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The consequences of ignoring measurement invariance for path coefficients in structural equation models

We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strat...

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Autores principales: Guenole, Nigel, Brown, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4166111/
https://www.ncbi.nlm.nih.gov/pubmed/25278911
http://dx.doi.org/10.3389/fpsyg.2014.00980
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author Guenole, Nigel
Brown, Anna
author_facet Guenole, Nigel
Brown, Anna
author_sort Guenole, Nigel
collection PubMed
description We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance – non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds – in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direction to the difference in loadings and thresholds relative to the referent group (i.e., lower loadings and thresholds for the focal group lead to overestimated regression parameters). With criterion non-invariance, the focal group regression parameters are biased in the same direction as the difference in loadings and thresholds relative to the referent group. While unacceptable levels of parameter bias were confined to the focal group, bias occurred at considerably lower levels of ignored non-invariance than was previously recognized in referent and focal groups.
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spelling pubmed-41661112014-10-02 The consequences of ignoring measurement invariance for path coefficients in structural equation models Guenole, Nigel Brown, Anna Front Psychol Psychology We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance – non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds – in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direction to the difference in loadings and thresholds relative to the referent group (i.e., lower loadings and thresholds for the focal group lead to overestimated regression parameters). With criterion non-invariance, the focal group regression parameters are biased in the same direction as the difference in loadings and thresholds relative to the referent group. While unacceptable levels of parameter bias were confined to the focal group, bias occurred at considerably lower levels of ignored non-invariance than was previously recognized in referent and focal groups. Frontiers Media S.A. 2014-09-17 /pmc/articles/PMC4166111/ /pubmed/25278911 http://dx.doi.org/10.3389/fpsyg.2014.00980 Text en Copyright © 2014 Guenole and Brown. http://creativecommons.org/licenses/by/4.0/ 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
Guenole, Nigel
Brown, Anna
The consequences of ignoring measurement invariance for path coefficients in structural equation models
title The consequences of ignoring measurement invariance for path coefficients in structural equation models
title_full The consequences of ignoring measurement invariance for path coefficients in structural equation models
title_fullStr The consequences of ignoring measurement invariance for path coefficients in structural equation models
title_full_unstemmed The consequences of ignoring measurement invariance for path coefficients in structural equation models
title_short The consequences of ignoring measurement invariance for path coefficients in structural equation models
title_sort consequences of ignoring measurement invariance for path coefficients in structural equation models
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4166111/
https://www.ncbi.nlm.nih.gov/pubmed/25278911
http://dx.doi.org/10.3389/fpsyg.2014.00980
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