title: Why sample selection matters in exploratory factor analysis: implications for the 12-item World Health Organization Disability Assessment Schedule 2.0.
abstract: Sample selection can substantially affect the solutions generated using exploratory factor analysis. Validation studies of the 12-item World Health Organization (WHO) Disability Assessment Schedule 2.0 (WHODAS 2.0) have generally involved samples in which substantial proportions of people had no, or minimal, disability. With the WHODAS 2.0 oriented towards measuring disability across six life domains (cognition, mobility, self-care, getting along, life activities, and participation in society), performing factor analysis with samples of people with disability may be more appropriate. We determined the influence of the sampling strategy on (a) the number of factors extracted and (b) the factor structure of the WHODAS 2.0.
  
  
  
  Using data from adults aged 50+ from the six countries in Wave 1 of the WHO's longitudinal Study on global AGEing and adult health (SAGE), we repeatedly selected samples (n = 750) using two strategies: (1) simple random sampling that reproduced nationally representative distributions of WHODAS 2.0 summary scores for each country (i.e., positively skewed distributions with many zero scores indicating the absence of disability), and (2) stratified random sampling with weights designed to obtain approximately symmetric distributions of summary scores for each country (i.e. predominantly including people with varying degrees of disability).
  
  
  
  Samples with skewed distributions typically produced one-factor solutions, except for the two countries with the lowest percentages of zero scores, in which the majority of samples produced two factors. Samples with approximately symmetric distributions, generally produced two- or three-factor solutions. In the two-factor solutions, the getting along domain items loaded on one factor (commonly with a cognition domain item), with remaining items loading on a second factor. In the three-factor solutions, the getting along and self-care domain items loaded separately on two factors and three other domains (mobility, life activities, and participation in society) on the third factor; the cognition domain items did not load together on any factor.
  
  
  
  High percentages of participants with no disability (i.e., zero scores) produce heavily censored data (i.e., floor effects), limiting data heterogeneity and reducing the numbers of factors retained. The WHODAS 2.0 appears to have multiple closely-related factors. Samples of convenience and those collected for other purposes (e.g., general population surveys) would usually be inadequate for validating measures using exploratory factor analysis.
authors.affiliation: Biostatistics Unit, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia. Cadeyrn.Gaskin@deakin.edu.au.
authors.forename: Cadeyrn J
authors.initials: CJ
authors.lastname: Gaskin
authors.affiliation: Ingram School of Nursing, Faculty of Medicine, McGill University, Montreal, QC, Canada.|St. Mary's Research Centre, Montreal, QC, Canada.
authors.forename: Sylvie D
authors.initials: SD
authors.lastname: Lambert
authors.affiliation: Biostatistics Unit, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia.
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Bowe
authors.affiliation: Biostatistics Unit, Faculty of Health, Deakin University, Locked Bag 20001, Geelong, VIC, 3220, Australia.
authors.forename: Liliana
authors.identifier: 0000-0003-3736-4337
authors.initials: L
authors.lastname: Orellana
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D004185:Disability Evaluation; D006233:Persons with Disabilities / Q000706:statistics & numerical data*; D005163:Factor Analysis, Statistical; D005260:Female; D006306:Health Surveys / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011594:Psychometrics / Q000706:statistics & numerical data*; D011788:Quality of Life; D015983:Selection Bias; D012648:Self Care / Q000706:statistics & numerical data; D014944:World Health Organization
keywords: Disability; Eligibility criteria; Exploratory factor analysis; ICF; Instrument validation; Psychometric assessment; Sample selection; WHODAS 2.0