Structural models of psychopathology provide dimensional alternatives to traditional categorical classification systems. Competing models, such as the bifactor and correlated factors models, are typically compared via statistical indices to assess how well each model fits the same data. However, simulation studies have found evidence for probifactor fit index bias in several psychological research domains. The present study sought to extend this research to models of psychopathology, wherein the bifactor model has received much attention, but its susceptibility to bias is not well characterized. We used Monte Carlo simulations to examine how various model misspecifications produced fit index bias for 2 commonly used estimators, WLSMV and MLR. We simulated binary indicators to represent psychiatric diagnoses and positively skewed continuous indicators to represent symptom counts. Across combinations of estimators, indicator distributions, and misspecifications, complex patterns of bias emerged, with fit indices more often than not failing to correctly identify the correlated factors model as the data-generating model. No fit index emerged as reliably unbiased across all misspecification scenarios. Although, tests of model equivalence indicated that in one instance fit indices were not biased-they favored the bifactor model, albeit not unfairly. Overall, results suggest that comparisons of bifactor models to alternatives using fit indices may be misleading and call into question the evidentiary meaning of previous studies that identified the bifactor model as superior based on fit. We highlight the importance of comparing models based on substantive interpretability and their utility for addressing study aims, the methodological significance of model equivalence, as well as the need for implementation of statistical metrics that evaluate model quality. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In this article, we describe the collaborative process that is underway to develop measures for the Hierarchical Taxonomy of Psychopathology (HiTOP). The HiTOP model has generated much interest in the psychiatric literature in recent years, but research applications and clinical translation of the model require measures that are specifically keyed to the model. To that end, the Measures Development Workgroup of HiTOP has been engaged in a collaborative effort to develop both questionnaire and interview methods that (a) are specifically tied to the elements of the HiTOP structure, and (b) provide one means of testing that structure. The work has been divided among five subgroups that are focused on specific HiTOP spectra. Our scale development methods are rooted in the principles of construct valid scale development. This report describes Phase 1 of this project, summarizes the methods and results thus far, and discusses the interplay between measurement and HiTOP model revisions. Finally, we discuss future phases of the scale development and the steps we are taking to improve clinical utility of the final measures.

Anomalous self-experiences (ASEs) are among the first symptoms to appear in the prodrome, predict the development of psychosis over and above clinical symptoms, and are common in people with schizophrenia. Although there are well-validated phenomenological interviews for assessing ASEs, there are no self-report measures. The current research describes 4 studies designed to develop and validate a new scale to assess ASEs: the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE). In Study 1, an overinclusive item pool was generated based on phenomenological descriptions of ASEs, and items were kept or discarded based on factor loadings in an exploratory factor analysis. Five factors were extracted including disturbances in Cognition, Consciousness, Self-Awareness and Presence, Somatization, and Transitivism/Demarcation. The 5-factor structure was confirmed in Study 2, and the scale showed measurement invariance between sexes. IPASE scores were correlated with self-report and task measures of self-processing including self-concept clarity, self-consciousness, and self-esteem as well as measures of psychotic-like experiences. In Study 3, people with positive schizotypy had higher IPASE scores than a negative schizotypy and comparison group. In Study 4, people with schizophrenia had higher IPASE scores than healthy controls. Overall, the IPASE displayed strong psychometric qualities and is a brief alternative to resource-intensive phenomenological interviews in clinical, at-risk, and general population samples. (PsycINFO Database Record

The associations among normal personality and many mental disorders are well established, but it remains unclear whether and how symptoms of schizophrenia and schizotypal traits align with the personality taxonomy. This study examined the joint factor structure of normal personality, schizotypy, and schizophrenia symptoms in people with psychotic disorders (n = 288) and never-psychotic adults (n = 257) in the Suffolk County Mental Health Project. First, we evaluated the structure of schizotypal (positive schizotypy, negative schizotypy, and mistrust) and normal traits. In both the psychotic-disorder and never-psychotic groups, the best-fitting model had 5 factors: neuroticism, extraversion, conscientiousness, agreeableness, and psychoticism. The schizotypy traits were placed on different dimensions: negative schizotypy went on (low) extraversion, whereas positive schizotypy and mistrust went on psychoticism. Next, we added symptoms to the model. Numerous alternatives were compared, and the 5-factor model remained best-fitting. Reality distortion (hallucinations and delusions) and disorganization symptoms were placed on psychoticism, and negative symptoms were placed on extraversion. Models that separated symptom dimensions from trait dimensions did not fit well, arguing that taxonomies of symptoms and traits are aligned. This is the first study to show that symptoms of psychosis, schizotypy, and normal personality reflect the same underlying dimensions. Specifically, (low) extraversion, negative schizotypy, and negative symptoms form one spectrum, whereas psychoticism, positive schizotypy, and positive and disorganized symptoms form another. This framework helps to understand the heterogeneity of psychosis and comorbidity patterns found in psychotic disorders. It also underscores the importance of traits to understanding these disorders.