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Evaluating Imputation-Based Fit Statistics in Structural Equation Modeling With Ordinal Data: The MI2S Approach.

Suppanut Sriutaisuk1, Yu Liu2, Seungwon Chung3

  • 1Faculty of Psychology, Chulalongkorn University, Bangkok, Thailand.

Educational and Psychological Measurement
|November 20, 2024
PubMed
Summary
This summary is machine-generated.

The multiple imputation two-stage (MI2S) approach improves structural equation model fit evaluation for ordinal data. MI2S-based mean-adjusted test statistics demonstrated superior performance across various conditions, enhancing model assessment accuracy.

Keywords:
missing datamodel fitmultiple imputationordinal datastructural equation modeling

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Quantitative psychology

Background:

  • Evaluating structural equation model (SEM) fit with ordinal variables and multiply imputed data presents challenges.
  • Previous research focused on residual-based test statistics within the multiple imputation two-stage (MI2S) framework.
  • Limited understanding of alternative test statistics' performance in this context.

Purpose of the Study:

  • To extend the evaluation of the multiple imputation two-stage (MI2S) approach for structural equation models with ordinal variables.
  • To examine the performance of mean-adjusted (T_adj) and mean- and variance-adjusted (T_adj,av) test statistics within the MI2S framework.
  • To compare these alternative statistics against previously studied MI2S-based residual statistics.

Main Methods:

  • Simulation study under various conditions to assess test statistic performance.
  • Application of the MI2S approach to structural equation models with ordinal variables.
  • Implementation using Mplus and R, with code provided for reproducibility.

Main Results:

  • The MI2S-based mean-adjusted test statistic (T_adj) generally outperformed other examined test statistics across a wide range of conditions.
  • The MI2S-based root mean square error of approximation (RMSEA) also demonstrated good performance.
  • The study provides empirical validation and practical implementation guidance.

Conclusions:

  • The MI2S approach, particularly with the mean-adjusted test statistic, offers a robust method for evaluating structural equation model fit for ordinal data with multiply imputed datasets.
  • The findings support the broader applicability and reliability of the MI2S framework in complex statistical analyses.
  • Practical guidance and code are provided to facilitate the adoption of this improved methodology.