P A Kelly1, M A Kallen, M E Suárez-Almazor. 1. Michael E. DeBakey VA Medical Center, and Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA. pakelly@bcm.edu
Abstract
OBJECTIVE: The Multidimensional Health Locus of Control (MHLC) scales are widely used to measure beliefs about determinants of persons' health. We evaluated the scales over the largest-ever disease-specific sample of subjects using a combined-method psychometric approach. STUDY DESIGN AND SETTING: We performed a secondary analysis of data from 1,206 subjects from three osteoarthritis studies, using Rasch analysis and confirmatory factor analysis simultaneously. Differential item functioning (DIF) by gender and data source, scale dimensionality, and item fit were examined. The Rasch model fit the data if Rasch residual principal components analysis (PCA) corroborated three distinct dimensions and item fit statistics fell between 0.80 and 1.20. The confirmatory factor (CFA) model fit the data if factor loadings exceeded 0.50 for all items. RESULTS: DIF by gender or data source was not materially evident for any items. PCA supported existence of three dimensions in the data. Both Rasch and CFA models fit the data for 16 items; two items were detected as misperforming. When these items were removed, fit of both models improved. CONCLUSION: Results of this large-sample evaluation of the MHLC scales corroborated earlier findings that removal of certain items improves the scales. The combined Rasch-CFA approach provided better insight to scale performance problems than either method alone provided.
OBJECTIVE: The Multidimensional Health Locus of Control (MHLC) scales are widely used to measure beliefs about determinants of persons' health. We evaluated the scales over the largest-ever disease-specific sample of subjects using a combined-method psychometric approach. STUDY DESIGN AND SETTING: We performed a secondary analysis of data from 1,206 subjects from three osteoarthritis studies, using Rasch analysis and confirmatory factor analysis simultaneously. Differential item functioning (DIF) by gender and data source, scale dimensionality, and item fit were examined. The Rasch model fit the data if Rasch residual principal components analysis (PCA) corroborated three distinct dimensions and item fit statistics fell between 0.80 and 1.20. The confirmatory factor (CFA) model fit the data if factor loadings exceeded 0.50 for all items. RESULTS: DIF by gender or data source was not materially evident for any items. PCA supported existence of three dimensions in the data. Both Rasch and CFA models fit the data for 16 items; two items were detected as misperforming. When these items were removed, fit of both models improved. CONCLUSION: Results of this large-sample evaluation of the MHLC scales corroborated earlier findings that removal of certain items improves the scales. The combined Rasch-CFA approach provided better insight to scale performance problems than either method alone provided.
Authors: Donna M Zulman; Matthew L Maciejewski; Janet M Grubber; Hollis J Weidenbacher; Dan V Blalock; Leah L Zullig; Liberty Greene; Heather E Whitson; Susan N Hastings; Valerie A Smith Journal: JAMA Netw Open Date: 2020-10-01