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

Kim De Roover*, Marieke E. Timmerman, Jozefien De Leersnyder, Batja Mesquita, Eva Ceulemans

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)
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Abstract

The issue of measurement invariance is ubiquitous in the behavioral sciences nowadays as more and more studies yield multivariate multigroup data. When measurement invariance cannot be established across groups, this is often due to different loadings on only a few items. Within the multigroup CFA framework, methods have been proposed to trace such non-invariant items, but these methods have some disadvantages in that they require researchers to run a multitude of analyses and in that they imply assumptions that are often questionable. In this paper, we propose an alternative strategy which builds on clusterwise simultaneous component analysis (SCA). Clusterwise SCA, being an exploratory technique, assigns the groups under study to a few clusters based on differences and similarities in the component structure of the items, and thus based on the covariance matrices. Non-invariant items can then be traced by comparing the cluster-specific component loadings via congruence coefficients, which is far more parsimonious than comparing the component structure of all separate groups. In this paper we present a heuristic for this procedure. Afterwards, one can return to the multigroup CFA framework and check whether removing the non-invariant items or removing some of the equality restrictions for these items, yields satisfactory invariance test results. An empirical application concerning cross-cultural emotion data is used to demonstrate that this novel approach is useful and can co-exist with the traditional CFA approaches.

Original languageEnglish
Article number604
Number of pages11
JournalFrontiers in Psychology
Volume5
DOIs
Publication statusPublished - 20 Jun 2014
Externally publishedYes

Keywords

  • measurement bias
  • configurel invariance
  • weak invariance
  • metric invariance
  • COVARIANCE STRUCTURE-ANALYSIS
  • COMMON FACTOR-ANALYSIS
  • FACTORIAL INVARIANCE
  • FIT INDEXES
  • SPECIFICATION SEARCHES
  • EMOTIONAL EXPERIENCE
  • MODELS
  • ISSUES

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