Qualitative Comparative Analysis
What is qualitative comparative analysis?
Qualitative Comparative Analysis (QCA) is a research methodology used in analyzing multiple cases in complex situations. This methodology can help in explaining why change occurs in some cases and why it doesn't happen in others. It is primarily a quantitative analysis method used to analyze qualities, though qualitative data can be used to derive those qualities. QCA should not be confused with qualitative analysis methods or constant comparison method which are forms of qualitative analysis
QCA was developed in 1987s by Charles Ragin, a social scientist. Qualitative comparative analysis is a set-based theory that seeks to explain the relationship between causal conditions and outcomes through the concept of sets and their relations.
QCA models causal conditions as superset or subset relationships between an outcome and explanatory factors. Using this approach, a researcher can determine whether a causal condition is neither sufficient nor necessary, sufficient but not necessary, necessary but not sufficient or sufficient to produce an outcome.
Qualitative comparative analysis approach draws strength from both quantitative and qualitative research methods. It combines the mathematical approaches used in quantitative research with the inductive and comparative case-based techniques employed in qualitative research.
Assumptions of qualitative comparative analysis
QCA is based on two main assumptions. Firstly, it assumes that one factor is rarely sufficient to produce a change. Instead, change is often caused by different combinations of factors. Secondly, Qualitative comparative analysis also recognizes that different combinations of factors can produce the same result.
When do researchers use QCA?
QCA is handy when cases are too small to apply statistical analysis techniques like linear regression and too large for qualitative case-study methodology. It is usually employed for analyzing an intermediate number of cases, usually between 10 and 50.
In addition, researchers use QCA when they predict that the causal structure of an outcome would likely be equifinal, conjunctural, and complex. Equifinal implies that there are different ways to achieve an outcome. Conjunctural means that outcome can only be obtained through a combination of conditions.
Lastly, researchers use qualitative comparative analysis when they are interested in knowing whether the causal conditions are sufficient, necessary, or both for predicting an outcome
How to use qualitative comparative analysis
Generally, there are six steps involved in qualitative comparative analysis. Usually, the steps are iterative, so you may find yourself moving back and forth when using QCA. Below is the summary of how to do QCA:
The first step is to identify the change you are interested in studying and the factors (in theory) that bring these changes. In other words, you determine the outcome you want to explain and define the target set.
The next step is to identify the set of causal conditions expected to contribute to the outcome under study. You can make your selections based on theory, knowledge of the cases, and prior research about the outcome. To use QCA, you are expected to select some cases in which the 'outcome' happened, and some other similar cases which didn't produce the same result.
The third step in QCA is to develop a set of factors commonly known as conditions. These involve listing out outcomes and key factors whose presence or absence may produce those outcomes
After identifying your cases and factors, you are to develop criteria for scoring each of the factors. There are two scoring methods you can adopt — crisp set and fuzzy set. In a crisp set, the score is either '0' or '1', while in the fuzzy set, the score is set at any level between '0' and '1'. For instance, the scores in the fuzzy set can be '0', 0.22, 055, 0.88, or '1'. When using a crisp set, '0' means an absence while '1' means a presence. If the factors can't be classified as present or absent, you should adopt the fuzzy set scoring approach.
After scoring all your factors, the next action is to analyze your dataset. This is usually done using computer software like Tosmana and fs/QCA or a spreadsheet formula/macro. However, if you are working on a small number of cases, you can scan the scores and identify patterns by eye.
Proceed to interpret your findings once the computer software has analyzed your dataset and identified the possible combinations of factors. This involves going to the individual cases you identified and asking whether your findings make sense or not.
Sometimes, it may be necessary to collect more data on the cases, run another dataset analysis using the computer software or even go back to examine if the theories you adopted in step one are still valid. Your study is ready for publishing when you arrive at a satisfactory solution, i.e., an explanation for the outcome under study.
Strengths of qualitative comparative analysis
QCA takes an in-depth look at each case and enables the researcher to draw patterns across different cases. It helps in understanding and explaining why change happens across an intermediary number of cases. Additionally, QCA can be applied in situations where the cases are too few to use traditional statistical analysis. Unlike qualitative approaches, which often do not aim to replicate findings, QCA makes it possible for others to test and replicate your findings.
Weaknesses of qualitative comparative analysis
The first weakness of QCA is that a minimum number of cases is required before you can use it. Secondly, missing information in one factor in any of the cases will render the affected case unusable. Critics have argued that this may lead to situations in which the researcher ignores an essential factor. Lastly, QCA is an iterative process, so it is hard to predict how much time is needed for the study.
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References
Devers, J; Lallemand, N; Burton, R; Kahwati, L; McCall, N; Zuckerman, S. (2013). Using Qualitative Comparative Analysis (QCA) to Study Patient-Centered Medical Homes. Urban Institute.
Ragin, C. (1998). The Logic of Qualitative Comparative Analysis. International Review of Social History.
Sehring, J; Korhonen-Kurki, K; Brockhaus, M. (2013). Qualitative Comparative Analysis (QCA). An application to compare national REDD+policy processes. CIFOR
Simister, N; Scholz, V. (2017). Qualitative Comparative Analysis (QCA). M&E Universer.
