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Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data

BACKGROUND: The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nest...

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Autores principales: Sharafi, Zahra, Mousavi, Amin, Ayatollahi, Seyyed Mohammad Taghi, Jafari, Peyman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613630/
https://www.ncbi.nlm.nih.gov/pubmed/29312463
http://dx.doi.org/10.1155/2017/7571901
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author Sharafi, Zahra
Mousavi, Amin
Ayatollahi, Seyyed Mohammad Taghi
Jafari, Peyman
author_facet Sharafi, Zahra
Mousavi, Amin
Ayatollahi, Seyyed Mohammad Taghi
Jafari, Peyman
author_sort Sharafi, Zahra
collection PubMed
description BACKGROUND: The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. METHODS: The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. RESULTS: Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. CONCLUSIONS: The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.
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spelling pubmed-56136302018-01-08 Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data Sharafi, Zahra Mousavi, Amin Ayatollahi, Seyyed Mohammad Taghi Jafari, Peyman Comput Math Methods Med Research Article BACKGROUND: The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. METHODS: The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. RESULTS: Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. CONCLUSIONS: The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed. Hindawi 2017 2017-09-12 /pmc/articles/PMC5613630/ /pubmed/29312463 http://dx.doi.org/10.1155/2017/7571901 Text en Copyright © 2017 Zahra Sharafi et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Sharafi, Zahra
Mousavi, Amin
Ayatollahi, Seyyed Mohammad Taghi
Jafari, Peyman
Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data
title Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data
title_full Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data
title_fullStr Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data
title_full_unstemmed Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data
title_short Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data
title_sort assessment of differential item functioning in health-related outcomes: a simulation and empirical analysis with hierarchical polytomous data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613630/
https://www.ncbi.nlm.nih.gov/pubmed/29312463
http://dx.doi.org/10.1155/2017/7571901
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