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It Matters: Reference Indicator Selection in Measurement Invariance Tests.

Yutian T Thompson1, Hairong Song2, Dexin Shi3

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PubMed
Summary
This summary is machine-generated.

Selecting a reference indicator (RI) is crucial for accurate measurement invariance (MI) testing. Newer methods improve RI selection, but their performance needs evaluation, especially compared to non-RI approaches.

Keywords:
factorial invariancemeasurement invariancemultiple-group CFAreference indicator

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Quantitative Psychology

Background:

  • Traditional methods for selecting reference indicators (RIs) in measurement invariance (MI) testing can yield unreliable results.
  • Emerging quantitative techniques offer more robust RI selection, but their efficacy in identifying truly invariant items remains under-investigated.

Purpose of the Study:

  • To evaluate the performance of newer quantitative methods for reference indicator (RI) selection in measurement invariance (MI) testing using simulated data.
  • To compare the advantages and disadvantages of RI-based versus non-RI-based approaches for MI testing.
  • To empirically assess RI selection methods and RI-based/non-RI-based MI approaches using real-world data.

Main Methods:

  • Study 1 utilized simulated data to assess the accuracy of various RI selection methods under different conditions.
  • Study 2 involved comparing RI-based MI testing strategies against non-RI-based strategies.
  • A large real-world dataset was employed for empirical comparisons of all discussed methods.

Main Results:

  • The performance of newer RI selection techniques was systematically evaluated.
  • Comparative analysis highlighted the strengths and weaknesses of RI-based and non-RI-based MI testing frameworks.
  • Empirical findings from real-world data provided practical insights into method application.

Conclusions:

  • The choice of reference indicator significantly impacts the outcomes of measurement invariance tests.
  • Recommendations are provided for applied researchers on selecting appropriate RI methods and MI testing approaches.
  • This research offers a comprehensive examination of the role and selection of RIs in MI analysis.