Mark van der Gaag

The five-factor model of the Positive and Negative Syndrome Scale II: A ten-fold cross-validation of a revised model

OBJECTIVEnThe 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.nnnMETHODSnA 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.nnnRESULTSnThe 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).nnnCONCLUSIONSnThe 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.

The five-factor model of the Positive and Negative Syndrome Scale I: confirmatory factor analysis fails to confirm 25 published five-factor solutions

OBJECTIVEnThe aim of this study was to test the goodness-of-fit of all previously published five-factor models of the Positive and Negative Syndrome Scale (PANSS).nnnMETHODSnWe used confirmatory factor analysis (CFA) with a large data set (N = 5769).nnnRESULTSnThe different subsamples were tested for heterogeneity and were found to be homogeneous. This indicates that despite variability in age, sex, duration of illness, admission status, etc., in the different subsamples, the structure of symptoms is the same for all patients with schizophrenia. Although previous research has shown that a five-factor model fits the data better than models with three or four factors, no satisfactory fit for any of the 25 published five-factor models was found with CFA.nnnCONCLUSIONSnVariability in age, sex, admission status and duration of illness has no substantial effect on the structure of symptoms in schizophrenia. The lack of fit can be caused by ill-defined items that aim to measure several properties in a single rating. Another explanation is that well-defined symptoms can have two or more causes. Then a double or triple loading item should not be discarded, but included because the complexity of symptoms in schizophrenia is represented by these multiple loadings. Such a complex model not only needs confirmation by CFA, but also has to be proven stable. A 10-fold cross-validation is suggested to develop a complex and stable model.