The distressed (Type D) and Five-Factor Models of personality in young,healthy adults and their association with emotional inhibition and distress

The distressed (Type D) personality (the combination of negative affectivity and social inhibition traits) has been associated with adverse health outcomes. This study investigated the validity of the Type D construct against the Five-Factor Model (FFM) of personality, and its association with emotional control and distress. In total 498 university students (mean age 28.9 ± 8.4 years) completed the Type D scale (DS14), and measurements for the FFM of personality, emotional control, anxiety, depression and stress. The construct validity of the Icelandic DS14 was confirmed. The Type D components negative affectivity and social inhibition were strongly associated with neuroticism and extraversion of the FFM (r = 0.82 and r = −0.67, respectively). Negative affectivity also correlated with rehearsal/rumination (r = 0.58) and social inhibition with emotional inhibition (r = 0.54), indicative of emotional control. Type D personality (40% of sample) was associated with higher levels of anxiety, depression and stress. The Type D personality components were associated with the FFM of personality, emotional control and emotional distress. Importantly, social and emotional inhibition were closely related, providing novel information about the presence of emotional inhibition within the social inhibition trait.     

Evaluating Structural Equation Models for Categorical Outcomes: A New Test Statistic and a Practical Challenge of Interpretation

This research is concerned with two topics in assessing model fit for categorical data analysis. The first topic involves the application of a limited-information overall test, introduced in the item response theory literature, to structural equation modeling (SEM) of categorical outcome variables. Most popular SEM test statistics assess how well the model reproduces estimated polychoric correlations. In contrast, limited-information test statistics assess how well the underlying categorical data are reproduced. Here, the recently introduced C₂ statistic of Cai and Monroe (2014) is applied. The second topic concerns how the root mean square error of approximation (RMSEA) fit index can be affected by the number of categories in the outcome variable. This relationship creates challenges for interpreting RMSEA. While the two topics initially appear unrelated, they may conveniently be studied in tandem since RMSEA is based on an overall test statistic, such as C₂. The results are illustrated with an empirical application to data from a large-scale educational survey.     

Conceptual and measurement issues of the complementarity dimension of the coach–athlete relationship across cultures

ObjectivesGrounded in the 3Cs model of the coach–athlete relationship, this multi-study outlined the development of two scales that measure coaches' dominant behaviors and athletes' submissive behaviors.Method & resultsIn study 1, a pool of items was generated based on relevant literatures and interviews. The item pool was then assessed by an expert panel including academics, athletes and coaches. In Study 2 and Study 3, construct and criterion validity, as wells as internal reliability of the refined items were tested with a sample of elite coaches and athletes. Analyses revealed that Coach Dominant Behavior Scale (CDB-S) and Athlete Submissive Behavior Scale (ASB-S) possessed sound psychometric properties. In Study 4, the measurement invariance of the 10-item ASB-S was assessed across gender and five countries: Britain, China, Greece, Spain, and Sweden. Results supported the full structural invariance of the ASB-S.ConclusionOverall, the items of both CDB-S and ASB-S were found to be psychometrically sound. The discussion highlights the contributions these findings make on both conceptual and measurement levels as well as the opportunities it opens up for research with practical relevance.     

Initial development and validity evidence for the processes of change in Psychological Skills Training Questionnaire

ObjectivesThe purpose of this study was to create and provide validity evidence for the Processes of Change in Psychological Skills Training Questionnaire (PCPSTQ).DesignThe current study used a cross-sectional research design.MethodsFive hundred fifty nine NCAA Division I, professional, and Olympic level athletes participated in the current study. To create the PCPSTQ, an initial pool of 114 items was generated by the research team. After a content validity process, 65 items were retained for analysis. Exploratory structural equation modeling was used as an analytic strategy to identify the most appropriate factor structure for the PCPSTQ. Decisions about the most appropriate model were made using multiple fit indices. To examine the construct validity of the PCPSTQ, a series of one-way ANOVAs were conducted to examine differences in processes of change use across stage of change.ResultsIn the current study, validity evidence provided support for a 7-factor process of change measure (χ² = 325.84, p < .001; Comparative Fit Index = .971; Tucker Lewis Index = .945; Root Mean Square Error of Approximation = .037; Standard Root Mean Square Residual = .020). Results also supported the construct validity of the scale as a significant difference in process of change use across stage of change was reported for all seven processes.ConclusionsResults of the current study support the factor structure and construct validity of the PCPSTQ. It appears that the processes of behavior change reported across multiple behavior change domains might also be viable for sport psychology professionals.     

On the Computation of the RMSEA and CFI from the Mean-And-Variance Corrected Test Statistic with Nonnormal Data in SEM

A new type of nonnormality correction to the RMSEA has recently been developed, which has several advantages over existing corrections. In particular, the new correction adjusts the sample estimate of the RMSEA for the inflation due to nonnormality, while leaving its population value unchanged, so that established cutoff criteria can still be used to judge the degree of approximate fit. A confidence interval (CI) for the new robust RMSEA based on the mean-corrected (“Satorra-Bentler”) test statistic has also been proposed. Follow up work has provided the same type of nonnormality correction for the CFI (Brosseau-Liard & Savalei, 2014). These developments have recently been implemented in lavaan. This note has three goals: a) to show how to compute the new robust RMSEA and CFI from the mean-and-variance corrected test statistic; b) to offer a new CI for the robust RMSEA based on the mean-and-variance corrected test statistic; and c) to caution that the logic of the new nonnormality corrections to RMSEA and CFI is most appropriate for the maximum likelihood (ML) estimator, and cannot easily be generalized to the most commonly used categorical data estimators.     

Higher-order approximations to the distributions of fit indexes under fixed alternatives in structural equation models

Higher-order approximations to the distributions of fit indexes for structural equation models under fixed alternative hypotheses are obtained in nonnormal samples as well as normal ones. The fit indexes include the normal-theory likelihood ratio chi-square statistic for a posited model, the corresponding statistic for the baseline model of uncorrelated observed variables, and various fit indexes as functions of these two statistics. The approximations are given by the Edgeworth expansions for the distributions of the fit indexes under arbitrary distributions. Numerical examples in normal and nonnormal samples with the asymptotic and simulated distributions of the fit indexes show the relative inappropriateness of the normal-theory approximation using noncentral chi-square distributions. A simulation for the confidence intervals of the fit indexes based on the normal-theory Studentized estimators under normality with a small sample size indicates an advantage for the approximation by the Cornish–Fisher expansion over those by the noncentral chi-square distribution and the asymptotic normality. The author is indebted to the reviewers for their comments and suggestions, which have led to the improvement of the previous versions of this paper. This work was partially supported by Grant-in-Aid for Scientific Research from the Japanese Ministry of Education, Culture, Sports, Science, and Technology.