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

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. METHOD: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.

authors

Shevlin M,Adamson G,Vollebergh W,de Graaf R,van Os J

doi

10.1007/s00127-007-0244-6

subject

Has Abstract

pub_date

2007-10-01 00:00:00

pages

771-9

issue

10

eissn

0933-7954

issn

1433-9285

journal_volume

42

pub_type

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