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Test treatment effect differences in repeatedly measured symptoms with binary values: The matched correspondence
1Department of Psychology, Fordham University, The Bronx, NY, USA. sekim@fordham.edu.
Behavior Research Methods
|February 21, 2020
Summary
This study introduces matched correspondence analysis (matched CA) for analyzing changes in multiple binary variables over time. This novel statistical method is suitable for within-group comparisons, offering an alternative to traditional tests for related dichotomous data.
Area of Science:
- Statistics
- Biostatistics
- Data Analysis
Background:
- Traditional statistical tests like paired t tests and chi-squared tests are not ideal for analyzing multiple related binary variables measured over time.
- Existing methods may not adequately capture the nuances of within-group changes in dichotomous data.
Purpose of the Study:
- To introduce a novel statistical approach, matched correspondence analysis (matched CA), for examining differences in related binary variables between two time points.
- To adapt the original matched CA, designed for between-group comparisons, for within-group analysis of matched matrices.
Main Methods:
- Application of matched correspondence analysis (matched CA) to within-group matched matrices (e.g., admission vs. discharge).
- Utilizing permutation and bootstrapping methods to assess the stability of parameter estimates.
- Comparing the novel within-group matched CA approach with traditional statistical methods.
Main Results:
- Matched correspondence analysis (matched CA) provides a viable method for analyzing within-group changes in related binary variables.
- The adapted matched CA effectively examines differences between two time points for dichotomous data.
- Permutation and bootstrapping confirm the stability of the parameter estimates for the within-group matched CA.
Conclusions:
- Within-group matched correspondence analysis (matched CA) is a valuable statistical tool for researchers analyzing longitudinal binary data.
- This method offers a robust alternative when dealing with multiple related dichotomous variables measured at different time points.
- Further discussion on the advantages and limitations of within-group matched CA is provided.
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