Relevant Restricted Quantification

The paper reviews a number of approaches for handling restricted quantification in relevant logic, and proposes a novel one. This proceeds by introducing a novel kind of enthymematic conditional.     

The Effect of Information Quantity on Distinctive Accuracy and Normativity of Personality Trait Judgments

Information quantity is an important moderator of personality judgment accuracy. Some evidence suggests that the amount of available information is positively related to accuracy. The current study utilized the social accuracy model to investigate the effects of differences in thin slices of information quantity on the distinctive accuracy and normativity of personality trait judgments. It was hypothesized that distinctive accuracy and normativity would increase as information quantity increased. Participants were 431 individuals who participated in an online study that varied the length of stimulus target observations (30 seconds, 1 minute, 3 minutes, and 5 minutes), after which judges rated targets using other‐report measures of the Big Five personality traits. For all traits combined, significant levels of accuracy were found for all observation lengths, but distinctive accuracy and normativity did not increase as video length increased. Findings varied for individual traits. For distinctive accuracy, there was a linear increase with information quantity for Extraversion and a non‐linear relationship for Conscientiousness, while there was a linear decrease for Openness. For normativity, there was a linear increase with information quantity for Agreeableness and a non‐linear relationship for Conscientiousness. There are important differences in how observation length affects distinctive accuracy and normativity for different personality traits. © 2019 European Association of Personality Psychology     

Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects

Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses (Baron &; Kenny, 1986 Baron, R. M. and Kenny, D. A. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations.. Journal of Personality and Social Psychology, 51: 1173–1182. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Sobel, 1982 Sobel, M. E. 1982. “Asymptotic confidence intervals for indirect effects in structural equation models.”. In Sociological methodology 1982 Edited by: Leinhardt, S. 290–312. San Francisco: Jossey-Bass. [Crossref] , [Google Scholar]) have in recent years been supplemented by computationally intensive methods such as bootstrapping, the distribution of the product methods, and hierarchical Bayesian Markov chain Monte Carlo (MCMC) methods. These different approaches for assessing mediation are illustrated using data from Dunn, Biesanz, Human, and Finn (2007). However, little is known about how these methods perform relative to each other, particularly in more challenging situations, such as with data that are incomplete and/or nonnormal. This article presents an extensive Monte Carlo simulation evaluating a host of approaches for assessing mediation. We examine Type I error rates, power, and coverage. We study normal and nonnormal data as well as complete and incomplete data. In addition, we adapt a method, recently proposed in statistical literature, that does not rely on confidence intervals (CIs) to test the null hypothesis of no indirect effect. The results suggest that the new inferential method—the partial posterior p value—slightly outperforms existing ones in terms of maintaining Type I error rates while maximizing power, especially with incomplete data. Among confidence interval approaches, the bias-corrected accelerated (BC _a ) bootstrapping approach often has inflated Type I error rates and inconsistent coverage and is not recommended; In contrast, the bootstrapped percentile confidence interval and the hierarchical Bayesian MCMC method perform best overall, maintaining Type I error rates, exhibiting reasonable power, and producing stable and accurate coverage rates.     

Daily cognitive appraisals, daily affect, and long-term depressive symptoms: the role of self-esteem and self-concept clarity in the stress process

The current study investigated how self-esteem and self-concept clarity are implicated in the stress process both in the short and long term. Initial and 2-year follow-up interviews were completed by 178 participants from stepfamily unions. In twice-daily structured diaries over 7 days, participants reported their main family stressor, cognitive appraisals (perceived stressor threat and stressor controllability), and negative affect. Results of multilevel modeling indicated that high self-esteem ameliorated the effect of daily negative cognitive appraisals on daily negative affect. Self-concept clarity also buffered the effect of low self-self-esteem on depressive symptoms 2 years later. Our findings point to the vulnerability of those having low self-esteem or low self-concept clarity in terms of both short- and long-term adaptation to stress. They indicate the need for the consideration of such individual differences in designing stress management interventions.     

Towards understanding assessments of the big five: multitrait-multimethod analyses of convergent and discriminant validity across measurement occasion and type of observer

Multitrait-multimethod analyses were used to examine the degree of convergent and discriminant validity of the Big Five. Phase 1 examined self-reports of the Big Five across three measurement occasions. Self-reports of the Big Five traits were stable, but were moderately intercorrelated. Phase 2 examined assessments of the Big Five across different types of informants (self, peer, and parent). Assessments converged across types of informants and, importantly, there was no evidence of correlation between the Big Five traits across the perspectives of different types of informants. The present results suggest that the degree of orthogonality of the Big Five traits depends on the source of the data. A single informant produces Big Five traits that are intercorrelated, whereas diverse informants tend to produce a much more orthogonal structure. Discussion focuses on methodological considerations in examining levels of convergent and discriminant validity and the theoretical implications for understanding personality assessments and the relationship between three-and five-factor models of personality.