Examining the interactive effects of coach-created empowering and disempowering climate dimensions on athletes’ health and functioning

Based in Duda’s (2013) hierarchical and multidimensional conceptualisation of the motivational climate, the purpose of this study was to examine whether a coach-created empowering motivational climate moderated the debilitating effects of a disempowering motivational climate on athletes’ health and optimal functioning. Athletes (N = 406, M age = 23.1 years; 67% male) completed questionnaires assessing their perceptions of coach-created empowering and disempowering climates created in training and competition, enjoyment in sport, burnout symptoms, global self-worth, and symptoms of physical ill-health. Following the recommendations of Hayes (2013) and Dawson (2014), and using PROCESS (Hayes), moderated regression analyses showed that the interaction between disempowering and empowering climate dimensions was significant and predicted 1% unique variance in 3 outcome variables (i.e., enjoyment, reduced accomplishment, and physical symptoms). The Johnson-Neyman technique was employed to plot and probe the significant interactions, which revealed moderately strong to strong values of an empowering climate tempered the significant relationship between a disempowering climate and the three outcome variables. The findings from this study have implications for coach education and suggest programmes that train coaches to understand how to create empowering climates and avoid (or dramatically reduce) disempowering climates are warranted.     

Entitlement and conscientiousness in the prediction of organizational deviance

Responding to Wu and LeBreton’s (2011) call for further study, this paper examines dispositional predictors of organizational deviance. In a sample of 428 participants, self-report data were collected anonymously. Using hierarchical regression, the dispositional variables of entitlement and conscientiousness were similarly strong and statistically significant predictors of organizational deviance. The total variance explained in deviance by these variables and some demographic variables was .31. Additionally, the specificity matching principle suggests that narrow band traits like entitlement are better at predicting narrowly measured behaviors like deviance than are broad band traits like conscientiousness. Using dominance analysis, entitlement was a stronger predictor of organizational deviance than is conscientiousness.     

领导-下属外向性人格匹配性与下属工作投入的关系:基于支配补偿理论

基于支配补偿理论,本研究考察领导与下属外向性人格的匹配性对下属工作投入的影响。对743对领导-下属进行配对问卷调查,在两个时间点获取调查数据。多项式回归与响应面分析表明,下属与领导外向性人格差异越大,下属工作投入水平越高。在下属与领导外向性人格存在差异的情形下,"高下属外向性、低领导外向性"组合比"低下属外向性、高领导外向性"组合,下属的工作投入水平更高。在下属与领导外向性人格一致的情形下,下属的工作投入和外向性人格存在倒U型曲线关系。研究证明了在外向性维度上领导和下属是支配互补的关系时,下属的工作投入水平更高。     

Testing Differential Item Functioning in Small Samples

Abstract

Differential item functioning (DIF) is a pernicious statistical issue that can mask true group differences on a target latent construct. A considerable amount of research has focused on evaluating methods for testing DIF, such as using likelihood ratio tests in item response theory (IRT). Most of this research has focused on the asymptotic properties of DIF testing, in part because many latent variable methods require large samples to obtain stable parameter estimates. Much less research has evaluated these methods in small sample sizes despite the fact that many social and behavioral scientists frequently encounter small samples in practice. In this article, we examine the extent to which model complexity—the number of model parameters estimated simultaneously—affects the recovery of DIF in small samples. We compare three models that vary in complexity: logistic regression with sum scores, the 1-parameter logistic IRT model, and the 2-parameter logistic IRT model. We expected that logistic regression with sum scores and the 1-parameter logistic IRT model would more accurately estimate DIF because these models yielded more stable estimates despite being misspecified. Indeed, a simulation study and empirical example of adolescent substance use show that, even when data are generated from / assumed to be a 2-parameter logistic IRT, using parsimonious models in small samples leads to more powerful tests of DIF while adequately controlling for Type I error. We also provide evidence for minimum sample sizes needed to detect DIF, and we evaluate whether applying corrections for multiple testing is advisable. Finally, we provide recommendations for applied researchers who conduct DIF analyses in small samples.     

Modelling monotonic effects of ordinal predictors in Bayesian regression models

Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under- or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which it is reasonable to assume their effects to be monotonic. The parameterization of such monotonic effects is realized in terms of a scale parameter b representing the direction and size of the effect and a simplex parameter modelling the normalized differences between categories. This ensures that predictions increase or decrease monotonically, while changes between adjacent categories may vary across categories. This formulation generalizes to interaction terms as well as multilevel structures. Monotonic effects may be applied not only to ordinal predictors, but also to other discrete variables for which a monotonic relationship is plausible. In simulation studies we show that the model is well calibrated and, if there is monotonicity present, exhibits predictive performance similar to or even better than other approaches designed to handle ordinal predictors. Using Stan, we developed a Bayesian estimation method for monotonic effects which allows us to incorporate prior information and to check the assumption of monotonicity. We have implemented this method in the R package brms, so that fitting monotonic effects in a fully Bayesian framework is now straightforward.     

Putting mood in context: Using smartphones to examine how people feel in different locations

Does personality predict how people feel in different types of situations? The present research addressed this question using data from several thousand individuals who used a mood tracking smartphone application for several weeks. Results from our analyses indicated that people’s momentary affect was linked to their location, and provided preliminary evidence that the relationship between state affect and location might be moderated by personality. The results highlight the importance of looking at person-situation relationships at both the trait- and state-levels and also demonstrate how smartphones can be used to collect person and situation information as people go about their everyday lives.     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.