Quality of life in children surviving cancer: A personality and multi-informant perspective [IF: 1.5]

Objective: To describe quality of life (QoL) of children surviving cancer in relation to their personality, using self- and maternal reports and examining differences with healthy referents. Method: Sixty-seven children who survived childhood cancer were compared with eighty-one healthy children on QoL and personality characteristics. Results: Children who survived cancer reported higher QoL than healthy children, whereas there were no differences for personality. Two main effects emerged for informant with children rating themselves as less neurotic and more conscientious than their mothers. The correspondence between mothers and children was substantially higher for survivors for QoL and personality ratings. QoL and trait measures share substantial variance, and personality traits significantly predict QoL. Parental personality ratings explained child QoL beyond children's personality ratings. Conclusions: Personality traits contribute to quality of life, indicating that personality significantly influences child's quality of life beyond the experience of a negative life event such as surviving cancer and its treatment. From a diagnostic perspective, parental trait ratings are informative in addition to children's ratings of personality to understand children's QoL

Psychological Bulletin, 121(2), 219–245.

personality, and interests: Evidence for overlapping traits

General and maladaptive traits in a Five-Factor Framework for DSM-5 in a university student sample

The relationships between two measures proposed to describe personality pathology, that is the Revised NEO Personality Inventory (NEO-PI-3) and the Personality Inventory for DSM-5 (PID-5), are examined in an undergraduate sample (N = 240). The NEO inventories are general trait measures, also considered relevant to assess disordered personality, whereas the PID-5 measure is specifically designed to assess pathological personality traits, as conceptualized in the DSM-5 proposal. A structural analysis of the 25 PID-5 traits confirmed the factor structure observed in the U.S. derivation sample, with higher order factors of Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism. A joint factor analysis of, respectively, the NEO domains and their facets with the PID-5 traits showed that general and maladaptive traits are subsumed under an umbrella of five to six major dimensions that can be interpreted from the perspective of the five-factor model or the Personality Psychopathology Five. Implications for the assessment of personality pathology and the construction of models of psychopathology grounded in personality are discussed

What are agile, flexible, or adaptable employees and students? A typology of dynamic individual differences in applied settings

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this recordThe applied psychology literature has discussed and used a variety of different definitions of dynamic individual differences. Descriptions like dynamic, agile, adaptive, or flexible can refer to a variety of different types of constructs. The present article contributes to the literature by presenting an organizing typology of dynamic constructs. We also conducted a literature review of four major applied journals over the last 15 years to validate the taxonomy and to use it to map what type of dynamic individual differences constructs are typically studied in the applied psychology literature. The typology includes six basic conceptualizations of dynamic individual differences: Variability constructs (inconsistency across situations), skill acquisition constructs (learning new skills), transition constructs (avoiding “loss” in performance after unforeseen change), reacquisition constructs (relearning after change), acceleration/deceleration constructs (losing or gaining energy by displaying the behavior), and integration/dissolution constructs (behavior becomes more or less uniform). We provide both verbal and statistical definitions for each of these constructs, and demonstrate how these conceptualizations can be operationalized in assessment and criterion measurement using R code and simulated data. We also show how researchers can test different dynamic explanations using likelihood-based R² statistics

Replication is more than hitting the lottery twice

The main goal of our target article was to provide concrete recommendations for improving the replicability of research findings. Most of the comments focus on this point. In addition, a few comments were concerned with the distinction between replicability and generalizability and the role of theory in replication. We address all comments within the conceptual structure of the target article, and hope to convince readers that replication in psychological science amounts to much more than hitting the lottery twice

First look at the five-factor model personality facet associations with sensory processing sensitivity

status: publishe

Type D Personality, Temperament, and Mental Health in Military Personnel Awaiting Deployment

Type D personality, temperament, and mental health in military personnel awaitin

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students