Is attention to feelings beneficial or detrimental to affective well-being? Mood regulation as a moderator variable

This research examined the functionality of attention to feelings for affective well-being. The authors found that mood regulation, but not clarity of feelings, moderated the attention-well-being relationship. For individuals with high mood regulation scores, attention was beneficial to affective well-being, whereas for individuals with low mood regulation scores, attention was detrimental to affective well-being. This finding was corroborated by self- and peer reports in Study 1 and replicated in Study 2. The validity of the scales was established by the convergence of self- and peer ratings. Moreover, Study 2 showed that dysfunctional and functional and self consciousness scales suppressed variance in attention to feelings, thereby revealing that attention incorporates both adaptive and maladaptive aspects.     

A Comparison of ML,WLSMV, and Bayesian Methods for Multilevel Structural Equation Models in Small Samples: A Simulation Study

Multilevel structural equation models are increasingly applied in psychological research. With increasing model complexity, estimation becomes computationally demanding, and small sample sizes pose further challenges on estimation methods relying on asymptotic theory. Recent developments of Bayesian estimation techniques may help to overcome the shortcomings of classical estimation techniques. The use of potentially inaccurate prior information may, however, have detrimental effects, especially in small samples. The present Monte Carlo simulation study compares the statistical performance of classical estimation techniques with Bayesian estimation using different prior specifications for a two-level SEM with either continuous or ordinal indicators. Using two software programs (Mplus and Stan), differential effects of between- and within-level sample sizes on estimation accuracy were investigated. Moreover, it was tested to which extent inaccurate priors may have detrimental effects on parameter estimates in categorical indicator models. For continuous indicators, Bayesian estimation did not show performance advantages over ML. For categorical indicators, Bayesian estimation outperformed WLSMV solely in case of strongly informative accurate priors. Weakly informative inaccurate priors did not deteriorate performance of the Bayesian approach, while strong informative inaccurate priors led to severely biased estimates even with large sample sizes. With diffuse priors, Stan yielded better results than Mplus in terms of parameter estimates.     

On the meaning of the latent variables in the CT-C(M-1) model: a comment on Maydeu-Olivares and Coffman (2006)

In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an application to the Life Orientation Test (M. F. Scheier & C. S. Carver, 1985), Maydeu-Olivares and Coffman found that results obtained from the CT-C(M-1) model were difficult to interpret. In particular, Maydeu-Olivares and Coffman challenged the asymmetry of the CT-C(M-1) model. In the present article, the authors show that the difficulties faced by Maydeu-Olivares and Coffman rest upon an improper interpretation of the meaning of the latent factors. The authors' aim is to clarify the meaning of the latent variables in the CT-C(M-1) model. The authors explain how to properly interpret the results from this model and introduce an alternative restricted model that is conceptually similar to the CT-C(M-1) model and nested within it. The fit of this model is invariant across different reference methods. Finally, the authors provide guidelines as to which model should be used in which research context.     

Norms for experiencing emotions in different cultures: inter- and intranational differences.

Within- and between-nations differences in norms for experiencing emotions were analyzed in a cross-cultural study with 1,846 respondents from 2 individualistic (United States, Australia) and 2 collectivistic (China, Taiwan) countries. A multigroup latent class analysis revealed that there were both universal and culture-specific types of norms for experiencing emotions. Moreover, strong intranational variability in norms for affect could be detected, particularly for collectivistic nations. Unexpectedly, individualistic nations were most uniform in norms, particularly with regard to pleasant affect. Individualistic and collectivistic nations differed most strongly in norms for self-reflective emotions (e.g., pride and guilt). Norms for emotions were related to emotional experiences within nations. Furthermore, there were strong national differences in reported emotional experiences, even when norms were held constant.     

Communication and Performance in Co-Located and Distributed Teams: An Issue of Shared Mental Models of Team Members?

The aim of the present study was to test whether the shared mental models of team members have an effect on team performance, communication, and physical arousal in two distributed teams in pursuit of a common goal. A sample (N = 15) of newly formed navy teams was compared with a sample (N = 13) of seasoned navy teams. The results showed that familiar teams displayed higher performance levels, faster reaction times, more accuracy, and greater mission success compared to unfamiliar teams. A significant shift in communication strategy and physiological response (heart rate) was observed between the teams and from baseline to low workload. Implications for team training are discussed.     

A Comparison of Expert Ratings and Self-Assessments of Situation Awareness During a Combat Fatigue Course

The current study compared expert-observer ratings of situation awareness (SA) with subjective self-ratings of SA in Norwegian military academy cadets during a summer combat survival course. The cadets (N?=?30) completed an 8-day combat survival course characterized by sleep and food deprivation, continuous operations, and altered circadian entrainment cues. Results indicated that self-ratings of SA did not correlate consistently with expert-observer SA ratings, and self-ratings were consistently higher than expert-observer ratings. The results are congruent with expected effects of these extreme conditions on cognition and self-awareness, demonstrate a pronounced self-enhancement bias and suggest that subjective measures of SA are not likely to provide valid estimates of SA under extreme conditions.     

Subjective well-being and adaptation to life events: a meta-analysis

Previous research has shown that major life events can have short- and long-term effects on subjective well-being (SWB). The present meta-analysis examines (a) whether life events have different effects on affective and cognitive well-being and (b) how the rate of adaptation varies across different life events. Longitudinal data from 188 publications (313 samples, N = 65,911) were integrated to describe the reaction and adaptation to 4 family events (marriage, divorce, bereavement, childbirth) and 4 work events (unemployment, reemployment, retirement, relocation/migration). The findings show that life events have very different effects on affective and cognitive well-being and that for most events the effects of life events on cognitive well-being are stronger and more consistent across samples. Different life events differ in their effects on SWB, but these effects are not a function of the alleged desirability of events. The results are discussed with respect to their theoretical implications, and recommendations for future studies on adaptation are given.     

Time frames and the distinction between affective and cognitive well-being

We examined whether the empirical differences between affective well-being (AWB) and cognitive well-being (CWB) might be due to (a) the use of different time frames in measures of AWB and CWB or (b) structural differences. In Study 1, a multitrait-multimethod (MTMM) analysis indicated that levels of different components are more similar but do not converge completely when the same time frame is used. In Study 2, we found that people are more likely to consider global life circumstances (as opposed to specific events and activities) when they evaluate their CWB, regardless of the specific time frame. In both studies, the time frame did not moderate the associations between AWB and CWB and important correlates (personality, life circumstances).     

Measuring post-traumatic stress: A psychometric evaluation of symptom- and coping questionnaires based on a Norwegian sample

The purpose of this study was to evaluate the psychometric characteristics of the Norweigian versions of the Impact of Event Scale, the Post Traumatic Stress Scale-10 item version and General Coping Questionnaire-30 item version. A group of 40 male and 56 female medical students was tested one week and four months after having started dissection of cadavers for the first time. The results showed that all scales had good internal consistency and test-retest reliability. The student sample scored lower on the IES and PTSS-10 than comparable groups of traumatized subjects. A gender difference emerged, with female subjects scoring higher than male subjects. The factor analysis of the instruments indicated good construct validity for the symptom scales. The analysis of content validity related to DSM IV criteria indicated that the IES and PTSS-10 may have some limitations in their predictive validity of PTSD. Taken together, the three scales have shown good psychometric properties and could be used in future research and clinical work.     

Structural equation modeling of multitrait-multimethod data: different models for different types of methods

The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods used) should guide the choice of the statistical model to analyze the data. In this respect, the authors distinguish between (a) interchangeable methods, (b) structurally different methods, and (c) the combination of both kinds of methods. The authors present an appropriate model for each type of method. All models allow separating measurement error from trait influences and trait-specific method effects. With respect to interchangeable methods, a multilevel confirmatory factor model is presented. For structurally different methods, the correlated trait-correlated (method-1) model is recommended. Finally, the authors demonstrate how to appropriately analyze data from MTMM designs that simultaneously use interchangeable and structurally different methods. All models are applied to empirical data to illustrate their proper use. Some implications and guidelines for modeling MTMM data are discussed.