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Modeling ordinal intensive longitudinal data (ILD) as continuous in dynamic structural equation models (DSEMs) requires careful consideration. Results suggest at least seven categories for within-person effects, but fewer may suffice for between-person effects.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Intensive longitudinal data (ILD) is increasingly collected via technological innovations, featuring numerous repeated measures.
  • Dynamic structural equation models (DSEMs) are suited for ILD, but often treat ordinal outcomes as continuous, which is of questionable validity.
  • Existing guidelines for modeling ordinal data as continuous, primarily from factor analysis, may not apply to DSEMs due to their unique characteristics.

Purpose of the Study:

  • To evaluate the statistical properties of probit DSEM for ordinal data at realistic ILD sample sizes.
  • To determine the conditions under which ordinal ILD can be defensibly modeled as continuous within DSEMs.

Main Methods:

  • Simulations were used to assess the performance of probit DSEM with varying numbers of ordinal categories.
  • The study examined the accuracy of estimating within-person and between-person effects under different conditions.
  • Comparisons were made to existing factor analysis literature on continuous treatment of ordinal data.

Main Results:

  • Accurate estimation of within-person effects in DSEMs typically requires at least seven ordinal categories.
  • Between-person effects can be reasonably estimated with as few as five, and sometimes three, ordinal categories.
  • Findings indicate that factor analysis guidelines for continuous modeling of ordinal data are not directly applicable to DSEMs.

Conclusions:

  • The assumption of continuity for ordinal ILD in DSEMs is not universally defensible and depends heavily on the number of response categories.
  • Specific recommendations for modeling ordinal ILD within DSEMs are needed, diverging from factor analysis conventions.
  • Further research is warranted to refine best practices for analyzing ordinal ILD using DSEMs.