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What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component

Kim De Roover1, Marieke E Timmerman2, Jozefien De Leersnyder1

  • 1Methods, Individual and Cultural Differences, Affect and Social Behavior, KU Leuven Leuven, Belgium.

Frontiers in Psychology
|July 8, 2014
PubMed
Summary

This study introduces a parsimonious method for identifying non-invariant items in multivariate multigroup data using clusterwise simultaneous component analysis (SCA). This approach simplifies detecting measurement invariance issues in behavioral sciences research.

Keywords:
configural invariancemeasurement biasmetric invarianceweak invariance

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Area of Science:

  • Psychometrics
  • Behavioral Sciences
  • Multivariate Statistics

Background:

  • Measurement invariance is crucial for multivariate multigroup data in behavioral sciences.
  • Existing methods for detecting non-invariant items within multigroup CFA are often cumbersome and rely on questionable assumptions.
  • Identifying specific items that lack measurement invariance across groups is essential for valid cross-group comparisons.

Purpose of the Study:

  • To propose a more parsimonious and efficient strategy for tracing non-invariant items in multivariate multigroup data.
  • To introduce clusterwise simultaneous component analysis (SCA) as an exploratory technique for identifying measurement non-invariance.
  • To provide a heuristic for the proposed clusterwise SCA procedure and demonstrate its utility.

Main Methods:

  • The study proposes a novel approach utilizing clusterwise simultaneous component analysis (SCA).
  • Groups are clustered based on similarities and differences in item component structures derived from covariance matrices.
  • Non-invariant items are identified by comparing cluster-specific component loadings using congruence coefficients.

Main Results:

  • Clusterwise SCA offers a more parsimonious alternative to traditional methods for tracing non-invariant items.
  • The proposed heuristic effectively identifies items lacking measurement invariance across groups.
  • The approach was demonstrated to be useful in an empirical application with cross-cultural emotion data.

Conclusions:

  • The novel clusterwise SCA approach provides an efficient method for identifying non-invariant items in multigroup studies.
  • This exploratory technique can be integrated with traditional multigroup CFA for robust measurement invariance testing.
  • The findings suggest that this method can coexist with and complement existing CFA approaches, enhancing the analysis of complex datasets.