Objective: The lack of fit of 25 previously published five-factor models for the PANSS items, can be due to the statistics used. The purpose of this study was to use a 'new' statistical method to develop and confirm an improved five-factor model. The improved model is both complex and stable. Complex means that symptoms can have multiple factor loadings, because they have multiple causes, not because they are ill defined. Stable means that the complex structure is found repeatedly in validations.
Methods: A ten-fold cross-validation (10 CV) was applied on a large data set (N = 5769) to achieve an improved factor model for the PANSS items. The advantages of 10 CV are minimal effect of sample characteristics and the ability to investigate the stability of items loading on multiple factors.
Results: The results show that twenty-five items contributed to the same factor all ten validations with one item showing a consistent loading on two factors. Three items were contributing to the same factor nine out of ten validations, and two items were contributing to the same factor six to eight times. The resulting five-factor model covers all thirty items of the PANSS, subdivided in the factors: positive symptoms, negative symptoms, disorganization, excitement, and emotional distress. The five-factor model has a satisfactory goodness-of-fit (Comparative Fit Index = .905; Root Mean Square Error of Approximation = .052).
Conclusions: The five-factor model developed in this study is an improvement above previously published models as it represents a complex factor model and is more stable.