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Evaluating methods for handling missing ordinal data in structural equation modeling.

Fan Jia¹, Wei Wu²

¹University of Kansas, Lawrence, KS, USA. fanjia@ku.edu.

Behavior Research Methods

|January 27, 2019

Summary

This summary is machine-generated.

Missing ordinal data in structural equation modeling (SEM) is common. This study evaluated five methods, offering guidance on the best approaches for handling such missing data in SEM research.

Keywords:

Missing ordinal data Multiple imputation Robust estimation Structural equation modeling

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Area of Science:

Statistics
Psychometrics
Quantitative Psychology

Background:

Missing data are prevalent in studies employing structural equation modeling (SEM).
Existing methods for addressing missing ordinal data lack systematic evaluation within SEM contexts.
This gap hinders robust statistical analysis and reliable model interpretation.

Purpose of the Study:

To systematically evaluate and compare five distinct methods for handling missing ordinal data in SEM.
To identify the most effective methods based on performance under various missing data conditions.
To provide evidence-based recommendations for researchers using SEM.

Main Methods:

Utilized Monte Carlo simulation to generate data with varying missingness patterns.
Compared one direct robust estimation method against four multiple imputation techniques.
Assessed method performance based on accuracy, bias, and efficiency in SEM parameter estimation.

Main Results:

Simulation results indicated significant differences in the performance of the evaluated methods.
Multiple imputation methods generally outperformed the direct robust estimation method under most conditions.
Specific imputation strategies demonstrated superior accuracy and stability for SEM analyses.

Conclusions:

Researchers should carefully select methods for handling missing ordinal data in SEM.
Multiple imputation is often a preferred strategy, but the choice of imputation method matters.
The study provides practical guidance to improve the quality and validity of SEM research with missing ordinal data.