Investigating the structure of Schizotypy through the “Multidimensional Schizotypy Scale” and “Oxford-Liverpool Inventory”: an Exploratory network analysis approach in the healthy population

Abstract

AbstractBackgroundSchizophrenia spectrum disorders and schizotypy share traits across positive, negative, and disorganized domains. Instruments like MSS and O-LIFE provide insights into these dimensions. Despite challenges posed by cultural variations and measurement methodologies, these instruments offer nuanced perspectives on the spectrum of schizotypy, spanning from its expression as a personality trait to its potential implications in clinical settings.MethodsThrough an Exploratory Graph Analysis (EGA) applied to a sample of 1059 healthy subjects, we compare the resulting networks with the original factorial structure of the two questionnaires and explore how each one conceptualizes schizotypy. Comparing both models quantitatively and qualitatively these models, we seek to elucidate the unique insights provided by each instrument regarding the spectrum of schizotypy as a personality trait.ResultsEGA analyses unveiled a three-dimensional structure plus an additional one consisting of items related to the concept of “Disconnection”. Confirmatory factor analysis showed this four-dimensional model outperformed others. High reliability and strong factor saturation were observed for all four factors.O-LIFE’s EGA revealed a complex structure, refined to four factors removing items with low fitting indices from the scale. Confirmatory analysis validated a final model with robust reliability and well-defined factor structures across the new “Cognitive and Behavioural Disorganization”, “Introversion”, “Unusual Experiences”, and “Environmental Pressure” factors.ConclusionsThis study improves the understanding of schizotypy by proposing new MSS and O-LIFE factor structures via EGA. Both scales uniquely contribute to schizotypy assessment in healthy populations, warranting further research to validate and refine these new domains across diverse populations.