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An iterative strategy for referent variable selection in MIMIC-interaction modeling.

Cheng-Hsien Li1, Anne Traynor2

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Summary
This summary is machine-generated.

This study introduces an iterative search strategy for selecting referent variables in multiple-indicator multiple-cause (MIMIC)-interaction models. The new method improves accuracy in measurement invariance testing, especially with higher noninvariance levels.

Keywords:
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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Cross-Cultural Psychology

Background:

  • Measurement invariance testing is crucial for examining cross-group latent differences.
  • Selecting a referent observed variable is a key limitation in multiple-group confirmatory factor analysis (MGCFA).
  • Existing repetitive methods for referent variable selection in MIMIC-interaction models have limitations.

Purpose of the Study:

  • To propose and evaluate an iterative search strategy for referent variable selection in MIMIC-interaction models.
  • To enhance the accuracy of referent variable identification, particularly under conditions of increased noninvariance.
  • To compare the performance of the iterative strategy against the repetitive strategy.

Main Methods:

  • Employed a Monte Carlo simulation design.
  • Investigated the effects of proportion of noninvariance, magnitude of noninvariance, latent variable differences, and sample size.
  • Assessed performance using the accuracy rate of selecting truly invariant observed variables.

Main Results:

  • The proposed iterative strategy generally outperformed the repetitive strategy in identifying credible referent variables.
  • The iterative strategy showed substantial superiority in conditions with high proportions of noninvariance or large latent variable differences.
  • The iterative approach is suggested as a reliable and practical method for referent variable selection.

Conclusions:

  • The iterative search strategy offers improved accuracy for referent variable selection in MIMIC-interaction models.
  • This method is particularly beneficial when dealing with significant levels of measurement noninvariance.
  • An illustrative example demonstrates the practical applicability of the iterative strategy.