Psychometric properties and a latent class analysis of the 12‐item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) in a pooled dataset of community samples. Issue 4 (15th September 2016)

• Record Type: Journal Article
• Authors: MacLeod, Melissa A.; Tremblay, Paul F.; Graham, Kathryn; Bernards, Sharon; Rehm, Jürgen; Wells, Samantha
• Is Part Of: International journal of methods in psychiatric research. Volume 25:Issue 4(2016:Dec.)
• Page Start: 243
• Page End: 254
• Publication Date: 2016-09-15
• Subjects: confirmatory factor analysis (CFA) -- disability -- latent class analysis (LCA) -- WHODAS; Psychiatry -- Research -- Methodology -- Periodicals; Psychiatry -- Periodicals; 616.890072
• DOI: 10.1002/mpr.1523
• Languages: English
• ISSNs: 1049-8931

Abstract

Abstract: The 12‐item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a brief measurement tool used cross‐culturally to capture the multi‐dimensional nature of disablement through six domains, including: understanding and interacting with the world; moving and getting around; self‐care; getting on with people; life activities; and participation in society. Previous psychometric research supports that the WHODAS 2.0 functions as a general factor of disablement. In a pooled dataset from community samples of adults ( N = 447) we used confirmatory factor analysis to confirm a one‐factor structure. Latent class analysis was used to identify subgroups of individuals based on their patterns of responses. We identified four distinct classes, or patterns of disablement: (1) pervasive disability; (2) physical disability; (3) emotional, cognitive, or interpersonal disability; (4) no/low disability. Convergent validity of the latent class subgroups was found with respect to socio‐demographic characteristics, number of days affected by disabilities, stress, mental health, and substance use. These classes offer a simple and meaningful way to classify people with disabilities based on the 12‐item WHODAS 2.0. Focusing on individuals with a high probability of being in the first three classes may help guide interventions.