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Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol

Background: Applied health science research commonly measures concepts via multiple-item tools (scales), such as self-reported questionnaires or observation checklists. They are usually validated in more detail in separate psychometric studies or very cursorily in substantive studies. However, metho...

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Autor principal: Dima, Alexandra L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Routledge 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8133536/
https://www.ncbi.nlm.nih.gov/pubmed/34040826
http://dx.doi.org/10.1080/21642850.2018.1472602
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author Dima, Alexandra L.
author_facet Dima, Alexandra L.
author_sort Dima, Alexandra L.
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description Background: Applied health science research commonly measures concepts via multiple-item tools (scales), such as self-reported questionnaires or observation checklists. They are usually validated in more detail in separate psychometric studies or very cursorily in substantive studies. However, methodologists advise that, as validity is a property of the inferences based on measurement in a context, psychometric analyses should be performed in substantive studies as well. Until recently, performing comprehensive psychometrics required expert knowledge of different, often proprietary, software. The increasing availability of statistical techniques in the R environment now makes it possible to integrate such analyses in applied research. Methods: In this tutorial, I introduce a 6-step protocol which allows detailed diagnosis of core psychometric properties (e.g. structural validity, internal consistency) for scales with binary and ordinal response options aiming to measure differences in degree or quantity, the most common in applied research. The protocol includes investigations of (1) item distributions and summary statistics, item properties via (2) non-parametric and (3) parametric item response theory, (4) scale structure using factor analysis, (5) reliability via classical test theory, and (6) calculation and description of global scores. I illustrate the procedure on a measure of self-reported disability, the 24-item Sickness Impact Profile Roland Scale (RM-SIP), administered in a survey of 222 chronic pain sufferers. An R Markdown script is provided that generates reproducible reports. Results: In this sample, 15 of 24 RM-SIP items formed a unidimensional ordinal scale with good homogeneity (H = 0.43) and reliability (α = .86[.84–.89]; ω = .87[.85–.88]). The two versions were highly correlated (r = .96), and regression models predicting RM-SIP disability produced comparable results. Conclusions: The example analysis illustrates how psychometric properties may be assessed in substantive studies and identify avenues for measure improvement. Applied researchers can adapt this script to perform and communicate these analyses as part of questionnaire validation and substantive studies.
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spelling pubmed-81335362021-05-25 Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol Dima, Alexandra L. Health Psychol Behav Med Advanced Methods in Health Psychology and Behavioral Medicine Background: Applied health science research commonly measures concepts via multiple-item tools (scales), such as self-reported questionnaires or observation checklists. They are usually validated in more detail in separate psychometric studies or very cursorily in substantive studies. However, methodologists advise that, as validity is a property of the inferences based on measurement in a context, psychometric analyses should be performed in substantive studies as well. Until recently, performing comprehensive psychometrics required expert knowledge of different, often proprietary, software. The increasing availability of statistical techniques in the R environment now makes it possible to integrate such analyses in applied research. Methods: In this tutorial, I introduce a 6-step protocol which allows detailed diagnosis of core psychometric properties (e.g. structural validity, internal consistency) for scales with binary and ordinal response options aiming to measure differences in degree or quantity, the most common in applied research. The protocol includes investigations of (1) item distributions and summary statistics, item properties via (2) non-parametric and (3) parametric item response theory, (4) scale structure using factor analysis, (5) reliability via classical test theory, and (6) calculation and description of global scores. I illustrate the procedure on a measure of self-reported disability, the 24-item Sickness Impact Profile Roland Scale (RM-SIP), administered in a survey of 222 chronic pain sufferers. An R Markdown script is provided that generates reproducible reports. Results: In this sample, 15 of 24 RM-SIP items formed a unidimensional ordinal scale with good homogeneity (H = 0.43) and reliability (α = .86[.84–.89]; ω = .87[.85–.88]). The two versions were highly correlated (r = .96), and regression models predicting RM-SIP disability produced comparable results. Conclusions: The example analysis illustrates how psychometric properties may be assessed in substantive studies and identify avenues for measure improvement. Applied researchers can adapt this script to perform and communicate these analyses as part of questionnaire validation and substantive studies. Routledge 2018-05-10 /pmc/articles/PMC8133536/ /pubmed/34040826 http://dx.doi.org/10.1080/21642850.2018.1472602 Text en © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Advanced Methods in Health Psychology and Behavioral Medicine
Dima, Alexandra L.
Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol
title Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol
title_full Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol
title_fullStr Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol
title_full_unstemmed Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol
title_short Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol
title_sort scale validation in applied health research: tutorial for a 6-step r-based psychometrics protocol
topic Advanced Methods in Health Psychology and Behavioral Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8133536/
https://www.ncbi.nlm.nih.gov/pubmed/34040826
http://dx.doi.org/10.1080/21642850.2018.1472602
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