13.Qualitative07.Coincidence Analysis (CNA) - sporedata/researchdesigneR GitHub Wiki
- Coincidence Analysis (CNA), of which QCA (Qualitative Comparative Analysis) is a subtype, is often used in studies with small sample sizes, but where researchers still want to investigate the association between certain co-existing variables and outcomes (known as conditions in the QCA literature).
- Healthcare policy analysis - see Covid-19 pandemic by the "real-time" monitoring: the Tunisian case and lessons for global epidemics in the context of 3PM strategies
- Marketing applied to healthcare - see A Qualitative and Quantitative Comparative Analysis of Commercial and Independent Online Information for Hip Surgery: A Bias in Online Information Targeting Patients?
- As an aid in meta-analyses, for example Conventional weight loss interventions across the different BMI obesity classes: A systematic review and quantitative comparative analysis
- As part of mixed methods studies - see Inclusion of intimate partner violence-related content within undergraduate health care professional curriculum: mixed methods study of academics' attitudes and beliefs
- Implementation studies - see Qualitative Comparative Analysis: A Mixed-Method Tool for Complex Implementation Questions
- At least one outcome (condition) and a few predictors.
- Crisp CNA
- The technique relies on finding possible combinations of variables (i.e., order is not important) that are often associated with a given outcome
- Crisp CNA has a standardized set of steps
- Crisp set calibration (variable recategorization) for categorical and continuous variables, using a mix of theory-driven and data analysis.
- Testing of necessity relations, which is measured by counting the frequency of the occurrence of the outcome given the presence of a specific predictor, as a percentage of all all outcomes events. This analysis is aided by Venn diagrams.
- Analysis of sufficiency relationss, which includes complex, parsimonious and intermediate solutions. These include the analysis of all combinations of predictors leading to the outcome.
- Plotting of results, often including Venn diagrams
- Fuzzy CNA
- In contrast with crisp CNA, each of the variables is not Boolean (i.e., having a true/false value), but instead can have continuous values ranging between 0 and 1.
- The two calibration methods include direct and transformational assignment.
- Multivalue CNA, where the outcome (condition) has more than two categories
- Temporal CNA, where the condition (outcome) measurement is repeated over time.
- QCA
- Introduction to the CNA method and package [3]
- The package cutpointr provides tools to determine optimal cutpoints and may assist in the calibration of crisp datasets.
- Visualization
- Books
- Qualitative Comparative Analysis with R: A User’s Guide
- Qualitative Comparative Analysis in Mixed Methods Research and Evaluation
- Articles
- CRISP QCA models
- Necessity tables
- Sufficiency tables
- Venn diagrams
[1] Baumgartner M. Inferring Causal Complexity. Sociological methods & research. 2009 Aug;38(1):71-101.
[2] Baumgartner M, Ambühl M. Causal modeling with multi-value and fuzzy-set Coincidence Analysis. Political Science Research and Methods. 2020 Jul;8(3):526-42.