An application of item response mixture modelling to psychosis indicators in two large community samples

M Shevlin, Gary Adamson, Wilma Vollebergh, Ron de Graaf, Jim van Os

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Objective Previous research has suggested that psychosis is better described as a continuum rather than a dichotomous entity. This study aimed to describe the distribution of positive psychosis-like symptoms in two large community samples using an item response mixture model. Methods An item response mixture model was used to explain the pattern of psychosis-like symptom endorsement. This model incorporated two elements. First, a continuous non-normal latent variable to explain the observed pattern of data. Second, a categorical latent variable to explain the variation in the continuous non-normal latent variable. Results For both samples, representing broadly and narrowly defined psychosis, the best fitting model was a four-class solution. In both cases, the classes differed quantitatively rather than qualitatively. Conclusions The analysis showed that psychosis-like symptoms at the population level could be best explained by four classes that appeared to represent an underlying continuum.
Original languageEnglish
Pages (from-to)771-779
JournalSocial Psychiatry and Psychiatric Epidemiology
Volume42
Issue number10
DOIs
Publication statusPublished - Oct 2007

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