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Bayesian SEM for Specification Search Problems in Testing Factorial Invariance.

Dexin Shi1, Hairong Song1, Xiaolan Liao1

  • 1a University of Oklahoma.

Multivariate Behavioral Research
|April 22, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-step method for testing factorial invariance, improving the selection of reference indicators and identification of non-invariant parameters in structural equation modeling.

Keywords:
Bayesian methodfactorial invariance: informative priorsreference indicators

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Structural Equation Modeling

Background:

  • Factorial invariance testing is crucial for cross-group comparisons.
  • Selecting reference indicators and locating non-invariant parameters remain challenging.
  • Existing methods lack comprehensive solutions for specification search problems.

Purpose of the Study:

  • To propose a two-step procedure for addressing specification search problems in factorial invariance testing.
  • To enhance the accuracy of identifying reference indicators and non-invariant parameters.
  • To provide a practical framework for applied researchers.

Main Methods:

  • A two-step procedure combining Bayesian structural equation modeling and parameter localization.
  • Step 1: Identifying a proper reference indicator based on invariance likelihood.
  • Step 2: Locating specific non-invariant parameters after reference indicator selection.

Main Results:

  • The proposed method demonstrates robust performance across various data conditions.
  • Optimal performance is achieved with large non-invariance magnitudes, low non-invariance proportions, and large sample sizes.
  • Simulation analyses validate the effectiveness of the two-step procedure.

Conclusions:

  • The proposed method offers an effective solution for specification search problems in factorial invariance testing.
  • The approach facilitates more accurate cross-group comparisons by improving parameter identification.
  • The study highlights the importance of informative priors and discusses potential extensions.