How to evaluate local fit (residuals) in large structural equation models.

Consistent with reporting standards for structural equation modelling (SEM), model fit should be evaluated at two different levels, global and local. Global fit concerns the overall or average correspondence between the entire data matrix and the model, given the parameter estimates for the model. Local fit is evaluated at the level of the residuals, or differences between observed and predicted associations for every pair of measured variables in the model. It can happen that models with apparently satisfactory global fit can nevertheless have problematic local fit. This may be especially true for relatively large models with many variables, where serious misspecification is indicated by some larger residuals, but their contribution to global fit is diluted when averaged together with all the other smaller residuals. It can be challenging to evaluate local fit in large models with dozens or even hundreds of variables and corresponding residuals. Thus, the main goal of this tutorial is to offer suggestions about how to efficiently evaluate and describe local fit for large structural equation models. An empirical example is described where all data, syntax and output files are freely available to readers.

Cognitive capacity similarly predicts insight into symptoms in first- and multiple-episode psychosis.

BACKGROUND: Lack of insight is a frequent characteristic of psychotic disorders, both in patients who recently experienced a first episode of psychosis (FEP) and those who experience recurrent multiple episodes (MEP). Insight is a multifaceted construct: its clinical form notably includes the unawareness of being ill, of symptoms, and of the need for treatment. Cognitive capacity is among the key determinants of insight into symptoms, but less is known about whether stage of illness (FEP vs. MEP) moderates this association. METHODS: Our aim is to evaluate the association between cognitive capacity and symptom unawareness using structural equation modeling and moderated multiple regression. A total of 193 FEP and MEP patients were assessed using the CogState battery and the Scale to Assess Unawareness of Mental Disorder. RESULTS: Analyses suggest that cognitive capacity accounts for a relatively small proportion of the total variation in symptom unawareness (6.4%). There was no evidence to suggest a moderating effect of stage of illness on this association. CONCLUSIONS: The effect of general cognitive capacity on symptom unawareness is relatively small, and this basic relation was unrelated to stage of illness. It is possible that stage of illness could moderate this association only for certain facets of insight not assessed in this study (e.g., unawareness of the need for treatment).

Intent of reporting standards: Reply to Rossiter (2018).

In this reply to Rossiter (2018), we note that the goal of developing Journal Article Reporting Standards has been to specify the kinds of information that should be provided to the readers of scientific articles in order to allow maximal understanding of the work being reported-in the case of psychometrics, information that demonstrates the underlying adequacy of the measures used in the research being reported. Although we illustrate some kinds of items that might be utilized to make these demonstrations, the illustrations are not proscriptive. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report.

Following a review of extant reporting standards for scientific publication, and reviewing 10 years of experience since publication of the first set of reporting standards by the American Psychological Association (APA; APA Publications and Communications Board Working Group on Journal Article Reporting Standards, 2008), the APA Working Group on Quantitative Research Reporting Standards recommended some modifications to the original standards. Examples of modifications include division of hypotheses, analyses, and conclusions into 3 groupings (primary, secondary, and exploratory) and some changes to the section on meta-analysis. Several new modules are included that report standards for observational studies, clinical trials, longitudinal studies, replication studies, and N-of-1 studies. In addition, standards for analytic methods with unique characteristics and output (structural equation modeling and Bayesian analysis) are included. These proposals were accepted by the Publications and Communications Board of APA and supersede the standards included in the 6th edition of the Publication Manual of the American Psychological Association (APA, 2010). (PsycINFO Database Record