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A multiple imputation score test for model modification in structural equation models.

Maxwell Mansolf1, Terrence D Jorgensen2, Craig K Enders1

  • 1Department of Psychology.

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Summary
This summary is machine-generated.

A new score test for multiply imputed data in structural equation modeling (SEM) was developed. This imputation-based score test helps identify local model misfit, performing well in simulations.

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Structural equation modeling (SEM) commonly uses likelihood ratio, Wald, and score tests for model evaluation.
  • Current significance testing options are limited for multiply imputed datasets due to the lack of a general score test.

Purpose of the Study:

  • To introduce a novel score test specifically designed for multiply imputed data in SEM.
  • To provide a method for identifying local model misfit within SEM analyses using multiply imputed data.

Main Methods:

  • Developed an imputation-based score test, mirroring the expected parameter change statistic from complete-data analyses.
  • Assessed the performance of the new score test using a simulation study, comparing its Type I error rate and power against the full information maximum likelihood (FIML) score test.
  • Made the procedure available in the R package semTools.

Main Results:

  • The proposed imputation-based score test demonstrated good calibration.
  • In some scenarios, the score test showed slightly reduced power compared to the FIML statistic due to the two-stage nature of multiple imputation.
  • The test effectively functions as a model modification index for identifying local misfit.

Conclusions:

  • The developed score test offers a valuable tool for assessing local model fit in SEM with multiply imputed data.
  • The imputation-based score test is a viable alternative to existing methods, particularly for identifying specific areas of model misspecification.