Examples of qualitative comparative analysis

Examples of qualitative comparative analysis

Qualitative comparative analysis finds its utility in a diverse range of fields, and its flexibility makes it a favorite among researchers tackling intricate questions. Within research on politics and democratic transitions, the use of QCA, particularly “crisp set QCA”, is evident. This version of QCA, which relies on binary distinctions (e.g., democratic vs. non-democratic), aids researchers in understanding the myriad conditions—such as civil unrest, economic stability, international influences, and historical legacies—that lead to a nation’s democratic evolution. Utilizing crisp set QCA, researchers pinpoint combinations of these conditions that consistently catalyze democratic shifts.

In health care research, specifically studies analyzing the effectiveness of web-based campaigns promoting vaccination, “multi-value QCA” may be more suitable. Unlike its binary counterpart, multi-value QCA allows for more than two values in the causal conditions. This is particularly useful when examining a variety of factors, such as age groups, different socioeconomic brackets, and varying levels of prior beliefs. With this nuanced approach, researchers can systematically determine which combination of conditions are related to heightened vaccination rates.

What is the qualitative comparative analysis method?

Conducting QCA involves a series of structured steps that guide researchers from the initial phase of conceptualizing their study to the final interpretation of results. Here’s a simplified breakdown of the process:

1. Case selection: Begin by choosing the cases you wish to study. These cases should have varying outcomes concerning the research question, ensuring a mix of both positive and negative results.
2. Define conditions and outcomes: Clearly define the causal conditions you believe influence the outcome. These can be binary (e.g., success/failure) in crisp set QCA or more nuanced in fuzzy set or multi-value QCA. Additionally, identify the outcome or outcomes of interest.
3. Calibration: Assign values to each causal condition within each case. In crisp set QCA, this is a straightforward binary distinction. However, in fuzzy set QCA, the causal conditions need to be calibrated to indicate the degree of membership of each case in a given condition (i.e., given a value between 0 and 1, which refers to full membership). These set membership scores depend on each condition and the dataset, such that researchers’ chosen cutoff points are key to fuzzy set analysis.
4. Construct a truth table: After assigning values to each causal condition, create a truth table. This data matrix lists all possible combinations of conditions and their associated outcomes. It’s a visual representation of how different conditions are related to the desired outcome.
5. Analyze patterns: With the truth table at hand, identify patterns that lead to the outcome of interest. Look for combinations of conditions that consistently result in a particular outcome. Dedicated computer software for QCA can greatly facilitate this process by calculating and setting frequency and consistency values. Determining cutoff points (both for determining set membership and which possible configurations are related to the presence of the outcome) is often an iterative process, as researchers can try different combinations based on their causal inferences.
6. Interpretation and presentation: After setting up the truth table and indicating the positive or negative outcomes of each combination, run the analysis and interpret the findings. The results convey which combinations of causal conditions are necessary or sufficient for the desired outcome. These findings can be presented in a manner that highlights the causal complexity and provides insights into the phenomenon under study. Researchers typically present the results of QCA in a table displaying the different causal configurations with symbols indicating the absence or presence of each condition within each configuration.