Factor mixture modelling analysis
This is often useful in reducing the number of classes into more meaningful subgroups, especially if the classes are modelling differences in severity. The FMMA results presented in Table 5 should be interpreted in the light of our theoretical model of paranoia, specifically that the items are related non-reflexively, with the more extreme paranoia items being associated with a greater overall severity, as indicated by the item count. Based on the goodness-of-fit indices, two models stand out (shown in bold in Table 5): both were one-factor models with four latent classes. Following the notation of Clark et al,29 the best-fitting model in terms of the Bayesian information criterion was the four-class variant of FMM-3 (FMM-3, 4C). This model proposes that: (1) people in the survey can be categorised into four groups (or classes): the people in each class experience a similar type of paranoid ideation, distinct from that experienced by people in the other classes; and (2) underlying each class, there is a single dimension of ‘paranoia’, which is conceptualised identically in each class (as indicated by the invariant factor loadings; range of standardised loadings 0.394-0.850). In other words, the level of paranoia (’severity’) is the same in each class (as indicated by the invariant factor variance). On both theoretical and empirical grounds, this assumption is, however, implausible - people in the community with different types of paranoid experiences will vary in terms of the severity of those experiences.