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

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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.

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Correspondence to Dr. Mark Shevlin.

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Shevlin, M., Adamson, G., Vollebergh, W. et al. An application of item response mixture modelling to psychosis indicators in two large community samples. Soc Psychiat Epidemiol 42, 771–779 (2007). https://doi.org/10.1007/s00127-007-0244-6

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Key words

  • psychosis
  • IRT
  • latent class analysis
  • hybrid model