The Relative Importance and Interaction of Contextual and Methodological Predictors of Mean rWG for Work Climate

A variety of collective phenomena are understood to exist to the extent that workers agree on their perceptions of the phenomena, such as perceptions of their organization’s climate or perceptions of their team’s mental model. Researchers conducting group-level studies of such phenomena measure individuals’ perceptions via surveys and then aggregate data to the group level if the mean within-group agreement for a sample of groups is sufficiently high. Despite this widespread practice, we know little about the factors potentially affecting mean within-group agreement. Here, focusing on work climate, we report an investigation of a number of expected contextual (social interaction) and methodological predictors of mean r_WG, a common statistic for judging within-group agreement in applied psychology and management research. We used the novel approach of meta-CART, which allowed us to assess the relative importance and possible interactions of the predictor variables. Notably, mean r_WG values are driven by both contextual (average number of individuals per group and cultural individualism-collectivism) and methodological factors (the number of items in a scale and scale reliability). Our findings are largely consistent with expectations concerning how social interaction affects within-group agreement and psychometric arguments regarding why adding more items to a scale will not necessarily increase the magnitude of an index based on a Spearman-Brown “stepped-up correction.” We discuss the key insights from our results, which are relevant to the study of multilevel phenomena relying on the aggregation of individual-level data and informative for how meta-analytic researchers can simultaneously examine multiple moderator variables.

     

A Qualitative Study on Children’s Digital Media Use and Parents’ Self-interest

Journal of Child and Family Studies - This qualitative study provides insight into the role of parents’ self-interest in digital media use of children in different age groups. We conducted 31...     

Responding to the Psychological Needs of Health-Care Workers During the COVID-19 Pandemic: Case Study from the Medical College of Wisconsin

With the advent of the novel coronavirus (COVID-19) pandemic, health-care workers have been faced with an inordinately high level of trauma as frontline providers. The Medical College of Wisconsin (MCW) partnered with affiliate hospitals and community partners to mobilize a matrix of available support and interventions to deliver psychological services to reach all levels of health-care providers in timely, accessible formats. While virtual peer support groups were the most utilized resource among the support group options, other opportunities also provided unique benefits to learners whose education had been disrupted by the pandemic. Mental health must be prioritized for health-care workers in the event of future public health crises. Lessons learned from this pandemic indicate that it is critical to involve learners early on in the process in order to meet their educational needs and to increase access to evidence-based care.

     

Bold and bright: shy and supple? The effect of habitat type on personality–cognition covariance in the Aegean wall lizard (Podarcis erhardii)

Animal Cognition - Animals exhibit considerable and consistent among-individual variation in cognitive abilities, even within a population. Recent studies have attempted to address this variation...     

Estimation of the predictive power of the model in mixed‐effects meta‐regression: A simulation study

Several methods are available to estimate the total and residual amount of heterogeneity in meta‐analysis, leading to different alternatives when estimating the predictive power in mixed‐effects meta‐regression models using the formula proposed by Raudenbush (1994, 2009). In this paper, a simulation study was conducted to compare the performance of seven estimators of these parameters under various realistic scenarios in psychology and related fields. Our results suggest that the number of studies (k) exerts the most important influence on the accuracy of the results, and that precise estimates of the heterogeneity variances and the model predictive power can only be expected with at least 20 and 40 studies, respectively. Increases in the average within‐study sample size () also improved the results for all estimators. Some differences among the accuracy of the estimators were observed, especially under adverse (small k and ) conditions, while the results for the different methods tended to convergence for more optimal scenarios.     

Religious Bias Among Religiously Conscious Black Christians in the United States

Research with White participants has demonstrated religious intergroup bias; however, religious identity may be different for Black Americans. Only religiously conscious Black Christians demonstrated a preference for Christian targets over Muslim and Atheist targets. Future research should consider what factors result in a person becoming conscious of other's religion.     

School System Evaluation by Value Added Analysis Under Endogeneity

Value added is a common tool in educational research on effectiveness. It is often modeled as a (prediction of a) random effect in a specific hierarchical linear model. This paper shows that this modeling strategy is not valid when endogeneity is present. Endogeneity stems, for instance, from a correlation between the random effect in the hierarchical model and some of its covariates. This paper shows that this phenomenon is far from exceptional and can even be a generic problem when the covariates contain the prior score attainments, a typical situation in value added modeling. Starting from a general, model-free definition of value added, the paper derives an explicit expression of the value added in an endogeneous hierarchical linear Gaussian model. Inference on value added is proposed using an instrumental variable approach. The impact of endogeneity on the value added and the estimated value added is calculated accurately. This is also illustrated on a large data set of individual scores of about 200,000 students in Chile.     

Hierarchical Approximate Bayesian Computation

Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper, we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function.