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. 2018 Dec:270:869-879.
doi: 10.1016/j.psychres.2018.10.046. Epub 2018 Oct 30.

The component structure of the scales for the assessment of positive and negative symptoms in first-episode psychosis and its dependence on variations in analytic methods

Marc S Tibber  1 , James B Kirkbride  2 , Eileen M Joyce  3 , Stanley Mutsatsa  4 , Isobel Harrison  5 , Thomas R E Barnes  6 , Vyv Huddy  7
Affiliations

The component structure of the scales for the assessment of positive and negative symptoms in first-episode psychosis and its dependence on variations in analytic methods

Marc S Tibber et al. Psychiatry Res. 2018 Dec.

Abstract

A secondary analysis was undertaken on Scales for the Assessment of Positive and Negative Symptoms (SAPS/SANS) data from 345 first-episode psychosis (FEP) patients gathered in the West London FEP study. The purpose of this study was to determine: (i) the component structure of these measures in FEP (primary analyses), and (ii) the dependence of any findings in these primary analyses on variations in analytic methods. Symptom ratings were exposed to data reduction methods and the effects of the following manipulations ascertained: (i) level of analysis (individual symptom vs. global symptom severity ratings), (ii) extraction method (principal component vs. exploratory factor analysis) and (iii) retention method (scree test vs. Kaiser criterion). Whilst global ratings level analysis rendered the classic triad of psychotic syndromes (positive, negative and disorganisation), symptom level analyses revealed a hierarchical structure, with 11 first-order components subsumed by three second-order components, which also mapped on to this syndrome triad. These results were robust across data reduction but not component retention methods, suggesting that discrepancies in the literature regarding the component structure of the SAPS/SANS partly reflect the level of analysis and component retention method used. Further, they support a hierarchical symptom model, the implications of which are discussed.

Keywords: Disorganisation symptoms; Factor analysis; Factor structure; Negative symptoms; Positive symptoms; Psychosis.

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Figures

Fig. 1.
Fig. 1
Scree plot for components identified in the global ratings level principal component analysis. Component numbers are plotted against the scaled eigenvalues for each eigenvector (eigenvalue divided by the total number of components). The dotted line represents the eigenvector > 1 line (i.e. 1 divided by the total number of components).
Fig. 2.
Fig. 2
Scree plot for components identified in the first-order individual symptom level principal component analysis. Component numbers are plotted against the scaled eigenvalues for each eigenvector (eigenvalue divided by the total number of components). The dotted line represents the eigenvector > 1 line (i.e. 1 divided by the total number of components).
Fig. 3.
Fig. 3
Scree plot for components identified in the second-order symptom level principal component analysis. Component numbers are plotted against the scaled eigenvalues for each eigenvector (eigenvalue divided by the total number of components). The dotted line represents the eigenvector > 1 line (i.e. 1 divided by the total number of components).
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