Open AccessMultilevel ordinal factor analysis of the Positive and Negative Syndrome Scale (PANSS)
Author(s) -
Stochl Jan,
Jones Peter B.,
Plaistow James,
Reininghaus Ulrich,
Priebe Stefan,
Perez Jesus,
Croudace Tim J.
Publication year - 2014
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.1429
Subject(s) - positive and negative syndrome scale , psychology , confirmatory factor analysis , psychopathology , exploratory factor analysis , clinical psychology , schizophrenia (object oriented programming) , scale (ratio) , sample (material) , multilevel model , psychometrics , psychiatry , structural equation modeling , psychosis , computer science , machine learning , physics , quantum mechanics , chemistry , chromatography
Clinical assessments of the presence and severity of psychopathology are often collected by health care professionals in mental health services or clinical researchers trained to use semi‐structured interviews. Clustering by interviewer or rater needs to be considered when performing psychometric analyses such as factor analysis or item response modelling as non‐independence of observations arises in these situations. We apply more suitable multilevel methods to analyse ordinally scored Positive and Negative Syndrome Scale (PANSS) items. Our aim is to highlight the differences in results that occur when the data are analysed using a hierarchically sensitive approach rather than using a traditional (aggregated) analysis. Our sample ( n = 507) consisted of patients diagnosed with schizophrenia who participated in a multi‐centre randomized control clinical trial, the DIALOG study. Analyses reported and compared include an exploratory factor analysis as well as several recently published multifactor models re‐estimated within a confirmatory analysis framework. Our results show that the fit of the model and the parsimony of the exploratory factor analysis (EFA) models indicated by the number of factors necessary to explain the inter‐correlation among PANSS items improved significantly when data clustering is taken into account through multilevel analysis. Our modeling results support the pentagonal PANSS model first proposed by White et al . (1997). Copyright © 2014 John Wiley & Sons, Ltd .