A is present, then A can be dropped from those two solutions. Finally, the researcher computes two parameters of fit: coverage and consistency. Coverage determines the empirical relevance of a solution and quantifies the variation in causal pathways to an outcome [ 14 ]. When coverage of a causal pathway is high, the more common the solution is, and more of the outcome is accounted for by the pathway. However, maximum coverage may be less critical in implementation research because understanding all of the pathways to success may be as helpful as understanding the most common pathway. Consistency assesses whether the causal pathway produces the outcome regularly (“the degree to which the empirical data are in line with a postulated subset relation,” p. 324 [ 14 ]); a high consistency value (e.g., 1.00 or 100 %) would indicate that all cases in a causal pathway produced the outcome. A low consistency value would suggest that a particular pathway was not successful in producing the outcome on a regular basis, and thus, for translational purposes, should not be recommended for policy or practice changes. A causal pathway with high consistency and coverage values indicates a result useful for providing guidance; a high consistency with a lower coverage score also has value in showing a causal pathway that successfully produced the outcome, but did so less frequently.

For example, Kahwati et al. [ 9 ] examined their truth table and analyzed the data for single conditions and combinations of conditions that were necessary for higher or lower facility-level patient weight loss outcomes. The truth table analysis revealed two necessary conditions and four sufficient combinations of conditions. Because of significant challenges with logical remainders, they used a bottom-up approach to assess whether combinations of conditions yielded the outcome. This entailed pairing conditions to ensure parsimony and maximize coverage. With a smaller number of conditions, a researcher could hypothetically find that more cases share similar characteristics and could assess whether those cases exhibit the same outcome of interest.

At the completion of the truth table analysis, Kahwati et al. [ 9 ] used the qualitative data from site interviews to provide rich examples to illustrate the QCA solutions that were identified, which explained what the solutions meant in clinical practice for weight management. For example, having an involved champion (usually a physician), in combination with low facility accountability, was sufficient for program success (i.e., better weight loss outcomes) and was related to better facility weight loss. In reviewing the qualitative data, Kahwati et al. [ 9 ] discovered that involved champions integrate program activities into their clinical routines and discuss issues as they arise with other program staff. Because involved champions and other program staff communicated informally on a regular basis, formal accountability structures were less of a priority.

ADVANTAGES AND LIMITATIONS OF QCA

Because translational (and other health-related) researchers may be interested in which intervention features—alone or in combination—achieve distinct outcomes (e.g., achievement of program outcomes, reduction in health disparities), QCA is well suited for translational research. To assess combinations of variables in regression, a researcher relies on interaction effects, which, although useful, become difficult to interpret when three, four, or more variables are combined. Furthermore, in regression and other variable-oriented approaches, independent variables are held constant at the average across the study population to isolate the independent effect of that variable, but this masks how factors may interact with each other in ways that impact the ultimate outcomes. In translational research, context matters and QCA treats each case holistically, allowing each case to keep its own values for each condition.

Multiple case studies or studies with the organization as the unit of analysis often involve a small or intermediate number of cases. This hinders the use of standard statistical analyses; researchers are less likely to find statistical significance with small sample sizes. However, QCA draws on analyses of set relations to support small-N studies and to identify the conditions or combinations of conditions that are necessary or sufficient for an outcome of interest and may yield results when probabilistic methods cannot.

Finally, QCA is based on an asymmetric concept of causation , which means that the absence of a sufficient condition associated with an outcome does not necessarily describe the causal pathway to the absence of the outcome [ 14 ]. These characteristics can be helpful for translational researchers who are trying to study or implement complex interventions, where more than one way to implement a program might be effective and where studying both effective and ineffective implementation practices can yield useful information.

QCA has several limitations that researchers should consider before choosing it as a potential methodological approach. With small- and intermediate-N studies, QCA must be theory-driven and circumscribed by priority questions. That is, a researcher ideally should not use a “kitchen sink” approach to test every conceivable condition or combination of conditions because the number of combinations increases exponentially with the addition of another condition. With a small number of cases and too many conditions, the sample would not have enough cases to provide examples of all the possible configurations of conditions (i.e., limited diversity), or the analysis would be constrained to describing the characteristics of the cases, which would have less value than determining whether some conditions or some combination of conditions led to actual program success. However, if the number of conditions cannot be reduced, alternate QCA techniques, such as a bottom-up approach to QCA or two-step QCA, can be used [ 14 ].

Another limitation is that programs or clinical interventions involved in a cross-site analysis may have unique programs that do not seem comparable. Cases must share some degree of comparability to use QCA [ 16 ]. Researchers can manage this challenge by taking a broader view of the program(s) and comparing them on broader characteristics or concepts, such as high/low organizational capacity, established partnerships, and program planning, if these would provide meaningful conclusions. Taking this approach will require careful definition of each of these concepts within the context of a particular initiative. Definitions may also need to be revised as the data are gathered and calibration begins.

Finally, as mentioned above, crisp set calibration dichotomizes conditions of interest; this form of calibration means that in some cases, the finer grained differences and precision in a condition may be lost [ 3 ]. Crisp set calibration provides more easily interpretable and actionable results and is appropriate if researchers are primarily interested in the presence or absence of a particular program feature or organizational characteristic to understand translation or implementation.

QCA offers an additional methodological approach for researchers to conduct rigorous comparative analyses while drawing on the rich, detailed data collected as part of a case study. However, as Rihoux, Benoit, and Ragin [ 17 ] note, QCA is not a miracle method, nor a panacea for all studies that use case study methods. Furthermore, it may not always be the most suitable approach for certain types of translational and implementation research. We outlined the multiple steps needed to conduct a comprehensive QCA. QCA is a good approach for the examination of causal complexity, and equifinality could be helpful to behavioral medicine researchers who seek to translate evidence-based interventions in real-world settings. In reality, multiple program models can lead to success, and this method accommodates a more complex and varied understanding of these patterns and factors.

Implications

Practice : Identifying multiple successful intervention models (equifinality) can aid in selecting a practice model relevant to a context, and can facilitate implementation.

Policy : QCA can be used to develop actionable policy information for decision makers that accommodates contextual factors.

Research : Researchers can use QCA to understand causal complexity in translational or implementation research and to assess the relationships between policies, interventions, or procedures and successful outcomes.

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In This Article Expand or collapse the "in this article" section Qualitative Comparative Analysis (QCA)

Introduction.

• The Emergence of QCA
• Comparisons with Other Techniques
• Criticisms of QCA
• Case Selection and Combining Cross-Case and Within-Case Analysis
• Model Specification and Parameters of Fit
• Applications of QCA
• QCA Software

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Qualitative Comparative Analysis (QCA) by Axel Marx LAST REVIEWED: 13 November 2018 LAST MODIFIED: 28 November 2016 DOI: 10.1093/obo/9780199756384-0188

The social sciences use a wide range of research methods and techniques ranging from experiments to techniques which analyze observational data such as statistical techniques, qualitative text analytic techniques, ethnographies, and many others. In the 1980s a new technique emerged, named Qualitative Comparative Analysis (QCA), which aimed to provide a formalized way to systematically compare a small number (5<N<75) of case studies. John Gerring in the 2001 version of his introduction to social sciences identified QCA as one of the only genuine methodological innovations of the last few decades. In recent years, QCA has also been applied to large-N studies ( Glaesser 2015 , cited under Applications of QCA ; Ragin 2008 , cited under The Essential Features of QCA ) and the application of QCA to perform large-N analysis is in full development. This annotated bibliography aims to provide an overview of the main contributions of QCA as a research technique as well as an introduction to some specific issues as well as QCA applications. The contribution starts with sketching the emergence of QCA and situating the method in the debate between “qualitative” and “quantitative” methods. This contextualization is important to understand and appreciate that QCA in essence is a qualitative case-based research technique and not a quantitative variable-oriented technique. Next, the article discusses some key features of QCA and identifies some of the main books and handbooks on QCA as well as some of the criticism. In a third section, the overview focuses attention on the importance of cases and case selection in QCA. The fourth section introduces the way in which QCA builds explanatory models and presents the key contributions on the selection of explanatory factors, model specification, and testing. The fifth section canvasses the applications of QCA in the social sciences and identifies some interesting examples. Finally, since QCA is a formalized data-analytic technique based on algorithms, the overview ends with an overview of the main software package which can assist in the application of QCA.

Qualitative Case-Based Research in the Social Sciences

This section grounds Qualitative Comparative Analysis (QCA) in the tradition of qualitative case-based methods. As a research approach QCA mainly focuses on the systematic comparison of cases in order to find patterns of difference and similarity between cases. The initial intention of Ragin 1987 (cited under The Essential Features of QCA ) was to develop an original “synthetic strategy” as a middle way between the case-oriented (or “qualitative”) and the variable-oriented (or “quantitative”) approaches, which would “integrate the best features of the case-oriented approach with the best features of the variable-oriented approach” ( Ragin 1987 , p. 84). However, instead of grounding qualitative research on the premises of quantitative research such as King, et al. 1994 did, Ragin aimed to develop a method which is firmly rooted on a case-based qualitative approach ( Ragin and Becker 1992 ; Ragin 1997 for a systematic discussion of the differences between QCA and the approach advocated by King, et al. 1994 ). In recent years the fundamental differences between case-based and variable-oriented approaches have been further elaborated in terms of selection of units of observation or cases, approaches to explanation, causal analysis, measurement of concepts, and external validity (scope and generalization). Many researchers including Charles Ragin, Andrew Bennett ( George and Bennett 2005 ), John Gerring ( Gerring 2007 , Gerring 2012 ), David Collier ( Brady and Collier 2004 ) and James Mahoney ( Mahoney and Rueschemeyer 2003 ) have contributed significantly to identifying the key ontological, epistemological, and logical differences between the two approaches. Goertz and Mahoney 2012 brings this literature together and shows the distinct differences between quantitative and qualitative research. The authors refer to two “cultures” of conducting social-scientific research. In this distinction QCA falls firmly in the “camp” of qualitative research. The overview below identifies some key texts which discuss these differences more in depth.

Brady, H., and D. Collier, eds. 2004. Rethinking social inquiry: Diverse tools, shared standards . Lanham, MD: Rowman and Littlefield.

This edited volume goes into a detailed discussion with King, et al. 1994 and shows the distinctive strengths of different approaches with a strong emphasis on the distinctive strengths of qualitative case-based methods. Book also introduces the idea of process-tracing for within-case analysis. Reprint 2010.

George, A., and A. Bennett. 2005. Case research and theory development . Cambridge, MA: MIT.

Very extensive treatment of how case-based research focusing on longitudinal analysis and process-tracing can contribute to both theory development and theory testing. Discusses many examples from empirical political science research.

Gerring, J. 2007. Case study research: Principles and practice . Cambridge, UK: Cambridge Univ. Press.

Very good introduction into what a case study is and what analytic and descriptive purposes it serves in social science research.

Gerring, J. 2012. Social science methodology: A unified framework . Cambridge, UK: Cambridge Univ. Press.

An update of the 2001 volume which provides a concise introduction to different research approaches and techniques in the social sciences. Clearly shows the added value of different approaches and aims to overcome “the one versus the other” approaches.

Goertz, G., and J. Mahoney. 2012. A tale of two cultures: Qualitative and quantitative research in the social sciences . Princeton, NJ: Princeton Univ. Press.

Book elaborates the differences between qualitative and quantitative research. They elaborate these differences in terms of (1) approaches to explanation, (2) conceptions of causation, (3) approaches toward multivariate explanations, (4) equifinality, (5) scope and causal generalization, (6) case selection, (7) weighting observations, (8) substantively important cases, (9) lack of fit, and (10) concepts and measurement.

King, G., R. Keohane, and S. Verba. 1994. Designing social enquiry: Scientific inference in qualitative research . Princeton, NJ: Princeton Univ. Press.

A much-quoted and highly influential book on research design for the social sciences. This book aimed to discuss and assess qualitative research and argued that qualitative research should be benchmarked against standards used in quantitative research such as never select cases on the dependent variables, making sure one has always more observations than variables, maximize variation, and so on.

Mahoney, J., and D. Rueschemeyer, eds. 2003. Comparative historical analysis in the social sciences . Cambridge, UK: Cambridge Univ. Press.

This is a very impressive volume with chapters written by the best researchers in macro-sociological research and comparative politics. It shows the key strengths of comparative historical research for explaining key social phenomena such as revolutions, social provisions, and democracy. In addition it combines masterfully substantive discussions with methodological implications and challenges and in this way shows how case-based research contributes fundamentally to understanding social change.

Poteete, A., M. Janssen, and E. Ostrom. 2010. Working together: Collective action, the commons and multiple methods in practice . Princeton, NJ: Princeton Univ. Press.

The study of Common Pool Resources (CPRs) has been one of the most theoretically advanced subjects in social sciences. This excellent book introduces different research designs to analyze questions related to the governance of CPRs and situates QCA nicely in the universe of different research designs and strategies.

Ragin, C. C. 1997. Turning the tables: How case-oriented methods challenge variable-oriented methods. Comparative Social Research 16:27–42.

Engages directly with the work of King, et al. 1994 and fundamentally disagrees with its authors Ragin argues that qualitative case-based research is based on different standards and that this type of research should be assessed on the basis of these standards.

Ragin, C. C., and H. Becker. 1992. What is a case? Exploring the foundations of social inquiry . Cambridge, UK: Cambridge Univ. Press.

Brings together leading researchers to discuss the deceptively easy question “what is a case?” and shows the many different approaches toward case-study research. One red line going through the contributions is the emphasis on thinking hard about the question “what is my case a case of?” in theoretical terms.

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3 Qualitative Comparative Analysis (QCA): What It Is, What It Does, and How It Works

• Published: September 2014
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In this chapter, Qualitative Comparative Analysis (QCA) is introduced as a research design which can be a fruitful tool for the (comparative) analysis of social movements. QCA is a case-study methodology that enables researchers to compare mid-sized numbers of cases in view of sufficiency and necessity set relations. It is especially suitable for the assessment of “if… then” hypotheses. It takes into account complex causal structures, referring to equifinality, conjunctural causation, and asymmetrical causality. With the fuzzy set version, it is also possible to work with those concepts which are dichotomous in nature, but which can then be more finely grained; these kinds of concepts are very typical for the social sciences in general and for social movement research in particular. The contribution also gives some examples for applied QCAs in social movement research.

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Qualitative Comparative Analysis

An Introduction to Research Design and Application

Patrick A. Mello

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A comprehensive and accessible guide to learning and successfully applying QCA Social phenomena can rarely be attributed to single causes—instead, they typically stem from a myriad of interwoven factors that are often difficult to untangle. Drawing on set theory and the language of necessary and sufficient conditions, qualitative comparative analysis (QCA) is ideally suited to capturing this causal complexity. A case-based research method, QCA regards cases as combinations of conditions and compares the conditions of each case in a structured way to identify the necessary and sufficient conditions for an outcome. Qualitative Comparative Analysis: An Introduction to Research Design and Application is a comprehensive guide to QCA. As QCA becomes increasingly popular across the social sciences, this textbook teaches students, scholars, and self-learners the fundamentals of the method, research design, interpretation of results, and how to communicate findings. Following an ideal typical research cycle, the book’s ten chapters cover the methodological basis and analytical routine of QCA, as well as matters of research design, causation and causal complexity, QCA variants, and the method’s reception in the social sciences. A comprehensive glossary helps to clarify the meaning of frequently used terms. The book is complemented by an accessible online R manual to help new users to practice QCA’s analytical steps on sample data and then implement with their own findings. This hands-on textbook is an essential resource for students and researchers looking for a complete and up-to-date introduction to QCA.

List of Boxes, Figures, and Tables Preface Acknowledgments 1. Introduction What Is Qualitative Comparative Analysis? How To Use This Book The QCA Research Cycle A Brief History of QCA Trends in QCA Applications Book Outline Notes 2. Research Design Research Questions Uses of QCA Case Selection Condition Selection Multi-Method Research Designs A Survey of Empirical Applications Notes 3. Set Theory Crisp and Fuzzy Sets Set Operations Truth Tables Necessary and Sufficient Conditions Assessing Set Relations Notes 4. Causation and Causal Complexity Theories of Causation in the Social Sciences Causal Complexity Causal Analysis Notes 5. Calibrating Sets Measurement and Calibration Calibration Procedures Types of Data The Direct Method of Calibration Calibration: Applied Examples Common Misconceptions about Calibration Good Practices of Calibration Notes 6. Measures of Fit Set-Theoretic Consistency Set-Theoretic Coverage Proportional Reduction in Inconsistency Relevance of Necessity Notes 7. Set-Theoretic Analysis Analyzing Necessary Conditions Truth Table Construction Truth Table Analysis Solution Terms Counterfactual Analysis Notes 8. QCA Variants Multi-Value QCA Temporal QCA Two-Step QCA Fuzzy Set Ideal Type Analysis Related Methods and Approaches Notes 9. QCA and Its Critics Analytical Robustness Comparisons with Other Methods Formalization and Algorithms Causal Analysis and Solution Terms Recognizing QCA’s Strengths and Limitations Notes 10. Conclusion Good Research Practice Documenting and Communicating QCA Results QCA Resources The Way Ahead Notes Appendix: Link to Online R Manual Glossary References Index About the Author

"A well-written, lucid textbook dealing with all the essentials that one needs to do a QCA analysis. Just the right amount of technical details to get the basic ideas across but easily understandable to those interested in learning QCA or brushing up on recent developments. A user-friendly R manual accompanies the book, allowing one to quickly start doing analyses."—Gary Goertz, professor of political science, University of Notre Dame "Deftly navigating decades of methodological advancement, discussion, and debate, Mello produces a clearly written and well-reasoned guide to understanding QCA and how to conduct it effectively. Researchers from all disciplines will be well served by Qualitative Comparative Analysis: An Introduction to Research Design and Application , which teaches those new to the method how to do it well and provides a comprehensive reference for experienced QCA researchers."—Claude Rubinson, associate professor of sociology, University of Houston-Downtown "Patrick Mello offers a thorough but approachable introduction to Qualitative Comparative Analysis (QCA). He grounds his presentation of the approach in a discussion of the QCA research cycle, offering important analytic insights for novices and experts alike. Mello also includes 'behind-the-scaffolding' infoboxes where authors of published QCA studies comment on their analytic strategies."—Charles C. Ragin, Chancellor's Professor of Sociology at the University of California, Irvine "A diverse set of empirical illustrations, a clear presentation of QCA as a research approach, and a sharp but yet accessible hands-on presentation of QCA protocols from A to Z: this textbook is a great companion for anyone seriously engaging with QCA."—Benoît Rihoux, University of Louvain and COMPASSS international network "Mello’s book especially stands out from other publications on QCA in three different areas: the chapters on research design (Chapter II), QCA and its critics (Chapter IX) and the online ‘R Manual’ that accompanies the publication. These elements are especially interesting given the fact they are not discussed in many texts on QCA, thus filling an additional gap in the literature on the method."— Political Studies Review

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About the author.

Patrick A. Mello is a visiting scholar at the Willy Brandt School of Public Policy at the University of Erfurt and privatdozent at the TUM School of Governance of the Technical University of Munich. He is the author of Democratic Participation in Armed Conflict: Military Involvement in Kosovo, Afghanistan, and Iraq , winner of the 2015 Dissertation Award from the German Political Science Association. His articles have appeared in journals such as Foreign Policy Analysis , European Journal of International Security , and the British Journal of Politics and International Relations .

Hardcover 240 pp., 7 x 10 32 figures, 46 tables ISBN: 978-1-64712-144-0 Dec 2021 WORLD

Paperback 240 pp., 7 x 10 32 figures, 46 tables ISBN: 978-1-64712-145-7 Dec 2021 WORLD

Ebook 240 pp. 32 figures, 46 tables ISBN: 978-1-64712-146-4 Dec 2021 WORLD

Categories: International Affairs , Political Science , Migration , International Affairs - General , Public Administration and Public Management , General Political Science ,

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Qualitative Comparative Analysis in Mixed Methods Research and Evaluation

• Leila C. Kahwati - RTI International
• Heather L. Kane - RTI International
• Description

Qualitative Comparative Analysis in Mixed Methods Research and Evaluation provides a user-friendly introduction for using Qualitative Comparative Analysis (QCA) as part of a mixed methods approach to research and evaluation. Offering practical, in-depth, and applied guidance for this unique analytic technique that is not provided in any current mixed methods textbook, the chapters of this guide skillfully build upon one another to walk researchers through the steps of QCA in logical order. To enhance and further reinforce learning, authors Leila C. Kahwati and Heather L. Kane provide supportive learning objectives, summaries, and exercises, as well as author-created datasets for use in R via the companion site.   Qualitative Comparative Analysis in Mixed Methods Research and Evaluation is Volume 6 in SAGE’s Mixed Methods Research Series. To learn more about each text in the series, please visit sagepub.com/mmrs .

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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Supplements

“This book is written in a way that is easy to follow and should expand the range of fields in which QCA is used. Also, there are quite a few principles and practice tips articulated, especially in later chapters, which are applicable more broadly across social sciences and evaluation work. Novice researchers will find those suggestions especially helpful, even if QCA does not become a major tool in their practice.”

“The practical, how-to, nature of the text is very appealing to me as an instructor. I like the examples and appreciate the numerous figures used to illustrate processes and arguments for visual learners.”

“The text introduces an important, specific approach to research.”

“I think the key strengths of this text are its organization and breadth. From an organization perspective, the wealth of resources and focus is essential for guiding the reader/learner toward practical keywords, i.e. language, and skills necessary to implement.”

This is a very good resource for students and teaching

• Use of a concrete example that is woven across multiple chapters provides a thread of continuity that allows readers to follow the step-by-step process for understanding the method. 
• A guiding heuristic helps orient the reader at the beginning of each chapter to understand where they are in the process of conducting an analysis.
• Analytic checklists easily summarize the analytic process described in the chapter and serve as a reference.
• Practice exercises provide essential practice and reinforce key concepts.
• Helpful summaries and key points succinctly summarize main points of each chapter.

Sample Materials & Chapters

Chapter 1: Qualitative Comparative Analysis as Part of a Mixed Methods Approach

Chapter 5: Analyzing the Data -- Initial Analyses

For instructors

Select a purchasing option, related products.

This title is also available on SAGE Research Methods , the ultimate digital methods library. If your library doesn’t have access, ask your librarian to start a trial .

• 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

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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A qualitative assessment of QCA: method stretching in large-N studies and temporality

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Qualitative Comparative Analysis (QCA) is a descriptive research method that can provide causal explanations for an outcome of interest. Despite extensive quantitative assessments of the method, my objective is to contribute to the scholarly discussion with insights constructed through a qualitative lens. Researchers using the QCA approach have less ability to incorporate and nuance information on set membership as the number of cases grows. While recognizing the suggested ways to overcome such challenges, I argue that since setting criteria for membership, calibrating, and categorizing are crucial QCA aspects that require in-depth knowledge, QCA is unfit for larger-N studies. Additionally, I also discuss that while the method is able to identify various parts of a causal configuration—the ‘what’—it falls short to shed light on the ‘how’ and ‘why,’ especially when temporality matters. Researchers can complement it with other methods, such as process tracing and case studies, to fill in these missing explanatory pieces or clarify contradictions—which begs the question of why they would also choose to use QCA.

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Qualitative Comparative Analysis

Process Tracing Methods

The role of analytic direction in qualitative research

Joanna E. M. Sale

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1 Introduction

Researchers are interested in explaining the—often complex, combinational, and conjunctural—causes of specific events (Ragin 2008 ; Schneider and Wagemann 2012 ). To do so, some scholars opt for the comparative method, which is a qualitative, set-theoretic, and case-oriented approach. They work with small populations, defining the boundaries of the study using macrosocial units, then choose cases and measure similarities and differences on theoretically relevant conditions.

Qualitative Comparative Analysis (QCA) was first envisioned by Charles C. Ragin in 1987, then further developed by Ragin in 2008 and with other scholars in a 2009 edited book. It is simultaneously qualitative and quantitative, taking advantage of the strengths associated with each tradition (Ragin 2008 ). QCA expounds that combining necessary and sufficient conditions in various cases, and then comparing them, can lead researchers to determine causal explanations for an outcome (Hug 2013 : 253). The method is insensitive to how frequently the cause, or combinational causes, occurs for the outcome. This means fewer, but more in-depth, cases that demonstrate the phenomenon of interest, since one does not have to search for repetitive occurrences throughout a population. QCA allows researchers to explain complex complexity within configurations (e.g., equifinality) and to perceive the ‘nuances’ of necessary and sufficient causes (Aus 2009 ; Olsen 2014 ).

Despite the method’s original intention of analyzing only a small number of cases, later Ragin ( 2008 : 7) extended QCA’s applicability also to large-N studies. When determining multiple and complex causality, Ragin ( 2008 : 82) suggests that fuzzy sets allow researchers to reap the best from both quantitative and qualitative approaches. Whereas other scholars have focused on the quantitative shortcomings of QCA, this discussion focuses on its qualitative aspects. While I recognize the merits of QCA and its extensive use, the present article focuses on some of its limitations. I avoid responding to a ‘side’ (advocates versus opponents) but rather, in the spirit of participating in the scholarly debate about the method, the intent is to contribute insights constructed through a qualitative, more so than a quantitative, lens.

I consider the extension to large-N studies method stretching since as the number of cases of analysis grows, researchers using QCA have less ability to incorporate detailed information about each case. Larger-N studies complicate setting criteria for membership, calibrating, and categorizing, which in turn affects the conclusions—thus should not be advocated for or presented as feasible. Footnote 1 Using QCA for large-N studies makes poor use of a researcher’s in-depth case knowledge (Rutten 2020 )—one of the main benefits of qualitative research. Goertz and Mahoney ( 2012 : 1) start A Tale of Two Cultures by outlining quantitative and qualitative research as “loosely integrated traditions,” each with their “own values, beliefs, norms” and “distinctive research procedures and practices.” Inferences arise from cross-case analysis in quantitative research but from within-case analysis in qualitative studies, both which can be exploited in multimethod research (Goertz 2017 ; Goertz and Mahoney 2012 ).

While I review some of the ways to overcome this barrier while still using QCA (e.g., collaborating or including external experts), I nonetheless point to shortcomings of such workarounds, as well as possible heterogeneous conclusions and decreased rigor. Differing conclusions do not necessarily indicate they are incorrect, for example when researchers analyze different populations, periods, or contexts. I also discuss that while QCA can identify various parts of a causal configuration (the ‘what’), given that one of QCA’s main aims is to link observed patterns to existent theory, a useful part of the answer would also explain how or why the causes contribute to the effect. From this standpoint, other methods such as process tracing and case studies can better offer such explanations.

The following Sect. 1.1 contains a review of QCA’s strengths and variants, as well as some critiques of it, underlying how the present discussion can contribute to the dialogue. Section 1.2 outlines the main argument that both analytically and practically questions using QCA with large-N cases. The briefer Sect. 1.3 suggests that other methods may be more appropriate when temporality is key to answering the research question at hand, followed by the conclusion.

1.1 QCA: merits, types, and limits

As a broad introduction to its merits, QCA was originally a variation of the comparative method, is grounded in set theory, and is ideally suited for studying “explicit connections” between causes and effects (Ragin 2008 : 23). Its Boolean algebra approach allows researchers to uncover combinations of causes that produce an outcome. QCA can be used to assess necessary and sufficient conditions as well as pinpoint multiple conjunctural causation patterns (Ragin 1987 : 101). Since the first application published in a journal in 1984, QCA’s popularity has grown—especially since 2004—most often used in political science, then sociology and anthropology, as well as in economics and management, and in multidisciplinary studies (Rihoux et al. 2013 ). Its use has continued to spread, although not without errors in effectively using the method (see Schneider and Wagemann [ 2010 ] for good practices and Rubinson and colleagues [ 2019 ] for common mistakes).

The method can be used to summarize data, develop new theoretical arguments, and check existent theories (Berg-Schlosser, De Meur, Rihoux, and Ragin 2009 : 15). Classifying through codification can assist scholars in organizing a social phenomenon thus can also be beneficial for exploring and selecting cases, as well as answering descriptive research questions. Finally, the classification process can be considered transparent, as it allows researchers to control conditions (bounds) if they clearly explain concepts, attributes, and indicators.

There are three main types of QCA: crisp set, multi-value, and fuzzy set (csQCA, mvQCA, fsQCA, respectively) (see, e.g., Rihoux 2006 ; Schneider and Wagemann 2012 ; also Annex Fig. 1 ). The difference between the various forms lies in how to score (i.e., categorize) the concepts of interest. Scholars later developed another type of QCA, temporal QCA (TQCA), to attempt to overcome critiques that had been made regarding the method’s inability to include temporality into its analysis (Caren and Panofsky 2005 ; Ragin and Strand 2008 ).

In its most basic and original form, csQCA, each case is classified as 0 (absence) or 1 (presence of the binary variable of interest) (Rihoux and De Meur 2009 : 34–36). Using democracy as an example, a 0 may indicate non-democracy, whilst 1 is democracy. The second type, mvQCA, broadens the strict dichotomous nature. MvQCA extends csQCA to allow for more notation values; the threshold justifications should be based on theory or empirics (Cronqvist and Berg-Schlosser 2009 : 70, 76). In other words, the researcher defines the extent to which each case is part of a subset. Continuing with the example of democracy, thinking along the lines of Collier and Mahon ( 1993 ), participatory democracy would have a notation value of 1, liberal democracy 2, and popular democracy 3.

The third type, fsQCA, is fuzzy-set scoring in which ‘fuzziness’ conveys the idea of conceptual boundaries that are not sharply defined (Schneider and Wagemann 2012 : 27). It is fuzzy around the edges since the score ranges between 0 and 1. It seeks to determine the degree of membership to a group, with 0 as absolute non-membership and 1 as full membership. In the democracy example, a case in fsQCA with a score of 1 would be a full-fledged democracy (following the researcher’s precise definition of what that entails) whereas a case scoring 0.4 is a partial, even weak, democracy (Schneider and Wagemann 2012 : 29). As compared to, for example, csQCA, Rhioux (2006) positions fsQCA as more appropriate for larger-N studies.

In addition to these three main types, TQCA tries to incorporate temporality, specifically by capturing the temporal nature of causal interactions (Caren and Panofsky 2005 : 147). This type gives weight to the sequence—as in the temporal order—of case attributes that could be causally relevant for the outcome of interest; TQCA is bounded by theoretical restrictions to maintain a manageable number of possible configurations (Caren and Panofsky 2005 : 148).

Quantitative scholars have put forth numerous critiques regarding problematic (particularly causal) inference, measurement errors, and fuzzy-set scoring which, they argue, doubt QCA’s reliability and usefulness as a method (see, e.g., Bowers 2014 ; Braumoeller 2014 ; Collier 2014 ; Hug 2013 ; Krogslund and Michel 2014 ; Lieberson 2004 ; Lucas and Szatrowski 2014 ; Munck 2016 ; Seawright 2005 , 2014 ). For instance, some researchers state that QCA algorithms, which are its main analytical procedure, generate inconsistent results when applying the method repeatedly in simulations (Collier 2014 ; Krogslund and Michel 2014 ; Lucas and Szatrowski 2014 ). QCA advocates have responded to such critiques (e.g., Berg-Schlosser et al. 2009 ; De Meur et al. 2009 ; Ragin and Strand 2008 ; Rihoux 2006 ; Rubinson et al. 2019 ; Schneider and Wagemann 2012 ).

However, in the qualitative literature, various issues within the QCA approach are presented as ‘challenges’ to overcome; the present article differs since it focuses on exploring a few of these qualitative shortcomings and suggests that the method should not be extended to large-N studies. To make these assessments, I combine the method’s logic and cognitive science to provide a cognitive-epistemic and linguistic qualitative critique of QCA. Specifically, I build from literature based on cognitive science focused on categorization (Elkins 2014 ; Rosch 1975a , b , 1978 ; Zadeh 1965 ), linguistics (Lakoff 1975 , 2014 ), logic (Munck 2016 ; Sartori 1970 , 2014 ), and on temporality based on path dependency (Mahoney 2000 ; Pierson 2004 ). While the contributions support some of the quantitative critiques’ takeaways, my parallel findings stem from a qualitative viewpoint—all the more reason that the discussion is of interest to readers of Quality & Quantity .

1.2 QCA: unfit for large-N studies

The main point is that when using Qualitative Comparative Analysis, sizes matters. In-depth knowledge intrinsically relates to smaller-N studies. Therefore, I argue that despite existent suggestions to use QCA in large-N studies, the method is only appropriate for small- and medium-N studies. While some types of cases are more scalable than others, as the number of cases in an analysis increases, the depth of a researcher’s case knowledge is forfeited for breadth. In Rihoux and Ragin’s ( 2009 : 176) words, “as the number of cases grows, it becomes increasingly difficult to develop a sufficient knowledge of all individual cases.” They clarify that “there must be sufficient ‘case-based knowledge’ before engaging in the further technical operations of QCA” but convey that researchers’ main concern “should still be the original research question and the subsequent use of theory to guide case selection” (Rihoux and Ragin 2009 : 24). While agreeing on the importance of the research question and use of theory, I also suggest that when the cases exceed a number that eliminates the possibility to have ‘sufficient case-based knowledge,’ the researchers’ best option would be to select another method to appropriately answer their research question. While not arguing against the use of QCA completely, the proposed statement questions its use in large-N studies.

Fiss, Sharapov, and Cronqvist ( 2013 : 191) recognize the conflict and its result, “in large-N QCA, it is difficult to maintain the kind of intimate familiarity with the cases that small-N QCA is usually based on. As a result, measurement errors in coding of cases are more likely.” For them, a step forward would be to combine large-N QCA with econometric analysis, convincingly outlining why the two methods, differing in their approach to social science research, can fruitfully be used in a hybrid method incorporating elements from both (Fiss et al. 2013 : 194). I focus on the first issue they highlighted about researchers’ familiarity with their cases.

As one of the main strengths of qualitative research, a researcher’s in-depth case knowledge is diminished when using QCA (or other methods) beyond a small number of cases (Rutten 2020 ). This argument is two-tiered: as the number of cases grows, researchers have less capability to find and correctly incorporate in-depth information about each case, which lessens the ‘qualitativeness’ of a qualitative study. As Krogslund and Michel ( 2014 : 25) point out, “the method relies heavily on the close knowledge of relatively few cases for making inferences.” Less specific knowledge, or empirical “intimacy” (Ragin 1994 cited in Rihoux and Ragin 2009 : 24), results in risking inappropriate coding, which can lead to differing conclusions.

Two ways to overcome this would be to use thematic or country experts or to collaborate in a larger group of researchers (as suggested in Rihoux and Ragin [ 2009 ]). Analytically for any method, more collaborators could affect rigor and cohesiveness across cases; for QCA, relying on experts would mean accepting external assessment regarding the extent to which a case pertains to membership. Footnote 2 As Ragin repeatedly emphasizes, researchers return to the cases throughout the process of searching for causal configurations. The back-and-forth would be productive only when maintaining continued engagement with experts, to refine and recalibrate conditions and cases. Alternatively, the suggestion for collaboration is welcome, although finding researchers specialized in over 50 areas or countries would signify a large research project, rather than typical analyses. Such a strategy again seems appropriate for smaller-N studies and only for larger-N studies in specific projects.

Thereafter, this diminished in-depth case knowledge affects how the principal researchers are able to categorize each case correctly and uniformly. I of course recognize the richness of qualitative work that offers in-depth answers that contribute to a single part of the larger picture. Nonetheless, an aim is to be as scientific in methods as possible, and this means researchers should arrive at similar conclusions if they had asked similar research questions.

Ragin refers to quantitative studies as variable-oriented whereas qualitative research is case-oriented. Case-oriented implies that researchers know—meaning profoundly know—their cases. In-depth case knowledge is a defining principal benefit of qualitative studies. This critical point parallels what Gerring ( 2007 : 10) states, “large-N cross-case analysis is always quantitative, since there are (by construction) too many cases to handle in a qualitative way.” Nonetheless, it seems to be unintendedly undermined, or at least restricted, in each of QCA’s three main types (csQCA, fsQCA, and mvQCA), particularly while handling a larger number of cases (not synonymous with a large number of units of analysis).

Along with outlining perks and innovations of QCA as a research approach, Rihoux and Marx ( 2013 ) highlight parsimonious explanations as one of QCA’s key benefits, while Aus ( 2009 ) positions QCA’s strength in causal complexity over parsimony, for small-N studies. Connecting parsimony and causality, Baumgartner ( 2015 : 840) highlights that “only maximally parsimonious solution formulas can represent causal structures” and as such, criticizes QCA researchers examining causal hypotheses who accept intermediate solution formulas (since parsimony is not maximized). Footnote 3

In set-theory, one places an object (or case) into a category: e.g., Germany, India, and the United States are democracies. One can use Boolean algebra to classify each democracy using a score of one, or in the absence of democracy, a score of zero, then create a truth table listing all the cases in an ordered fashion (Ragin 1987 : 86–88). In truth tables, each row is a case (or a possible logical combination of causes and outcome) and each column is a condition of interest (similar to independent variables), including the outcome (the dependent variable).

Researchers may alternatively choose to classify objects into partial categories, scoring between 0 and 1; or alternatively, for more dynamic classification, researchers can give partial membership based on degree and fuzzy-set theory, developed by Zadeh ( 1965 ). Lakoff ( 1975 , 2014 ) extensively explains fuzzy logic, fuzzy concepts, and fuzzy-set scoring, as well as their relationships to natural language. Thereafter, Rosch ( 1975b ) developed a prototype analysis allowing scholars to group cases according to characteristics and then calculate its membership based on the distance between the case and the prototype (also see Elkins [ 2014 : 37]). This literature and line of thinking are key in understanding how to categorize using Ragin’s fuzzy-set scoring.

The first issue is deciding which cases constitute an example of the phenomenon of interest (e.g., the example of democracy) while maintaining the concept’s validity and without stretching the concept (Collier and Levitsky 1997 ; Collier and Mahon 1993 ; Goertz 2006 ; Sartori 1970 ). The second challenge is deciding to what extent each one is a democracy. There is no fixed answer for this question since it incorporates a matter of degree, or in other words, a degree of truth (Lakoff 1975 ; Rosch 1975 b). This distorts the perceived clean image of how much truth a truth table may contain.

One of QCA’s benefits is allowing the researcher to first set the boundaries and then place the object into (or, when working with fsQCA, partially into) the membership. Nevertheless, this is also a drawback of QCA since it can give false positives, or in other words, be the result of chance (for details, see Braumoeller [ 2015 ]). Since researchers can fall into confirmation bias in fuzzy sets (Krogslund, Choi, and Poertner 2015 ), they would interpret concepts differently, similar to how conceptual innovation (i.e., interpretation) has occurred with democracy.

The ‘fuzziness’ membership is scored from 0 to 1 (just as Zadeh [ 1965 ] proposed). But what fits, or does not fit, into membership of a concept? While utilizing fuzzy sets, the answer is concerning for scholars such as Sartori ( 2014 : 15). Allowing researchers to set both the bounds and the membership within them makes QCA intrinsically inductive to use and leads to problematic inferences (Hug 2013 ). Yet in Ragin’s edited volume, the authors of one chapter retort that researchers setting the thresholds is not a weakness, but rather allows for exploration to discover what occurs when the boundaries are slightly shifted; although they also state that in both csQCA and mvQCA, “… the results derived might depend on the thresholds selected, and therefore the thresholds should be selected with care” (Cronqvist and Berg-Schlosser 2009 : 76, emphasis in original).

A certain degree of induction is suitable for qualitative research. Even though social scientists must remain open to inductive findings, qualitative research cannot rely solely on such discoveries since this could be a symptom of a poorly developed or interpreted theory. Ragin ( 1987 : 42) states that his inductive approach can indeed handle multiple or conjectural causation (which, for instance, Mill’s method of agreement and indirect method of difference cannot), and as a result, QCA is a more advanced technique than Mill’s methods (Thiem 2014 ).

Moreover, fuzzy-set categorization is not a free-for-all. Ragin ( 1987 ) insists that membership must be calibrated: measuring devices are matched to known standards, based on theoretical concepts; calibration can be done directly or indirectly (see Ragin [ 2008 ], Chapter 5). Two decades later, it also included empirical justification, with warnings of transparently justifying the thresholds so that the study is understandable and replicable (Cronqvist and Berg-Schlosser 2009 : 76). The benefit of fuzzy-set scoring is to avoid “black or white” dichotomies since it allows for, “more fine-grained assessment of set membership” (Rihoux and Marx 2013 : 169). Sticking to the focus on larger-N studies, accurately choosing conditions and determining membership is a greater feat as the cases increase.

To replicate, other scholars would have to agree with the design and justifications of conditions and thresholds (reflecting previous conceptualizations and definitions), as well as interpretation, and check that the analysis had passed a benchmark test. Footnote 4 As Rubinson and colleagues ( 2019 ) underline, “successful calibration requires one to carefully reflect upon the nature of one’s measures and their meaning.” If other researchers use or replicate the study, they must also ponder the in-depth work behind, and meaning of, calibrations, making it more difficult to undertake in a large-N study.

People should be able to categorize with minimum cognitive effort (Rosch 1978 ), yet fuzzy-set scoring requires a thinking exercise to determine the degree of membership (Elkins 2014 ). The cognitive effort associated with QCA truth tables intrinsically also requires subjectivism when one contemplates how to categorize each case; researchers interpret and apply the selected theory and their case-specific knowledge. Even since the beginning, Ragin ( 1987 : 162) recognized that creating useful truth tables is the most intellectually demanding part of the method.

While qualitative case-oriented researchers dedicate time and effort to coding, the ensuing process of categorizing requires further cognitive effort since researchers need to amend (or recalibrate) categorizations as part of the process. While supporting membership categorization via necessary and sufficient criteria to define well-bounded concepts, Sartori ( 2014 ) rejects set theory as a dominant framework because it may bog researchers down in unproductive techniques (Collier 2014 : 4). Other researchers would have to agree with the series of conditions and set relations included in the study before being able to verify its results.

Considering categorization, I turn to Goertz’s ( 2006 : 6) conceptual construction, positioning social science concepts as both multidimensional and multilevel. When working on a concept’s basic level, a scholar must pay attention to three issues: the negative pole, the continuity that exists (or not) between the poles, and the content of the continuum between the two poles, or the “gray zone” (Goertz 2006 : 30). Conceptualizing in csQCA falls short: working with 0 and 1, it only allows for including the positive pole (the concept itself) and the negative pole. The negative pole is only completely addressed when scholars explicitly explain it, including defining the substantive content of 0.

FsQCA indeed allows for exploring the gray area since the scholar classifies based on degrees of membership to the positive pole. Continuing using Goertz’s ( 2006 : 41) words, fuzzy logic is fitting for continuous variables “since it is an infinite-valued logic.” Nevertheless, the location does not help to understand how the case relates to the social world beyond the truth table, unless the researcher also explains the contents of the negative pole as well as the gray zone. For instance, if the concept is democracy and case (A) has a fsQCA score of 0.8, others understand that it is quite close to being a democracy. Yet, this information alone expresses little until more is known about the meaning of 0, which is the negative pole: is it autocracy, dictatorship, authoritarianism? Using asymmetrical calibration, it should be nondemocracy, which should be accompanied by a substantive explanation of its meaning (Rubinson et al. 2019 ). From there, others must be able to grasp the meaning of the values between 0 and 1 for each concept. In Goertz’s ( 2006 : 30) lingo, this would require explicitly explaining the “substantive content of the continuum between the two poles.”

These points lay the groundwork to see that the rigorous mental exercise of scoring in QCA truth tables requires vast in-depth knowledge of the phenomenon, the cases, and the theory before a researcher can accurately justify its categorization either to membership (0 or 1) or to a degree of membership (somewhere between 0 and 1). It requires both cognitive effort and interpretation. The exercise then also results in epistemic danger and volatile, less credible conclusions. Inconsistency among studies fails to add to the social science body of knowledge, which reduces QCA’s usefulness as a method as the number of cases grows.

Given epistemic and cognitive limitations, in-depth knowledge is more unlikely as the sample size increases . Initially, Ragin recognizes that case-oriented studies work best with about 2–4 positive cases, plus the same number of negative cases, and states that QCA is more difficult to use as the number of cases increases; specifically, that the approach is “incapacitated by a large number of cases” (Ragin 1987 : 49, 69). Despite the method’s original intention, Ragin ( 2008 : 7) later extends it by saying that, “the set-theoretic methods I had developed for small-N and medium-N research could be productively extended to large-N.” This is reiterated again by Ragin and co-authors (Berg-Schlosser et al. 2009 : 17) and by QCA supporters, stating that analyzing a mid-sized number of cases does not violate any of the method’s assumptions so it can also be used for analyzing large-N data (Schneider and Wagemann 2012 : 13). Such expansions overstate the method’s capacity and should not be advocated for as feasible.

Continuing this thinking, QCA with a larger number of cases “does not sacrifice explanatory richness” yet only applies to certain clusters and contexts (Rihoux 2006 : 698). To be more specific regarding the numbers and approaches, Rihoux ( 2006 : 686–687) classifies small-N situations as those with less than 30–40 cases, which emphasize case-based knowledge, and that best fit dichotomous QCA; medium-N situations are 40–50 cases and work best with mvQCA; and finally, fuzzy sets are best used in large-N situations, typically about 50–80 cases in practice (but some QCA studies have used over 100 cases) (see Annex Fig. 1 ). Caren and Panofsky ( 2005 : 151) state that successful studies have used between 18 and 50 cases.

Greckhamer, Misangyi, and Fiss ( 2013 ) recognize that increasing the number of cases in QCA changes the assumptions, objectives, and approach, suggesting that “two QCAs” exist: one for small-N and one for large-N (which they consider over 50 cases). Regarding the richness of information that the researcher has on each case, even Rihoux’s ( 2006 : 686) consideration of small (30–40) demonstrates a large discrepancy from Ragin’s original 4–8 cases. This shows how quickly the ‘in-depth’ case knowledge would be degraded and how the cognitive burden of the classification drastically increases when the set is quadrupled or multiplied even further. This is along the lines of what I call method stretching , or applying the method beyond its useful capacity. One aim of this analysis is to urge advocates and scholars not to fall into method stretching with QCA.

With more generalized and less contextual knowledge, I argue that one cannot correctly—or at least with low risk of making incorrect claims—interpret concepts and theories to properly set membership thresholds, nor appropriately place cases in a fuzzy set. I have already addressed issues surrounding collaborative efforts, which affect many research projects, not just those applying QCA.

To reiterate, unjust or non-uniform categorization can create fragile categorical definitions, even those rooted in theory. The process is prone to subjectivity since scholars could justify a variety of categories, as long as they could provide a plausible interpretation of theory. Returning to Fiss and colleagues ( 2013 : 191), “contradictory observations in large-N QCA might then at times be accepted as measurement error, whereas in small-N QCA, they will frequently trigger a re-examination of the cases selected and whether all relevant causal conditions have been included.” I interpret that producing heterogeneous conclusions undermine part of the method’s usefulness of building knowledge and consensus. Rather than considering such contradictions as measurement error, a researcher would want to further examine cases more in-depth.

When various contradictory cases appear, Greckhamer and colleagues ( 2013 ) suggest an in-depth analysis of a randomly selected sample of contradictory cases. But the main point stands that if researchers knew the phenomenon and cases, then it begs the question of why they would opt for QCA at the start rather than, for example, select one or more case studies. As Gerring ( 2007 : 12, 89–90) points out, researchers engage in a cross-case approach to select case studies then choose an appropriate type, based on the research question and if they aim to generate or test hypotheses. Footnote 5 Such methods link case characteristics and outcomes to theory, to question or refine existent theory.

1.3 When temporality matters

Despite suggestions of incorporating temporality into QCA, when temporality is key to understanding the question at hand, I suggest that other methods may be more appropriate. This is not the first time researchers have pointed out this weakness; it has been recognized in Ragin’s 2009 edited volume (De Meur et al. 2009 : 161–163) and some scholars have tried to overcome it. At least two solutions have been put forth: first, combining QCA with other techniques involving temporality (Boswell and Brown 1999 ; Griffin 1992 ), such as time series (Hino 2009 ), or second, directly including temporality in QCA, as Caren and Panofsky ( 2005 ) proposed.

Countering the first suggestion, I would ask if researchers are using another method, why would they additionally use QCA? In a chapter co-authored by Ragin, the authors state that, “QCA can lay the groundwork and be extended to even more demanding types of analyses—for example, taking into account the temporal dimension and the various ‘paths,’ ‘critical junctures,’ and overall dynamics…” (Berg-Schlosser et al. 2009 : 7). Here temporality is linked to the idea of path dependency, which is a dynamic process involving positive feedback, generating multiple possible outcomes depending on the sequence in which events unfold (Pierson 2004 : 20). This is not to say that path dependence is the only type of temporality, nor that all processes are path dependent or affected by positive (or negative) feedback. Temporality means it is not just what happens but when it happens and for how long it happens. What happens first (and why) greatly matters for what happens next since previous happenings affect the following occurrences. A researcher with in-depth case knowledge understands these details within and between the selected cases. QCA fails to account for temporality, so researchers using the approach are missing an important part of the story. If using QCA to lay the groundwork, researchers could simply consider path dependence and a method such as process tracing (see e.g., Bennett and Checkel 2014 ; Mahoney 2012 ). As Goertz ( 2017 : 49) explains, “the central purpose of process tracing is to find, verify, or disconfirm hypotheses about causal mechanisms.”

The second suggestion of how to include temporality into QCA comes from Caren and Panofsky ( 2005 ), calling it temporal QCA (TQCA). This addition deals with a type of temporality based on trajectory—meaning timing in a sequential order. Basing temporality on trajectory differs from path dependence. Path dependence involves positive feedback that generates multiple possible outcomes depending on the order in which events unfold (Pierson 2004 : 20). Although path dependence more realistically reflects the social world, TQCA is limited to only simple cases due to restricting the number of possible configurations (Caren and Panofsky 2005 : 163). Analyzing social phenomena involve more than the allowable sequences possible with TQCA. These restrictions mean that temporality has not been properly incorporated.

With added clarifications, Ragin and Strand ( 2008 : 440) recommend TQCA while using “simple temporal sequences.” In TQCA, temporal order is included to capture the conditions that are “potentially causally relevant,” can be used with crisp and fuzzy data sets, is grounded in set theory, uses truth tables, and makes necessity and sufficiency statements (Schneider and Wagemann 2012 : 16). Yet, due to QCA’s analytical use of truth tables, TQCA must be limited to combinations of only two temporal factors at a time to be considered one sequence and can empirically accommodate up to only four sequences (Ragin and Strand 2008 : 439; Schneider and Wagemann 2012 : 270). Two temporal factors are, for instance, having authoritarianism then democracy (in that order, which creates one sequence). But a researcher studying current democracies knows that having had a democracy, authoritarianism, then re-democratization will look different from a case that had nondemocracy, authoritarianism, then democracy. Hence allowing the researcher combinations of only two temporal factors are insufficient for adding temporality into QCA. Using a sequence of two could miss the chance of recognizing causal explanations since sequences of causally connected events occur under certain conditions and in a repeated manner (see Mayntz [ 2004 ]).

In limiting temporal factors to two in each sequence, TQCA further is limited to only four sequences. This seems overly restrictive since it forces the researcher to choose the most critical ones, while overlooking the others. Precisely to maintain a small number of variables as Ragin intended, Caren and Panofsky ( 2005 : 163) incorporated temporality based on trajectory, not path dependence. The difference is that trajectory is a path, but path dependence contains a particular event—for instance, a critical juncture—that can ‘derail’ the sequence into an alternative one (Caren and Panofsky 2005 : 163; Gerring 2007 ; Mahoney 2000 ). Since “TQCA is only suitable for very simple instances of path dependency” (Caren and Panofsky 2005 : 163), it falls short of capturing Pierson’s (2004) path dependence with self-reinforcing, positive feedback loops and the Polya urn process. Thus, when temporality matters, researchers should look to other methods.

To better understand the importance of temporality in QCA, I turn to a discussion on logic since it comprises a critical part of context-dependent categorization, directly affecting researchers’ causal conclusions. Set-theoretic comparative methods “reduce causation to a logical relation and erroneously posit that causal hypotheses can be formalized as a relation of material implication” (Munck 2016 : 775). One cannot simply infer causal conclusions from associational relations (Paine 2016 : 706). Logical relations are not synonymous to causal relations since the latter requires a change in X resulting in a change in Y, so causation should not be reduced to logical relations (Munck 2016 : 777). Recoding a case in QCA can then produce different ‘causal’ conclusions (Goldthorpe 1997 ).

The logical semantics behind the degree of membership are also important. Lakoff ( 1975 ) uses terms such as technically, strictly speaking, loosely speaking, and regular to scale an item to a group; note that these are non-linear . This is unlike fuzzy logic and fuzzy set-scoring, which place the item on a line from 0 to 1, where it is possible to measure the numerical points between those values. The differences—or the distance—between Lakoff’s terms are unmeasurable: “strictly speaking” may be closer to “technically” than to “loosely speaking,” or vice versa. Thresholds are also used, but categorization occurs through more natural language: “strictly speaking” means that each criterion is above certain thresholds to be included in the membership and the boundaries are context dependent (Lakoff 2014 : 10). To connect these ideas, context, history, and past occurrences in that specific context define the thresholds. To effectively categorize in QCA, researchers make measurement decisions based on their in-depth case knowledge. Where a case is placed depends on temporality within cases.

Alongside the temporality discussion, despite its ability to explore multicausality, QCA also falls short when trying to explain outcomes. Explanation includes the how and why those conditions in a certain context contribute to the outcome (Falleti and Lynch 2009 ). A causal mechanism consists of recurrent processes that connect initial conditions (causes) and that are composed of causally linked events that form sequences (Mayntz 2004 : 239–243); moreover, context is not necessarily part of the causal relationship (Denk and Lehtinen 2014 ). Mechanism-based explanations have begun to draw more attention from scholars throughout the social sciences (Hedström and Ylikoski 2010 : 49). Of course, not all research questions are causal; but when they are, causal mechanisms explain how various elements connect different causes to produce the outcome of interest (Y). In these situations, multimethod research (taking cross-case and within-case causal inference as complementary) “means a commitment to the causal mechanism approach to social and political research, which itself means a commitment to case studies as the methodology for exploring causal mechanisms” (Goertz 2017 : 5, 29).

QCA can identify the different conditions (X n ) that compose a causal configuration, which determines the occurrence of an outcome (Y), but it does not account for how nor why each condition impacts the outcome of interest. So, QCA on its own is unable to understand the exact effect X has on Y, further complicated by not being able to prioritize the sequence or duration of events (links within the causal chain) has on an outcome of interest. Following Goertz ( 2017 ), multimethod research inherently combines methodologies and as such, researchers can combine cross-case causal inference methods (QCA being one choice) with case studies to pinpoint and analyze causal mechanisms.

2 Conclusions

In the spirit of advancing research and supporting qualitative methods, I have argued against advocating for QCA in large-N studies because more cases reduce the researchers’ own in-depth knowledge, a key benefit of qualitative, or case -oriented, research. In short, researchers should avoid method stretching by not using QCA in large-N studies. During analysis, researchers’ bound setting and membership scoring directly affect the casual conclusions reached. Setting bounds in QCA creates a risk: in practice, if different researchers possess the same information and research question, they will produce different bounds—even using the same theories—resulting in varying memberships and then distinct causal explanations. Fuzzy-set membership can be estimated improperly, or differently: for instance, membership can be set at the mean rather than the minimum, the true threshold of the category may be off, which results in incorrect conclusions (Braumoeller 2014 : 46). The process of fuzzy-set scoring can result in measurement error, which must not be ignored, bringing the researcher to misleading inferences (Hug 2013 : 252). Using csQCA or mvQCA still requires determining bounds and inductive results. The scorings do not allow for temporality within and between cases. QCA (even TQCA) fails to adequately include temporality, weakening the ‘qualitativeness’ of QCA and it overlooks pieces of the case’s specific context. Social scientists’ conclusions must aim to build from existing information and generate new scientific knowledge. Varying and incomplete answers to a research question fail to reach this objective.

In addition to focusing on its limits from a qualitative perspective, I have also outlined many merits and uses of QCA. Scholars can use QCA productively for exploratory purposes for combinational causes of outcomes of interest (Berg-Schlosser et al. 2009 : 17), descriptive analyses, or to test deterministic hypotheses (Hug 2013 : 252). It can also provide valuable assistance when synthesizing results, particularly when a researcher is attempting to identify the causal configuration that determines the outcome of interest. Despite advocates’ recommendations to pair QCA with another method, QCA applications from 1984 to 2011, Rihoux and colleagues ( 2013 : 181) found 61.3% used QCA as a standalone tool. Since then, advances have been made in multimethod research, especially combining QCA with other methods for specific purposes. For example, in contextual analyses, Denk and Lehtinen ( 2014 ) outline how to combine csQCA and fsQCA with comparative multilevel analysis. Fiss and colleagues ( 2013 ) suggest steps toward creating hybrid methods combining aspects from the QCA approach and econometric methods.

As also discussed, QCA is able to find the ‘what’ (i.e., what combination of conditions generates the outcome of interest) and explore complex causality, but is unable to explain how and why those causes, within that particular combination, in that order, under certain circumstances, determine the occurrence of Y. Fear not: alternatives exist, for instance, MIMIC Modeling (Multiple-Indicators Multiple Causes), similarity-based measures, latent-class analysis (Elkins 2014 ), family resemblance (Rosch and Mervis 1975 ; Wittgenstein 1953 ), or vertical and horizontal dimensional categorization and classical versus radial subtyping (Collier and Levitsky 1997 ; Collier and Mahon 1993 ; Rosch 1978 ). For parsimony in causal hypotheses, Baumgartner ( 2015 ) suggests Coincidence Analysis. To find or explore causal mechanisms, another option is process tracing (e.g., Bennett 2008 ; Bennett and Checkel 2014 ; Mahoney 2012 ), which successfully navigates cases’ context specificity and can address historical sequences or outcomes, as well as handle complex and conjunctural explanations of outcomes. To find evidence of causal mechanisms, Goertz ( 2017 , Chapter 7) reviews how large-N qualitative testing has emerged—used for up to around 50 cases. Over this amount, as underlined in the present discussion, QCA is unfit for larger-N studies since setting criteria for membership, calibrating, and categorizing are crucial aspects requiring in-depth case knowledge.

Availability of data and material

Not applicable.

Code availability

Ragin ( 2008 : 208) has specifically addressed calibration for fuzzy sets, but he frames it as a strength: “Miscalibrations distort the results of set-theoretic assessments. The main principles guiding calibration are that (1) the target set must be carefully defined and labeled and (2) the fuzzy set scores must reflect external standards based on both substantive knowledge and the existing research literature. While some might consider the influence of calibration decisions ‘undue’ and portray this aspect of fuzzy-set analysis as a liability, in fact it is a strength. Because calibration is important, researchers must pay careful attention to the definition and construction of their fuzzy sets, and they are forced to concede that substantive knowledge is, in essence, a prerequisite for analysis.” Given this excerpt, I consider that “substantive knowledge” as a “prerequisite” strongly indicates that QCA should not be expanded to large-N studies.

Analytical difficulties arise for much collaborative research and some projects overcome them—for example, by creating extensive databases across world regions. Such projects bring additional benefits to scholarly networks and individuals, such as collaborating with researchers in the Global South.

However, Baumgartner ( 2015 ) warns that if a QCA researcher must accept flawed simplifying assumptions to maximize parsimony, a better solution would be to use Coincidence Analysis as the optimization algorithm (instead of the Quine-McCluskey optimization used in csQCA and fsQCA).

Marx and Dusa ( 2011 ) developed benchmark tables to “assess the chances of accepting a model for further analysis” which determines if researchers using a QCA approach would be able to “distinguish an analysis on the basis of random data versus real data” (as reviewed in Rihoux et al. [ 2013 : 180]).

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Acknowledgements

Thank you to Fernando Rosenblatt for the initial motivation and first review of this paper, to Gary Goertz and Luicy Pedroza for commenting on very early drafts, which greatly improved and balanced the arguments, and to Seba Koch for many fuzzy-set conversations. Many thanks also to the numerous anonymous reviewers for their patience and constructive rounds of comments.

Financial support from COES (Centro de Estudios de Conflicto y Cohesión Social), CONICYT/FONDAP/15130009.

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See Fig. 1 .

Choosing a Method: Rihoux’s description between in-depth case knowledge and number of cases. Source Rihoux ( 2006 ), Fig. 1 Best Use of QCA, MVQCA and Fuzzy Sets. Here small-N studies are considered less than about 30–40 cases, medium-N are 40–50 cases, and large-N in practice have been between 50 and 80 but have included over 100 cases (Rihoux 2006 : 686–687, 698). Rihoux’s “richness of information” is what I refer to as in-depth case knowledge.

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Finn, V. A qualitative assessment of QCA: method stretching in large-N studies and temporality. Qual Quant 56 , 3815–3830 (2022). https://doi.org/10.1007/s11135-021-01278-5

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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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• Open access
• Published: 01 April 2024

Paramedic attitudes and experiences working as a community paramedic: a qualitative survey

• Aarani Paramalingam 1 ,
• Andrea Ziesmann 1 ,
• Melissa Pirrie 1 ,
• Francine Marzanek 1 ,
• Ricardo Angeles 1 &
• Gina Agarwal 1 , 2  

BMC Emergency Medicine volume  24 , Article number:  50 ( 2024 ) Cite this article

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Community paramedicine (CP) is an extension of the traditional paramedic role, where paramedics provide non-acute care to patients in non-emergent conditions. Due to its success in reducing burden on hospital systems and improving patient outcomes, this type of paramedic role is being increasingly implemented within communities and health systems across Ontario. Previous literature has focused on the patient experience with CP programs, but there is lack of research on the paramedic perspective in this role. This paper aims to understand the perspectives and experiences, both positive and negative, of paramedics working in a CP program towards the community paramedic role.

An online survey was distributed through multiple communication channels (e.g. professional organizations, paramedic services, social media) and convenience sampling was used. Five open-ended questions asked paramedics about their perceptions and experiences with the CP role; the survey also collected demographic data. While the full survey was open to all paramedics, only those who had experience in a CP role were included in the current study. The data was qualitatively analyzed using a comparative thematic analysis.

Data was collected from 79 respondents who had worked in a CP program. Three overarching themes, with multiple sub-themes, were identified. The first theme was that CP programs fill important gaps in the healthcare system. The second was that they provide paramedics with an opportunity for lateral career movement in a role where they can have deeper patient connections. The third was that CP has created a paradigm shift within paramedicine, extending the traditional scope of the practice. While paramedics largely reported positive experiences, there were some negative perceptions regarding the slower pace of work and the “soft skills” required in the role that vary from the traditional paramedic identity.

Conclusions

CP programs utilize paramedic skills to fill a gap in the healthcare system, can improve paramedic mental health, and also provide a new pathway for paramedic careers. As a new role, there are some challenges that CP program planners should take into consideration, such as additional training needs and the varying perceptions of CP.

Peer Review reports

Community paramedicine (CP) is an emerging professional role where paramedics use their training and skills in emergency response to respond to individuals with non-acute needs who do not require transport to hospital [ 1 ]. In Ontario, Canada, CP programs have begun to garner attention as an innovative approach to support independent living in an aging older adult population with complex health conditions [ 2 ]. Although there were some very early adopters of CP programs in Ontario, these programs began to gain momentum in 2013 [ 3 ]. By 2014, 13 Paramedic Services in Ontario reported having CP programs [ 2 ]. Community paramedicine programs can be diverse in scope, and can include paramedics completing home visits to frequent 911 callers, supporting clients with healthcare navigation, providing community-based education, and conducting drop-in clinic style wellness programs [ 1 ]. The structure, mandate, and resources required for CP programs tend to vary by paramedic service and local contexts. Staffing and training arrangements can also vary, with some programs designating full-time ‘community paramedics’ while others deploy paramedics on modified duties to staff programs.

Our literature review found that few studies have sought to understand how paramedics experience and view these programs. Evaluations of CP tend to focus on patient experiences, such as their health outcomes and health service utilization [ 4 , 5 , 6 ]. While participants have generally expressed support for and acceptance of CP [ 5 , 6 ], it is unclear exactly how paramedics perceive CP programs, particularly as it relates to their understanding of paramedic professional identity and their mental health.

As the CP role becomes a more permanent part of paramedic practice, it is expected to redefine and broaden the paramedic identity beyond its traditional boundaries. Historically, service users and healthcare providers have defined paramedics as thrill seekers who provide transport, emergency response, and trauma care [ 7 ]. However, as the delivery of healthcare has become more complex and integrated, paramedic identity has also shifted. Paramedics in Canada have already adopted broad professional identities such as ‘clinician,’ ‘educator,’ ‘team member,’ and ‘patient advocate’ [ 8 ]. This expansion of the paramedic identity is expected to accelerate as CP programs are increasingly adopted in Ontario. CP programs require paramedics to work with individuals on a repeat basis, provide chronic disease management services, and use ‘soft’ skills such as motivational interviewing and advocacy. How paramedics feel about these changes to their professional identity as a result of CP has yet to be understood.

Additionally, participation in the CP role may alter paramedics’ mental health experience. Paramedics in traditional emergency response roles tend to experience Occupational Stress Injury (OSI) due to demanding work environments and exposure to traumatic incidents [ 9 , 10 ]. Occupational Stress Injury refers to any form of psychological stress resulting from the duties one performs on the job [ 9 ]. While OSI is common for all public safety personnel, some studies suggest a higher incidence of post traumatic stress disorder for paramedics when compared to police officers and firefighters [ 11 , 12 ]. Paramedics are estimated to be at higher risk of screening positive for a DSM-IV mental disorder than municipal or provincial police services, firefighters, and dispatchers [ 12 ]. While some preliminary research in one CP program suggests that paramedics who practice CP experience reduced stress and a greater quality of work life [ 9 ], it is unclear how working in CP programs in different capacities may alter paramedics’ exposure to OSI and affect one’s overall mental health.

This paper seeks to describe the positive and negative experiences of paramedics working in a CP program and assess CP’s impacts on paramedic professional identity and paramedic’s mental health experience. As paramedic experiences may not be aligned with the experiences of CP program participants or even paramedic leadership, this paper also seeks to identify workplace elements (e.g., training, supports, paramedic leadership and culture) that may promote or hinder the expansion of CP programs in Ontario.

A survey tool was developed and distributed by the McMaster Community Paramedicine Research Team in 2016, using the online platform FluidSurveys, to assess paramedics’ perceptions and experiences working in a CP role. The survey was developed based on recurring themes and insights from a focus group and three key informant interviews with paramedics. The survey drafts were also reviewed and approved by a paramedic and a paramedic superintendent with research experience. The survey tool used open-ended questions to have paramedics describe their perception of the CP role prior to, and after working in a CP program, including both positive and negative aspects.

Population and recruitment

Paramedics were invited to participate in a survey that was distributed through social media by the Ontario Paramedic Association and the CP@clinic program. On Twitter, the invitation to complete the survey was re-tweeted by multiple accounts including paramedic services, paramedic staff, and other accounts. In addition, some Paramedic Services in Ontario delivering CP programs emailed the survey link to their paramedic staff. All paramedics (with and without CP experience) were invited to complete the full survey, but only those who indicated that they had worked in a CP role were included in this study (screening question in the survey). Respondents were informed about the purpose of the research study and informed consent was obtained. This study was approved by the Hamilton Integrated Research Ethics Board (Project #13-466).

Data collection

A convenience sample was collected using an online survey. The survey was available for 16 weeks from October 2016 to January 2017, to provide ample time to gather responses from all potential participants. Data from the open-ended questions were collated into a single transcript.

The survey collected the following demographic information: age, sex, years of service, type of paramedic training (i.e., primary care, advanced care, critical care), whether the paramedic was on modified duty while working in a CP program (i.e., awaiting return to regular duties), length of time working in CP programs, and types of programs they worked in. Fivetypes of CP programs were provided as options: home visit program, clinic style program, paramedic navigator style program, triage program, and other.

The following open-ended questions were asked to elicit responses about paramedics’ experience of the CP role:

What was your opinion of community paramedicine before working a community paramedicine role?

Please explain how your opinion of community paramedicine has changed since working in a community paramedic role?

What was positive about your experience working in a community paramedic role? What did you enjoy about this role?

What were the negative aspects in your experience working as a community paramedic?

Would you like to change anything about the community paramedic role?

A comparative thematic analysis was used to describe the experiences of community paramedics before and after working in a CP role. Two members of the research team (AP, AZ) independently coded responses and identified emergent themes. Using a phenomenological approach during secondary coding, coders grounded the emergent themes within paramedics’ lived experience of the community paramedicine role, finding explanations for their experience within the context of the data itself. Responses with thick narrative descriptions were retained for analysis. Incomplete or partial responses were included in the qualitative analysis. Themes were then synthesized, refined, and were validated and triangulated by research team members (GA, AZ, MP, FM, RA). The demographic data was analyzed using descriptive analysis.

Demographics

Of the total survey respondents ( n =434), 79 reported working in a CP role. These respondents were predominantly male (57.0%), had 10 or more years of experience in a paramedic role (77.2%), and were not on modified duty while working in a CP role (86.1%). Respondents reported experience with working in multiple types of CP programs, with the most common type being clinic style programs (68.4%) (see Table 1 ). While the survey was open to all paramedics, the majority of respondents report working in Ontario ( n =61, 77.2%) and 16 respondents (20.3%) did not provide the province in which they worked.

A number of themes and sub-themes emerged from the analysis. Before having worked in a CP program, paramedics broadly identified three unique opportunities and impacts of the CP role: 1) filling gaps in emergency response and the healthcare system at large, 2) providing opportunity for lateral career movement, and 3) creating practice paradigm shifts. After working in a CP role, respondents were able to describe in detail the positive and negative aspects of these three opportunities and impacts. These themes are conceptualized in Fig.  1 .

Diagram depicting the major themes and the positive and negative experiences of paramedics working in a CP role

Theme 1: CP programs can fill important gaps in emergency response and the healthcare system at large, but come with new professional challenges

Before working in a CP role, the majority of respondents viewed the CP role positively. CP was thought to fill important gaps in emergency response and the health system at large. It offered paramedics an opportunity to practice continuity of care by providing prevention and disease management support to older adults who were often inappropriately accessing emergency care services. Paramedics felt that the needs of these individuals were not being fulfilled through traditional emergency response.

There are several individuals I have come across in my career who would have benefitted from a regularly scheduled home visit. ...There are a lot of individuals who require that [health] maintenance… it greatly reduces the workload of Emergency Services and frees them up for what they are actually required for – emergencies. (P.24)

[I thought] it was a vital service that filled gaps in the health care sector that was having excellent results where implemented (P.43)

After working in a CP program, respondents expanded on these initial sentiments. They described delivering a different level of care to their communities that involved stepping into a novel helping role, building relationships with participants and their families, supporting participant health outcomes, and taking part in interprofessional collaboration. This new level of care also came with new professional challenges such as increased emotional burden, managing participant expectations, and conflicts with other health and social service providers.

Sub-theme 1A: being in a helping role

Helping program participants in a CP role was described as novel and different when compared to the emergency response role. Community paramedics worked with participants on a long-term basis and witnessed their health and quality of life improvements. Paramedics enjoyed helping participants who were part of vulnerable or underserved communities. By taking time to listen to these participants and hear their stories, paramedics were able to exercise more compassion and felt less judgemental about participants’ situations. This was a rewarding aspect of the CP role, even having a powerful positive effect on paramedics’ own mental health.

Making a difference in people's lives ... often the people in the community who are ignored and shunned by others. I enjoyed going out in the community, solving problems, working with other services, having the time to LISTEN to patients rather than be worried about my scene time...this is one of the most important things for Paramedic mental health as well. (P.46)

...the knowledge that community paramedics, with sometimes very simple interventions/strategies can make all the difference in people's lives, preventing people from falling through the cracks, or helping them out of that situation…(P.61)

Sub-theme 1B: relationship building with program participants

Paramedics enjoyed building relationships with participants and getting to know them on a personal level, which was not possible in an emergency response role due to limited time on scene during acute calls. Building rapport with participants in the comfort of their homes created a sense of trust that fostered into natural friendships, with some paramedics describing themselves as building a ‘family’ with participants. Others noted that this trust allowed participants to share more details about their health and medical history, allowing paramedics to better assist in their care. Paramedics felt it was important to build these strong social relationships with participants in order to encourage and affect health behaviour changes for participants. Strong relationships with participants allow paramedics to thoroughly follow-up after initial visits and engage in conversations about participants’ short- and long-term health goals. Additionally, although the CP role lacked the adrenaline rush, this increased socialization was described as filling this gap.

The paramedics have built a rapport with [participants] and have really built a family with them.(P.19)

Getting to know [participants] beyond the 30 minutes to an hour we’re used to being with [them in an emergency capacity]. I found as they got to know me, they were more willing to share health concerns they were having and trusted me more. (P.26)

I realized that community paramedicine can be more enjoyable than I thought…where it lacks in adrenaline it makes up for in a social aspect. (P.10)

Seeing how much they trust us and tell us some of their most intimate issues. (P.49)

Sub-theme 1C: emotional burden

While paramedics enjoyed the rapport and relationships built with participants, they also felt they were making greater emotional investments in participants who were in poor health, may have been in a palliative state or dealing with addictions issues. Burnout, attachment fatigue, and difficulty dealing with participant deaths were common experiences. For some paramedics, having built rapport with certain participants meant that they were the primary contact for follow-up care even on their days off, leading to poor work-life balance. Similar to other clinical practitioners who work one-on-one with individuals over a long period of time (e.g., physicians, social workers), one respondent emphasized the need for paramedics in a CP role to be trained to reflect on their experience and make adjustments to how they work with participants.

Can be emotionally draining working over the long term with [participants]... who are very sick, some are palliative, difficult personalities, addictions, etc. Paramedics historically aren’t used to becoming emotionally involved with [people] … but this is difficult not to do when you are seeing people over and over again, and getting involved with their families and other circles of care as well. (P.5)

Couldn't just leave work behind at work like a traditional paramedic could - had to field phone calls on my vacation to help make arrangements for a [participant]... because no other community paramedics were available or as familiar with [them]. (P. 9)

Paramedics are not usually trained, educated, or encouraged to engage in self-reflective or reflective practice and it’s essential for a role like community paramedicine. (P. 34)

Sub-theme 1D: participant outcomes

Paramedics reported a better understanding of the impact of CP programing on participants’ health and well-being. Identifying ‘silent’ health issues before they resulted in emergency transport, making appropriate referrals and reducing 911 calls were some of the positive outcomes. For some, their CP training had become an integral part of their role as a paramedic overall, providing valuable transferable skills that could also be used during an emergency response to further improve health outcomes and close gaps in care. Additionally, beyond identifying health issues and making appropriate referrals, some paramedics felt that CP programs help build a sense of community, which may in turn also improve participant health and well-being. Paramedics particularly appreciated being able to witness these positive outcomes first-hand.

I have realized that community paramedicine has a very broad impact in the community. It is very underappreciated ... It has improved the livelihood of many [participants], and can (with the aid of other resources), assist them [with] their healthcare needs. (P.9)

Seeing them get proper treatment for an illness they did not know they had (i.e. hypertension, diabetes). (P.62)

Seeing the direct benefit of timely and appropriate interventions; having a big impact on people's quality of life, even when palliative (P. 60)

I see that most [people] don't want to go to the hospital and really don't need to. The issue is [that in] our current system people expect to be taken as they think that's the only way a doctor will see them. When they realised someone could see them at home and then refer them to the required service less 911 calls were made. (P.10)

I'm fortunate enough to work in a service that has integrated some aspects of community paramedicine into every response. Being trained to recognize signs in a [participant]'s home that indicate a higher need for home care and offering ways for them to access more care is deeply satisfying. The relief on a person's face when told they could get some home care, or help with day to day chores makes me feel like I made a difference to their quality of life. (P.36)

Seeing how much change we were able to create in a short period of time. Watching the sense of community flourish in the buildings while we were there. (P.49)

Sub-theme 1E: managing participant expectations

Managing the expectations of program participants and trying to elicit health behaviour change was a challenging aspect of the CP role. While seeing positive improvements in participants' lives motivated community paramedics and likely provided them with increased job satisfaction, working with participants who were not able to achieve these positive outcomes in some participants despite working to identify their health issues, and referring and connecting them to services, was a frustrating aspect of the role. Paramedics experienced frustration when participants did not follow their health advice, did not experience improvements in their health, or when participants expressed dissatisfaction with the help they received. Some of this frustration was also directed towards referral agencies who were not able to help the participant.

Some people are noncompliant with their medications or taking the advice of their physicians. It can be frustrating having people come to you for help for the same problems but not be receptive to the advice that you give. (P. 42)

There have been moments of frustration when patients don't follow through or even attempt to follow advice given to them by myself or the agency that has been tasked with giving them assistance. (P. 42)

[Some] clients who are out of the normal scope of practice for a paramedic who are better served by other agencies but those agencies failing the client. Even when you help put services in place for a client they are not happy and want more. (P.7)

Sub-theme 1F: interprofessional collaboration

Paramedics enjoyed working with differenthealthcare providers in their community. Collaboration with different services and providers was felt by paramedics to benefit program participants and improve their career satisfaction. Collaboration with different healthcare providers outside of an emergency paramedicine context made paramedics feel respected and part of a valued healthcare team that was centred around improving participant health. This collaboration provided better coordinated care and also showcased paramedics’ clinical skills beyond that of transport and ambulance-driving to other healthcare professions.

The integration, collaboration, and cooperation with health care and with allied health care providers. We truly make a difference in people's lives, keeping them in their homes longer, safer, and healthier. (P. 67)

Building relationships and pathways with community health care providers and showing them that paramedics are more than just ambulance drivers. (P. 13)

Interacting with the [primary care provider] as we caught early onset [urinary tract infections (UTIs)] and [upper respiratory tract infections] with treatment started based solely on our assessment and conversation via cell phone with [the provider] saving [the participant] stress and cost of travelling to their office. (P. 49)

...Enjoy working more closely with physicians to develop treatment plans.(P.56)

Sub-theme 1G: conflicts with other service providers

While paramedics appreciated the interprofessional collaboration offered by the CP role, they also described conflicts and challenges working with other service providers in the health and social work sector. Paramedics described some service providers as failing and unable to meet participant needs. Overlap between CP activities and other healthcare roles also led to tensions regarding professional boundaries, including physician concerns about CPs diagnosing their patients.

Some doctors did not like paramedics assessing and diagnosing issues (e.g. chest infections, UTIs, and muscular-skeletal injuries). (P. 39)

Don't know if referrals are getting back to [participants]…[There are] already programs in place that have [the] same mandate as CP, like Health Link, forcing medics to do home visits when [participants] don’t need them any more. (P. 12)

Oftentimes, navigating the system was a challenge and often wait times with family doctors or other services were unavoidable. (P. 29)

Theme 2: CP offers paramedics an opportunity for lateral career movement that is free from the demands of shift work and allows them to be connected to the community in a clinical capacity that is slower paced.

Some respondents viewed CP as a new opportunity for lateral career movement within the paramedic profession, ideal for paramedics in the late-stage of their career as it offered less physically demanding work. It was also noted that CP could help keep aging paramedics in the service for a longer period of time and the community could continue benefiting from their skill set.

After having worked in the new role, paramedics described CP as offering greater freedoms compared to the demands of shift work in traditional emergency response roles. CP offered freedom from the demands of shift work by providing better hours, increased autonomy, reduced physical demands, and reduced paramedic stress. For paramedics with longer years of service, this was a welcomed change of pace, with some reporting mental and physical health improvements. Others noted the importance of still being connected to the community in this new role. For others, adjustment to the slower pace of the CP role was difficult due to their preference for emergency work..

I enjoyed being still involved with the community but not having to have the daily physical demands of responding to 911 calls. The role is less stressful and after being a paramedic on the road for 14 years it is an amazing and a welcome change of pace both mentally and physically. (P. 58)

The autonomy to structure my day without the oversight of dispatch or supervisors. (P.63)

[It] would be great for light duty/modified work, could keep aging medics on for [a] longer period of time, good idea for last years of work. (P.51)

I prefer a higher paced environment dealing with acute injuries…(P.30)

Theme 3: Paramedics viewed and experienced the CP role as a practice paradigm shift

Before working in a CP role, paramedics viewed ed CP to be a practice paradigm shift for the profession. For some, this shift in practice was thought to be in opposition to the traditional emergency care role while others felt it was a natural extension of paramedic practice.

I did not feel that was something I would enjoy as it does not have the same adrenaline rush you get when on emergency calls. (P. 13)

[I] felt it was long overdue and a natural extension of what we were already doing in an emergency capacity. (P. 43)

I thought that it would be the next step in emergency medicine, our next frontier. Fire has prevention, we should have health promotion. (P. 26)

After working in a CP role and experiencing the practice paradigm shift first-hand, paramedics noted being largely satisfied by their newly expanded skill set, but also felt that it was a significant learning curve. Paramedics experienced negative sentiments from their peers in traditional emergency response regarding the CP role, highlighting the diverging paradigms between the two roles.

Sub-theme 3A: expanded skill set

The CP program expanded paramedics’ skill set to provide better care to program participants. Some of the new clinical skills described included medication provision, suturing, catheterization, point-of-care testing. Paramedics felt these skills improved their overall ability to perform when returning to emergency response duties. Others felt these new clinical skills were not used or required for the CP role because participants were mainly looking to socialize and interact.

I very much enjoyed the increased scope of practice. I believe that it allows me to provide better care and assist people in the community more than I have before. Moreover, I feel that the additional training has made me a better, and more well-rounded medic overall. (P.34)

I enjoyed the expanded roles (phlebotomy, catheterization, suturing etc)...(P.25).

Sub-theme 3B: learning curve

Working in a CP role was a significant learning curve for some paramedics. Challenges included learning soft skills such as communication, confidence leading sessions with older adults, and learning administrative tasks such as new documentation and computer skills. For paramedics working in both emergency response and CP roles, it was difficult to shift between emergency response protocols and CP protocols. This may have been due to competing priorities between emergency response and CP protocols, such as deciding whether to transport an individual to hospital or keeping an individual at home.

It is a difficult shift in frame of mind to go from 911 assessments to CP assessments and having to switch back into 911 mode when necessary...It can be tough to play the role of both emerg[ency] response and CP. (P.18)

Adapting to new ways, changing the way you do calls, learning the CP documentation and computer programs, being confident with [program participants] and visits, knowing when to communicate with the providers and how. (P. 2)

Much more patient advocacy & health teaching then I had expected. (P.14)

Sub-theme 3C: negative paramedic culture

Community paramedics described a negative paramedic culture that is unaccepting of the CP role and its softer skill set. Lack of buy-in from paramedics in traditional emergency response roles, along with poor understanding of the positive impacts of CP programming, have led to negative perceptions of the role in the paramedic workforce. Community paramedics felt that their emergency response colleagues did not respect their role and felt misunderstood by the profession at large.

Paramedic culture that needs to be educated and changed on the value of CP work. (P.32)

Misunderstood by co-workers and some management. Labeled the tea and cookie brigade. (P.24)

I also found that EMS crews treated CP with very little mutual respect and understanding... (P. 41)

There were a number of positive and negative aspects of the CP role identified by paramedic respondents. While the majority of respondents felt that working in a CP program was a largely positive experience, some expressed dissatisfaction and difficulty adapting to the role. Many positive aspects of the CP role also had unintended negative aspects, particularly as it related to paramedics’ sense of professional identity and their mental health experience when working in the CP role. In order to ensure paramedic job satisfaction and understand the future state of CP programs, these opposing experiences need to be further examined and addressed.

Paramedic professional identity

While many paramedics felt CP was an extension of the paramedic identity, some felt it was a threat to the traditional paramedic identity, removing the defining element of ‘emergency response’ and blurring professional boundaries with other health and social service roles. These diverging experiences and attitudes towards the CP role and its place in the paramedicine profession suggest that there are different fractional identities within the paramedic workforce. Donelley et al. found that emergency service workers often define their role using four domains: caregiving (helping individuals in need), thrill seeking (the adrenaline rush experienced during critical incidents), capacity (having the knowledge, skills, and training to act), and duty (obligation to one’s community and service) [ 7 ]. Paramedics who understand their professional identity as falling within the ‘caregiving’ or ‘duty’ domain may be more accepting of the CP role and understand its fit within their existing paramedic mandates. However, paramedics who understand their professional identity as falling within the ‘thrill seeking’ and ‘capacity to conduct an emergency response’ domain may view CP as not only redefining and expanding the profession, but a threat to the professional identity. Expansion and further resourcing of CP programs may exacerbate divisions and tensions between staff who have different professional motivations if these concerns are not addressed.

Paramedic mental health

Working in a CP role may have also led to some improvements in paramedic mental health. In the traditional emergency response role, paramedics take on shift work, are often exposed to traumatic emergency response incidents, and are limited in their interactions with individuals in their care (single touchpoint and limited time). In contrast, community paramedics experienced more freedom to structure their day, new opportunities to build relationships with program participants due to multiple touchpoints and they experienced reduced physical demands. These experiences likely contributed to a less stressful, flexible work environment which in turn improved mental health for some.

However, increased socialization with participants also introduced new emotional burdens and stressors for some community paramedics. Increased attachment to program participants often made it difficult to deal with their deaths. Participants are often vulnerable populations who face complex health and social issues, such as poverty and addiction. Increased contact with vulnerable populations may increase paramedics’ exposure to vicarious trauma or ‘compassion fatigue,’ which refers to the secondary trauma experienced by working closely with individuals who have experienced trauma first-hand [ 13 , 14 , 15 ]. Vicarious trauma and compassion fatigue can have similar negative impacts on paramedic mental health as first-hand trauma, leading to emotional disturbances, stress, intrusive thoughts, and reduced productivity [ 15 ]. Particularly for community paramedics with a strong orientation towards empathy and caregiving, compassion fatigue may be experienced as a negative or challenging consequence of the role [ 15 ].

Considerations for CP programming

The experiences of paramedics working in a CP program suggests the CP role comes with new opportunities and challenges for staff and the profession at large. Paramedics have broad and diverse understandings of their professional identity, leading some to view CP as a natural fit within the profession while others view it as extending too far beyond the boundaries of paramedicine. This suggests the need for paramedic leaders to clearly define the purpose, mandate, and function of the CP role within the paramedic workforce. Paramedic services interested in implementing and expanding on CP programs to achieve program outcomes such as a reduction in emergency calls and improving participant health outcomes should reflect on their workplace culture and consider the role of their leadership in promoting this role. Champions of CP programming may be identified to better support the workforce’s understanding of this role and how it fits within larger paramedic mandates and objectives. Paramedic leaders who are championing the CP role should consider what factors may contribute to a paramedic feeling alienated in a CP role and how staff are selected to fill this role. In addition, negative perceptions of the CP role as ‘soft’ or ‘easy’ in comparison to emergency response roles needs to be dispelled if community paramedics are to feel valued for their efforts and contributions.

In addition, a number of training supports may need to be provided that take into consideration the new emotional burdens of the CP role. While the CP role may contribute to good mental health by providing a flexible work environment, reducing exposure to traumatic incidents, and allowing paramedics to socialize with individuals in their care, it may also put some paramedics at risk for vicarious trauma and compassion fatigue. Drawing from professions such as social work and counselling, a number of training and professional development supports can be provided to reduce compassion fatigue. Examining compassion fatigue in community paramedics, Cornelius et al. suggests that paramedics should establish boundaries when working with program participants, ensuring that participants recognize the relationship between them and the paramedic is time limited [ 15 ]. Additionally, the caseload of community paramedics should be examined and managed by supervisors in terms of size and complexity of cases [ 15 ]. Other paramedic supports could include resiliency training, counselling services, and stress management workshops [ 15 ]. Training provided should match the type and scope of the CP program the paramedic is working in and their work environment.

Limitations

A limitation of this study is that it used an online survey with predefined open-ended questions to extract information on lived experience rather than a semi-structured interview. This approach prevented researchers from prompting paramedics on their responses and engaging in discussion to obtain a deeper description of their experiences. However, the survey approach allowed the research team to obtain responses from a large number of paramedics and collect responses from across Ontario. Another limitation is that due to the inherent nature of the survey link, it cannot be guaranteed that unique responses were captured. However, multiple entries from respondents are unlikely.

Future research should attempt to engage paramedics on the issues described in this paper and should consider how the relative impacts of working in different types of CP programs (e.g., clinic style programs, at home visits, etc.) may affect paramedic experiences. This approach may provide more detailed data to inform future CP training and program design.

This paper found paramedics who have worked in a CP role, reported that the role offered opportunities to fill a gap in the healthcare system, to move laterally within the paramedic profession, and to create a practice paradigm shift within the profession. Most described having positive perceptions of their professional identity after working as a CP, as they were able to fulfill stepping into a helping role to a greater extent. In contrast, some came out of the experience with negative perceptions. It is important for CP program planners to consider these diverse experiences when planning for the expansion of these programs. A workforce culture that views CP programming negatively and as potentially eroding the traditional paramedic identity may work to hinder the program’s ability to achieve positive outcomes such as a reduction in emergency calls and an improvement in participant health outcomes. Incorporating the CP role within larger paramedic mandates and objectives by paramedic leadership may support this work, as well as CP champions who clarify the role and impacts of CP to staff.

Availability of data and materials

The data that support the findings of this study are not publicly available due to them containing information that could compromise participant privacy. De-identified, limited data will be shared by the corresponding author upon reasonable request.

Abbreviations

• Community Paramedicine

Occupational Stress Injury

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Acknowledgements

We would like to acknowledge the assistance of Brent McLeod and the OPA (Ontario Paramedic Association).

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Aarani Paramalingam, Andrea Ziesmann, Melissa Pirrie, Francine Marzanek, Ricardo Angeles & Gina Agarwal

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The study was conceived of by GA, RA, FM and AP, AZ, GA, RA, FM and MP analysed the data. AP drafted the article under the supervision of GA and all authors were involved in editing to produce a final draft. All authors read and approved the final manuscript.

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Paramalingam, A., Ziesmann, A., Pirrie, M. et al. Paramedic attitudes and experiences working as a community paramedic: a qualitative survey. BMC Emerg Med 24 , 50 (2024). https://doi.org/10.1186/s12873-024-00972-5

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DOI : https://doi.org/10.1186/s12873-024-00972-5

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