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

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 f...

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Autores principales: De Roover, Kim, Timmerman, Marieke E., De Leersnyder, Jozefien, Mesquita, Batja, Ceulemans, Eva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4064661/
https://www.ncbi.nlm.nih.gov/pubmed/24999335
http://dx.doi.org/10.3389/fpsyg.2014.00604
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author De Roover, Kim
Timmerman, Marieke E.
De Leersnyder, Jozefien
Mesquita, Batja
Ceulemans, Eva
author_facet De Roover, Kim
Timmerman, Marieke E.
De Leersnyder, Jozefien
Mesquita, Batja
Ceulemans, Eva
author_sort De Roover, Kim
collection PubMed
description 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.
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spelling pubmed-40646612014-07-04 What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis De Roover, Kim Timmerman, Marieke E. De Leersnyder, Jozefien Mesquita, Batja Ceulemans, Eva Front Psychol Psychology 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. Frontiers Media S.A. 2014-06-20 /pmc/articles/PMC4064661/ /pubmed/24999335 http://dx.doi.org/10.3389/fpsyg.2014.00604 Text en Copyright © 2014 De Roover, Timmerman, De Leersnyder, Mesquita and Ceulemans. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
De Roover, Kim
Timmerman, Marieke E.
De Leersnyder, Jozefien
Mesquita, Batja
Ceulemans, Eva
What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis
title What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis
title_full What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis
title_fullStr What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis
title_full_unstemmed What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis
title_short What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis
title_sort what's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4064661/
https://www.ncbi.nlm.nih.gov/pubmed/24999335
http://dx.doi.org/10.3389/fpsyg.2014.00604
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