New Avenue for the Geriatric Depression Scale: Rasch Transformation Enhances Reliability of Assessment
Merkin, AG; Medvedev, ON; Sachdev, PS; Tippett, L; Krishnamurthi, R; Mahon, S; Kasabov, N; Parmar, P; Crawford, J; Doborjeh, ZG; Doborjeh, MG; Kang, K; Kochan, NA; Bahrami, H; Brodaty, H; Feigin, VL
One or more files are currently not publicly available.
MetadataShow full metadata
Background 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.