Search

Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic. [ABSTRACT FROM AUTHOR]

As the majority of the global population resides in cities, it is imperative to understand urban well-being. While cities offer concentrated social and economic opportunities, the question arises whether these benefits translate to equitable levels of satisfaction in these domains. Using a robust and objective measure of urbanicity on a sample of 156,000 U.K. residents aged 40 and up, we find that urban living is associated with lower scores across seven dimensions of well-being, social satisfaction, and economic satisfaction. In addition, these scores exhibit greater variability within urban areas, revealing increased inequality. Last, we identify optimal distances in the hinterlands of cities with the highest satisfaction and the least variation. Our findings raise concern for the psychological well-being of urban residents and show the importance of nonlinear methods in urban research. [ABSTRACT FROM AUTHOR]

Testing the equality of two proportions is a common procedure in science, especially in medicine and public health. In these domains, it is crucial to be able to quantify evidence for the absence of a treatment effect. Bayesian hypothesis testing by means of the Bayes factor provides one avenue to do so, requiring the specification of prior distributions for parameters. The most popular analysis approach views the comparison of proportions from a contingency table perspective, assigning prior distributions directly to the two proportions. Another, less popular approach views the problem from a logistic regression perspective, assigning prior distributions to logit‐transformed parameters. Reanalyzing 39 null results from the New England Journal of Medicine with both approaches, we find that they can lead to markedly different conclusions, especially when the observed proportions are at the extremes (ie, very low or very high). We explain these stark differences and provide recommendations for researchers interested in testing the equality of two proportions and users of Bayes factors more generally. The test that assigns prior distributions to logit‐transformed parameters creates prior dependence between the two proportions and yields weaker evidence when the observations are at the extremes. When comparing two proportions, we argue that this test should become the new default. [ABSTRACT FROM AUTHOR]

Background and Hypothesis: Psychotic disorders (PDs) have huge personal and societal impact, and efforts to improve outcomes in patients are continuously needed. Environmental risk factors (ERFs), especially modifiable risk factors, are important to study because they pose a target for intervention and prevention. No studies have investigated ERFs, cognition, and psychotic symptoms together in a network approach., Study Design: We explored interactions between 3 important ERFs (tobacco smoking, cannabis use, and childhood trauma), 6 cognitive domains, and 3 dimensions of symptoms in psychosis. From the Genetic Risk and Outcome of Psychosis (GROUP) cohort, we used data from patients, siblings, and healthy controls to construct networks using Bayesian analyses of all 12 variables. We constructed networks of the combined sample and of patients and siblings separately., Study Results: We found that tobacco smoking was directly associated with cognition and psychotic symptoms. The cognitive variable processing speed was the most central node, connecting clusters of psychotic symptoms and substance use through the variables of positive symptoms and tobacco smoking. Comparing the networks of patients and siblings, we found that networks were relatively similar between patients and siblings., Conclusions: Our results support a potential central role of processing speed deficits in PDs. Findings highlight the importance of integrating tobacco smoking as potential ERFs in the context of PDs and to broaden the perspective from cannabis discontinuation to smoking cessation programs in patients or people at risk of PDs., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.)

Central to many influential theories in the occupational health and stress literature is that job resources reduce the negative effects of job demands on workers' well-being. However, empirical investigations testing this supposition have produced inconsistent findings. This study evaluates the interaction between job demands and job control on workers' well-being through a systematic literature search and using a Bayesian meta-analytic approach. Both aggregated study findings and raw participant-level data were included in the study, resulting in 104 effect sizes of aggregate-level data and 14 participant-level data sets. Overall, the data provided strong evidence for the absence of an interaction between job demands and job control. Longitudinal and nonlinear research designs were also examined but did not alter this overall conclusion. Contrary to the postulations of widespread theories, job control does not reduce the negative impact of job demands on workers' well-being. Alternative theoretical approaches and the need for more consistent and rigorous research standards, like open science practices, are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).