New Avenue for the Geriatric Depression Scale: Rasch Transformation Enhances Reliability of Assessment

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aut.filerelease.date2020-11-22
aut.relation.journalJournal of Affective Disordersen_NZ
aut.researcherFeigin, Valery
dc.contributor.authorMerkin, AGen_NZ
dc.contributor.authorMedvedev, ONen_NZ
dc.contributor.authorSachdev, PSen_NZ
dc.contributor.authorTippett, Len_NZ
dc.contributor.authorKrishnamurthi, Ren_NZ
dc.contributor.authorMahon, Sen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.authorParmar, Pen_NZ
dc.contributor.authorCrawford, Jen_NZ
dc.contributor.authorDoborjeh, ZGen_NZ
dc.contributor.authorDoborjeh, MGen_NZ
dc.contributor.authorKang, Ken_NZ
dc.contributor.authorKochan, NAen_NZ
dc.contributor.authorBahrami, Hen_NZ
dc.contributor.authorBrodaty, Hen_NZ
dc.contributor.authorFeigin, VLen_NZ
dc.date.accessioned2019-12-01T23:06:28Z
dc.date.available2019-12-01T23:06:28Z
dc.date.copyright2019en_NZ
dc.date.issued2019en_NZ
dc.description.abstractBackground Depression is a common problem in older adults. The 15-item Geriatric Depression Scale (GDS-15) is a widely used psychometric tool for measuring depression in the elderly, but its psychometric properties have not been yet rigorously investigated. The aim was to evaluate psychometric properties of the GDS-15 and improve precision of the instrument by applying Rasch analysis and deriving conversion tables for transformation of raw scores into interval level data. Methods The data was extracted from the prospective cohort Sydney Memory and Ageing Study of initially not demented individuals aged 70 years and older. The GDS-15 items scores of 212 participants (47.2% males) were analysed using the dichotomous Rasch model. Results Initially poor reliability of the GDS-15, Person Separation Index (PSI)=0.68, was improved by combining locally dependent items into seven super-items. These modifications improved reliability of the GDS-15 (PSI=0.78) and resulted in the best Rasch model fit (χ2(28)=37.72, p=0.104), strict unidimensionality and scale invariance across personal factors such as gender, diagnostic and language background. Limitations Presence of participants with cognitive impairment may be a potential limitation. Conclusions Reliability and psychometric characteristics of the GDS-15 were improved by minor modifications and now satisfy expectations of the unidimensional Rasch model. By using Rasch transformation tables published here psychiatrists, psychologists and researchers can transform GDS raw scores into interval-level data, which improves reliability of the GDS-15 without the need to modify its original response format. These findings increase accuracy of clinical psychometric assessments, leading to more precise diagnosis of depression in the elderly.
dc.identifier.citationJournal of Affective Disorders (2019), doi: https://doi.org/10.1016/j.jad.2019.11.100
dc.identifier.doi10.1016/j.jad.2019.11.100en_NZ
dc.identifier.issn0165-0327en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13055
dc.languageenen_NZ
dc.publisherElsevier
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0165032719305981?via%3Dihub
dc.rightsCopyright © 2019 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectDepression; Older Adults; Geriatric Depression Scale; GDS; Rasch analysis; Psychometrics
dc.titleNew Avenue for the Geriatric Depression Scale: Rasch Transformation Enhances Reliability of Assessmenten_NZ
dc.typeJournal Article
pubs.elements-id366249
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Health & Environmental Science
pubs.organisational-data/AUT/Health & Environmental Science/Public Health & Psych Studies
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences
pubs.organisational-data/AUT/PBRF/PBRF Health and Environmental Sciences/HY Public Health & Psychosocial Studies 2018 PBRF
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