Subscale structure for the Positive and Negative Syndrome Scale (PANSS): A proposed solution focused on clinical validity

• Type of Material: Article
• Authors: Mary E. Kelley, Emory University; Leonard White, New York State Office of Mental Health; Michael T. Compton, The George Washington University; Philip D. Harvey, University of Miami
• Language: English
• Date: 2013-01-30
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2012 Elsevier Ireland Ltd. All rights reserved.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1016/j.psychres.2012.08.019
• Title of Journal or Parent Work: Psychiatry Research
• ISSN: 0165-1781
• Volume: 205
• Issue: 1-2
• Start Page: 137
• End Page: 142
• Grant/Funding Information: The study was funded in part by NIMH Grants 78775 and 63116 to Dr. Harvey, and grant R01 MH081011 to Dr. Compton.
• Keywords: confirmatory factor analysis (CFA); Positive and Negative Syndrome Scale (PANSS); schizophrenia; psychotic symptoms; latent variables
• Research Categories: Psychology, Behavioral

Abstract

Although the items of the positive and negative syndrome scale (PANSS) are ordinal, continuous data methods are consistently used to analyze them. The current study addresses this issue by applying a categorical method and critically examining the ideas of item inclusion and goodness of fit. Data from 1527 subjects was used to test a proposed solution to the factor structure of the PANSS using a categorical factor analytic method. The model was made more generalizable by setting a minimum level of association between the item and the factor, and the results were then compared to existing solutions. The model was also tested for consistency in a first-episode sample. Use of categorical methods indicated similar results to previous analyses; however, it is demonstrated that the strength of the estimates can be unstable when items are shared across factors. The current study demonstrates that solutions can change substantially when a model is over-fitted, and therefore use of measures of fit as the criterion for an acceptable model can mask important relationships and decrease clinical validity.