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Structural Models for Binary Repeated Measures: Linking Modern Longitudinal Structural Equation Models to
Structural Equation Modeling : a Multidisciplinary Journal
|December 8, 2018
Summary
Structural equation models (SEMs) can now analyze binary data over time, offering an alternative to traditional matched pair analyses. This research connects SEMs to conventional methods, enabling more complex longitudinal research questions.
Area of Science:
- Statistics
- Psychometrics
- Quantitative Psychology
Background:
- Traditional analyses for binary repeated measures, like McNemar's chi-squared, are limited in scope.
- Software advancements now facilitate complex statistical modeling using structural equation models (SEMs) with binary indicators.
Purpose of the Study:
- To describe the connection between conventional categorical data analyses for matched pairs/repeated measures and simple longitudinal SEMs.
- To demonstrate how SEMs can extend the analysis of binary data over time.
- To explore advanced longitudinal modeling strategies for complex research questions.
Main Methods:
- Describing the theoretical link between conventional tests and longitudinal SEMs.
- Illustrating several SEMs for analyzing binary repeated measures.
- Comparing results from conventional analyses and equivalent SEMs.
Main Results:
- Established the equivalence between several conventional analyses and specific longitudinal SEMs.
- Highlighted foundational concepts in longitudinal modeling through this connection.
- Identified potential SEM extensions for complex research designs.
Conclusions:
- SEMs provide a flexible and powerful framework for analyzing binary repeated measures and matched pairs.
- The integration of SEMs allows for more sophisticated investigation of proportional differences over time.
- Researchers can now address more complex hypotheses, including interactions and latent class comparisons, using SEMs.
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