Personality traits as moderators of the associations between deployment experiences and PTSD symptoms in OEF/OIF service members

Abstract

This investigation examined traits from the five-factor model of personality as moderators of the associations of combat and aftermath of battle experiences with symptoms of posttraumatic stress disorder (PTSD) in 214 National Guard/Reserve service members deployed to operations enduring and Iraqi freedom. Extraversion significantly moderated the associations of both combat experiences and aftermath of battle experiences with PTSD severity, with associations weakening as levels of extraversion increased. The relation between aftermath of battle experiences and PTSD was also moderated by the other four personality factors, with the relation being weaker at higher levels of agreeableness, conscientiousness, and openness, and lower levels of neuroticism. These results suggest that personality traits may impact individual responses to war trauma, particularly war-related experiences that are not directly threatening to one's safety (i.e., aftermath of battle events vs. actual combat events). Although this investigation was cross-sectional, these findings indicate that personality traits are an important risk/resiliency factor to consider in people's responses to traumatic events.     

How to Develop Collaborative Empiricism in Cognitive Behavior Therapy: Conclusions From the C&BP Special Series

In this concluding commentary, we reflect on the component parts of the special series (see Kazantzis, Cronin, Dattilio, & K. S. Dobson, 2013--this issue, for the introductory article) in order to reconstitute the “whole” concept of collaborative empiricism in CBT. Each contribution of the special series clearly acknowledges that collaboration and empiricism are imperative for effective CBT, as well as to further elucidate these concepts in a range of central and timely therapeutic contexts. The articulation of the component elements of collaboration and empiricism is discussed as an important contribution, and one that assists in distinguishing central features and clarifying differences between collaboration as defined in CBT. A second important theme of the special series is the further specification of the construct of empiricism as it relates to the practice of CBT. The special series provides clarification for the role of collaborative empiricism in therapeutic processes of case formulation, in-session structure, and self-monitoring to assist in assessment, as well as in the use of core techniques such as exposure, work for specific clinical groups such as psychosis, and adaption of the work in a manner that is respectful and responsive to the client’s culture, preferences, and abilities. Case examples illustrate the importance of relying on the client’s experience, rather than general principles or logic, and the use of the client’s experience both specifically and explicitly, and as a measure for the evaluation of therapeutic interventions.     

Cognitive Reorganization and the development of chess expertise

One current research strategy in the study of expertise is to compare experts and novices. An important aspect of decision making involves looking for similarities among problem types. Little is known about such processes. We used grid technique to examine similarity judgments associated with different levels of chess expertise. Novice, expert, and master chess players evaluated 4 sets of 12 chess boards. Average FIC scores showed a curvilinear relation to expertise, suggesting increasing differentiation followed by integration in cognitive frameworks for construing board positions. Additional cognitive measures based on move generation tended to support and extend this structural model.     

Structural constraints and object similarity in analogical mapping and inference

Theories of analogical reasoning have viewed relational structure as the dominant determinant of analogical mapping and inference, while assigning lesser importance to similarity between individual objects. An experiment is reported in which these two sources of constraints on analogy are placed in competition under conditions of high relational complexity. Results demonstrate equal importance for relational structure and object similarity, both in analogical mapping and in inference generation. The human data were successfully simulated using a computational analogy model (LISA) that treats both relational correspondences and object similarity as soft constraints that operate within a limited-capacity working memory; but not with a model (SME) that treats relational structure as pre-eminent.     

Pragmatic reasoning from multiple points of view: A response

Abstract

In our response we consider several broad sets of issues raised in the commentaries on our target article. We provide an elaboration of the general theory of pragmatic reasoning schemas and of the mechanisms posited to explain pespective effects and other contextual influences on reasoning. We consider the strengths and limitations of a number of alternative proposals offered by the commentators. Finally, we discuss possible links between pragmatic schemas and more elementary “building blocks” for human reasoning     

The Clergy Occupational Distress Index (CODI): Background and Findings from Two Samples of Clergy

This study demonstrates the reliability and validity of the Clergy Occupational Distress Index (CODI). The five-item index allows researchers to measure the frequency that clergy, who traditionally have not been the subject of occupational health studies, experience occupational distress. We assess the reliability and validity of the index using two samples of clergy: a nationally representative sample of clergy and a sample of clergy from nine Protestant denominations. Exploratory factor analysis and Cronbach’s scores are generated. Construct validity is measured by examining the association between CODI scores and depressive symptoms while controlling for demographic, ministerial, and health variables. In both samples, the five items of the CODI load onto a single factor and the Cronbach’s alpha scores are robust. The regression model indicates that a high score on the CODI (i.e., more frequent occupational distress) is positively associated with having depressive symptoms within the last 4 weeks. The CODI can be used to identify clergy who frequently experience occupational distress and to understand how occupational distress affects clergy’s health, ministerial career, and the functioning of their congregation.     

Quantifying and Testing Indirect Effects in Simple Mediation Models When the Constituent Paths Are Nonlinear

Most treatments of indirect effects and mediation in the statistical methods literature and the corresponding methods used by behavioral scientists have assumed linear relationships between variables in the causal system. Here we describe and extend a method first introduced by Stolzenberg (1980) Stolzenberg, R. M. 1980. The measurement and decomposition of causal effects in nonlinear and nonadditive models.. Sociological Methodology, 11: 459–488. [Crossref] , [Google Scholar] for estimating indirect effects in models of mediators and outcomes that are nonlinear functions but linear in their parameters. We introduce the concept of the instantaneous indirect effect of X on Y through M and illustrate its computation and describe a bootstrapping procedure for inference. Mplus code as well as SPSS and SAS macros are provided to facilitate the adoption of this approach and ease the computational burden on the researcher.     

Addressing Moderated Mediation Hypotheses: Theory,Methods, and Prescriptions

This article provides researchers with a guide to properly construe and conduct analyses of conditional indirect effects, commonly known as moderated mediation effects. We disentangle conflicting definitions of moderated mediation and describe approaches for estimating and testing a variety of hypotheses involving conditional indirect effects. We introduce standard errors for hypothesis testing and construction of confidence intervals in large samples but advocate that researchers use bootstrapping whenever possible. We also describe methods for probing significant conditional indirect effects by employing direct extensions of the simple slopes method and Johnson-Neyman technique for probing significant interactions. Finally, we provide an SPSS macro to facilitate the implementation of the recommended asymptotic and bootstrapping methods. We illustrate the application of these methods with an example drawn from the Michigan Study of Adolescent Life Transitions, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.     

A Comparison of Multilevel Imputation Schemes for Random Coefficient Models: Fully Conditional Specification and Joint Model Imputation with Random Covariance Matrices

Abstract

Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and “reverse random coefficient” imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster covariance matrices to preserve cluster-specific associations is a promising alternative for random coefficient analyses. This study is apparently the first to directly compare these procedures. Analytic results suggest that both imputation procedures can introduce bias-inducing incompatibilities with a random coefficient analysis model. Problems with fully conditional specification result from an incorrect distributional assumption, whereas joint imputation uses an underparameterized model that assumes uncorrelated intercepts and slopes. Monte Carlo simulations suggest that biases from these issues are tolerable if the missing data rate is 10% or lower and the sample is composed of at least 30 clusters with 15 observations per group. Furthermore, fully conditional specification tends to be superior with intraclass correlations that are typical of crosssectional data (e.g., ICC?=?.10), whereas the joint model is preferable with high values typical of longitudinal designs (e.g., ICC?=?.50).