Longitudinal Associations Between the Five-Factor Model of Personality and The Bi-Factor Model of Psychopathology: Continuity,Pathoplasty and Complication Effects in Adolescents

The study of the bifactor structure of psychopathology, which includes a general factor of psychopathology (or p factor) in addition to the internalizing and externalizing factors, has gained attention. However, its associations with the Five-Factor Model (FFM) of personality has been addressed in few studies, and none has examined different plausible etiological models (i.e., continuity, pathoplasty, complication) to explain its relationship, which is the aim of the present research. Additionally, the longitudinal association of the General Factor of Personality (GFP) and the p factor will be also explored. Personality and psychopathological symptoms of high school students were assessed at three time points (once a year) (n?=?655; M?=?13.79, SD?=?1.24; 49.8% girls). Confirmatory Factor Analysis (and measurement invariance across waves) were tested for the traits, the GFP and the bifactor model of psychopathology. While the bifactor model and the one-factor solution for each personality trait displayed good fit to the data and remained invariant over time, the structure of the GFP was adequate and invariant in two of the three waves. The resulting factors were included in cross-lagged panel models and showed that the FFM traits and the psychopathology factors influenced each other reciprocally. Most associations fell in line with the continuity model, but minor pathoplastic and complication effects were also reported. Similar associations were found between the GFP and the p factor. These results suggest that interventions in riskier personality profiles might prevent the development of general and more specific psychopathology spectra.

     

The Relative Importance and Interaction of Contextual and Methodological Predictors of Mean rWG for Work Climate

A variety of collective phenomena are understood to exist to the extent that workers agree on their perceptions of the phenomena, such as perceptions of their organization’s climate or perceptions of their team’s mental model. Researchers conducting group-level studies of such phenomena measure individuals’ perceptions via surveys and then aggregate data to the group level if the mean within-group agreement for a sample of groups is sufficiently high. Despite this widespread practice, we know little about the factors potentially affecting mean within-group agreement. Here, focusing on work climate, we report an investigation of a number of expected contextual (social interaction) and methodological predictors of mean r_WG, a common statistic for judging within-group agreement in applied psychology and management research. We used the novel approach of meta-CART, which allowed us to assess the relative importance and possible interactions of the predictor variables. Notably, mean r_WG values are driven by both contextual (average number of individuals per group and cultural individualism-collectivism) and methodological factors (the number of items in a scale and scale reliability). Our findings are largely consistent with expectations concerning how social interaction affects within-group agreement and psychometric arguments regarding why adding more items to a scale will not necessarily increase the magnitude of an index based on a Spearman-Brown “stepped-up correction.” We discuss the key insights from our results, which are relevant to the study of multilevel phenomena relying on the aggregation of individual-level data and informative for how meta-analytic researchers can simultaneously examine multiple moderator variables.

     

Adversariality in Argumentation: Shortcomings of Minimal Adversariality and A Possible Reconstruction

Argumentation - Minimal adversariality consists in the opposition of contradictory conclusions in argumentation, and its usual metaphorical expression as a game between combating arguers has seen...     

Hesitancy to Return to the Pre-pandemic Routine

Journal of Rational-Emotive & Cognitive-Behavior Therapy - The goal of the present study was to explore COVID-19 related hesitancy, which represents the inability of people to return to...     

A Qualitative Study on Children’s Digital Media Use and Parents’ Self-interest

Journal of Child and Family Studies - This qualitative study provides insight into the role of parents’ self-interest in digital media use of children in different age groups. We conducted 31...     

Effectiveness of Specific Techniques in Behavioral Teacher Training for Childhood ADHD Behaviors: Secondary Analyses of a Randomized Controlled Microtrial

Research on Child and Adolescent Psychopathology - Behavioral teacher training is an effective intervention for children with attention-deficit/hyperactivity disorder (ADHD). Intervention...     

Bold and bright: shy and supple? The effect of habitat type on personality–cognition covariance in the Aegean wall lizard (Podarcis erhardii)

Animal Cognition - Animals exhibit considerable and consistent among-individual variation in cognitive abilities, even within a population. Recent studies have attempted to address this variation...     

Michael S. Moore: Mechanical Choices. The Responsibility of the Human Machine

Ethical Theory and Moral Practice -     

Estimation of the predictive power of the model in mixed‐effects meta‐regression: A simulation study

Several methods are available to estimate the total and residual amount of heterogeneity in meta‐analysis, leading to different alternatives when estimating the predictive power in mixed‐effects meta‐regression models using the formula proposed by Raudenbush (1994, 2009). In this paper, a simulation study was conducted to compare the performance of seven estimators of these parameters under various realistic scenarios in psychology and related fields. Our results suggest that the number of studies (k) exerts the most important influence on the accuracy of the results, and that precise estimates of the heterogeneity variances and the model predictive power can only be expected with at least 20 and 40 studies, respectively. Increases in the average within‐study sample size () also improved the results for all estimators. Some differences among the accuracy of the estimators were observed, especially under adverse (small k and ) conditions, while the results for the different methods tended to convergence for more optimal scenarios.