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Cross-lagged panel modeling with binary and ordinal outcomes.

Bengt Muthén1, Tihomir Asparouhov1, Katie Witkiewitz2

  • 1Mplus.

Psychological Methods
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

This study extends cross-lagged panel modeling to binary and ordinal outcomes, offering new methods for analyzing complex data. It introduces a novel two-part ordinal model suitable for variables with floor effects, enhancing statistical analysis capabilities.

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Area of Science:

  • Statistics
  • Psychometrics
  • Longitudinal Data Analysis

Background:

  • Cross-lagged panel modeling is traditionally limited to continuous outcomes.
  • Existing methods do not adequately address binary or ordinal data, common in social and behavioral sciences.

Purpose of the Study:

  • To present novel statistical methods for cross-lagged panel modeling with binary and ordinal outcomes.
  • To introduce a two-part ordinal model for variables exhibiting floor effects.

Main Methods:

  • Development and discussion of modeling, testing, identification, and estimation techniques for non-continuous outcomes.
  • Proposal of a two-part ordinal model to handle floor effects.
  • Application to an example involving stress and alcohol use in an intervention study.

Main Results:

  • The proposed methods are suitable for analyzing binary and ordinal longitudinal data.
  • The two-part ordinal model effectively addresses strong floor effects.
  • The example demonstrates the utility of the methods in a real-world application.

Conclusions:

  • The presented methods significantly expand the applicability of cross-lagged panel modeling.
  • The new techniques provide valuable tools for researchers working with diverse outcome types.
  • Further extensions for multiple-group and trend analysis are discussed.