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Effect size measure for mediation analysis with a multicategorical predictor

Many currently available effect size measures for mediation have limitations when the predictor is nominal with three or more categories. The mediation effect size measure υ was adopted for this situation. A simulation study was conducted to investigate the performance of its estimators. We manipula...

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Autores principales: Cao, Zihuan, Cham, Heining, Stiver, Jordan, Rivera Mindt, Monica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036747/
https://www.ncbi.nlm.nih.gov/pubmed/36968723
http://dx.doi.org/10.3389/fpsyg.2023.1101440
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author Cao, Zihuan
Cham, Heining
Stiver, Jordan
Rivera Mindt, Monica
author_facet Cao, Zihuan
Cham, Heining
Stiver, Jordan
Rivera Mindt, Monica
author_sort Cao, Zihuan
collection PubMed
description Many currently available effect size measures for mediation have limitations when the predictor is nominal with three or more categories. The mediation effect size measure υ was adopted for this situation. A simulation study was conducted to investigate the performance of its estimators. We manipulated several factors in data generation (number of groups, sample size per group, and effect sizes of paths) and effect size estimation [different R-squared (R(2)) shrinkage estimators]. Results showed that the Olkin–Pratt extended adjusted R(2) estimator had the least bias and the smallest MSE in estimating υ across conditions. We also applied different estimators of υ in a real data example. Recommendations and guidelines were provided about the use of this estimator.
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spelling pubmed-100367472023-03-25 Effect size measure for mediation analysis with a multicategorical predictor Cao, Zihuan Cham, Heining Stiver, Jordan Rivera Mindt, Monica Front Psychol Psychology Many currently available effect size measures for mediation have limitations when the predictor is nominal with three or more categories. The mediation effect size measure υ was adopted for this situation. A simulation study was conducted to investigate the performance of its estimators. We manipulated several factors in data generation (number of groups, sample size per group, and effect sizes of paths) and effect size estimation [different R-squared (R(2)) shrinkage estimators]. Results showed that the Olkin–Pratt extended adjusted R(2) estimator had the least bias and the smallest MSE in estimating υ across conditions. We also applied different estimators of υ in a real data example. Recommendations and guidelines were provided about the use of this estimator. Frontiers Media S.A. 2023-03-10 /pmc/articles/PMC10036747/ /pubmed/36968723 http://dx.doi.org/10.3389/fpsyg.2023.1101440 Text en Copyright © 2023 Cao, Cham, Stiver and Rivera Mindt. https://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) and the copyright owner(s) 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
Cao, Zihuan
Cham, Heining
Stiver, Jordan
Rivera Mindt, Monica
Effect size measure for mediation analysis with a multicategorical predictor
title Effect size measure for mediation analysis with a multicategorical predictor
title_full Effect size measure for mediation analysis with a multicategorical predictor
title_fullStr Effect size measure for mediation analysis with a multicategorical predictor
title_full_unstemmed Effect size measure for mediation analysis with a multicategorical predictor
title_short Effect size measure for mediation analysis with a multicategorical predictor
title_sort effect size measure for mediation analysis with a multicategorical predictor
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036747/
https://www.ncbi.nlm.nih.gov/pubmed/36968723
http://dx.doi.org/10.3389/fpsyg.2023.1101440
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