{'fraction_non_alphanumeric': 0.025477707006369428, 'fraction_numerical': 0.009264620729588882, 'mean_word_length': 5.620689655172414, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mixture modelling of DSM-IV-TR paranoid personality disorder criteria in a general population sample

Complications in the research into personality disorders may be rooted in the assumption within psychiatric diagnosis that underlying constructs are measured with equally valid observed items without rank or recognition of measurement error. The aim of this paper is to investigate the internal validity of DSM-IV (APA, 2000) paranoid personality disorder while accounting for measurement error and the continuous and categorical nature of the construct. General population data from the British Psychiatric Morbidity Survey (Singleton et al., 2001) was obtained from the Data Archives, University of Essex, England. Information from individuals with responses in the paranoid personality disorder section (n = 8393) of the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II; First et al., 1997) screening questionnaire was analysed using confirmatory factor analysis (CFA), item response theory (IRT), latent class analysis (LCA) and latent class factor analysis (LCFA) mixture modelling. Results indicated that a one-factor model adequately represented the data, and that all items had reasonable factor loadings. However IRT analysis indicated that only four of the seven criteria discriminate well between individuals along different points of the underlying continuum. LCA and LCFA provided another perspective on the evaluation of paranoid personality disorder and indicated the presence of four underlying sub-populations. This is useful in terms of clinical and primary health settings as specific groups of interest can be investigated further in terms of characteristics, covariates and predictors.