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A Note on the Structural Change Test in Highly Parameterized Psychometric Models.

K B S Huth1,2,3, L J Waldorp4, J Luigjes5

  • 1Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129B, PO Box 15906, 1001 NK, Amsterdam, The Netherlands. k.huth@uva.nl.

Psychometrika
|February 1, 2022
PubMed
Summary
This summary is machine-generated.

Permutation approaches improve structural change tests by estimating sampling distributions without assumptions. This enhances statistical power and overcomes limitations of traditional methods in psychometric models.

Keywords:
finite sample behaviorparameter invarianceparameter stabilitypermutation teststructural change test

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Area of Science:

  • Psychometrics and Statistical Modeling
  • Behavioral and Social Sciences Research

Background:

  • Parameter invariance across subgroups is crucial for statistical tests, impacting replicability and theory.
  • Ignoring subgroup differences threatens study validity and model specification.
  • Structural change tests assess parameter invariance but rely on asymptotic assumptions.

Purpose of the Study:

  • To address the limitations of traditional structural change tests in small samples and large psychometric models.
  • To introduce and evaluate permutation approaches as an alternative for obtaining sampling distributions.
  • To enhance the power and validity of structural change tests.

Main Methods:

  • Investigated the empirical fluctuation process in structural change tests.
  • Analyzed the deviation of the empirical fluctuation process from Brownian bridge in small samples.
  • Implemented and compared permutation approaches against standard asymptotic approximations for sampling distribution estimation.

Main Results:

  • The empirical fluctuation process deviates from Brownian bridge in small samples, especially in large psychometric models.
  • Standard methods for obtaining sampling distributions are invalid, leading to conservative structural change tests.
  • Permutation approaches provide a valid estimation of the sampling distribution, avoiding distributional assumptions and improving test power.

Conclusions:

  • Permutation approaches offer a superior alternative to asymptotic approximations for structural change tests.
  • Resampling methods enhance statistical power and address limitations of traditional inference in psychometric modeling.
  • The study advocates for permutation-based methods to improve the reliability and validity of structural change tests.