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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Dynamic structural equation models with binary and ordinal outcomes in Mplus.

Daniel McNeish1, Jennifer A Somers2, Andrea Savord3

  • 1Arizona State University, PO Box 871104, Tempe, AZ, 85287, USA. dmcneish@asu.edu.

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Summary
This summary is machine-generated.

This tutorial provides accessible guidance on dynamic structural equation models (DSEM) for categorical outcomes, addressing complexities beyond continuous models. It offers practical Mplus code and interpretation strategies for researchers using binary or ordinal data.

Keywords:
Categorical dataDSEMDiscrete dataIntensive longitudinal dataTime-series analysis

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Area of Science:

  • Psychological science
  • Quantitative psychology
  • Longitudinal data analysis

Background:

  • Intensive longitudinal designs are increasingly common in research.
  • Dynamic structural equation models (DSEM) are well-suited for these designs.
  • Existing DSEM resources primarily focus on continuous outcomes, neglecting categorical data complexities.

Purpose of the Study:

  • To provide an accessible tutorial on DSEM for categorical outcomes using Mplus.
  • To clarify the nuances in model building and interpretation for categorical DSEM.
  • To demonstrate practical applications with real-world ecological momentary assessment data.

Main Methods:

  • Focuses on the general probit model for underlying continuous processes.
  • Explains DSEM for binary and ordinal outcomes, highlighting differences from continuous models.
  • Utilizes Mplus software with annotated code for practical examples.

Main Results:

  • Demonstrates unconditional, disaggregated covariate, and time-trend models for binary outcomes.
  • Illustrates model specification and interpretation for ordinal outcomes.
  • Provides insights into the unique challenges and solutions for categorical DSEM.

Conclusions:

  • Categorical outcome DSEM requires specific considerations beyond continuous models.
  • The tutorial offers practical tools and interpretations for empirical researchers.
  • Accessible methods for analyzing complex longitudinal categorical data are presented.