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Related Experiment Video

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Dynamic measurement invariance cutoffs for longitudinal and dyadic data.

Daniel McNeish1

  • 1Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, 85287, USA. dmcneish@asu.edu.

Behavior Research Methods
|March 14, 2026
PubMed
Summary
This summary is machine-generated.

Dynamic measurement invariance (DMI) provides tailored cutoffs for behavioral research. This study extends DMI to dependent groups (DG-DMI), offering new guidance for longitudinal and dyadic data analysis.

Keywords:
Fit index differenceLongitudinal invarianceLongitudinal measurement invarianceMeasurement equivalenceMeasurement invariance

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Area of Science:

  • Psychometrics
  • Behavioral Science
  • Statistical Modeling

Background:

  • Measurement invariance ensures score comparability across groups or time.
  • Traditional fit index cutoffs have limited generalizability.
  • Dynamic measurement invariance (DMI) offers custom-tailored cutoffs but is limited to independent groups.

Purpose of the Study:

  • To extend dynamic measurement invariance (DMI) cutoffs to dependent groups (DG-DMI).
  • To provide guidance for interpreting fit index differences in models with correlated responses across groups.
  • To address limitations of current methods for longitudinal and dyadic data.

Main Methods:

  • Proposing and describing the DG-DMI method.
  • Utilizing statistical modeling for dependent groups with between-group correlations.
  • Applying DG-DMI to three empirical examples.

Main Results:

  • Demonstrating the application of DG-DMI for assessing measurement invariance in dependent groups.
  • Providing empirical evidence for the utility of DG-DMI in longitudinal and dyadic data.
  • Offering open-source software for practical implementation.

Conclusions:

  • DG-DMI extends DMI principles to dependent groups, enhancing measurement invariance analysis.
  • The proposed method offers crucial guidance for researchers working with longitudinal or dyadic data.
  • DG-DMI facilitates more accurate and generalizable conclusions about score comparability in complex data structures.