Longitudinal Associations Between Trauma Exposure and Executive Functions in Children: Findings from a Dutch Birth Cohort Study

Research on Child and Adolescent Psychopathology - This study is the first to distinguish two possible predictive directions between trauma exposure and executive functioning in children in a...     

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...     

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.     

Trajectories of Depression and Anxiety Symptoms in Adolescent Girls: A Comparison of Parallel Trajectory Approaches

Longitudinal mixture models have become popular in the literature. However, modest attention has been paid to whether these models provide a better fit to the data than growth models. Here, we compared longitudinal mixture models to growth models in the context of changes in depression and anxiety symptoms in a community sample of girls from age 10 to 17. Model comparisons found that the preferred solution was a 5-class parallel process growth mixture model that differed in the course of depression and anxiety symptoms reflecting both ordering of symptoms and qualitative group differences. Comparisons between classes revealed substantive differences on a number of outcomes using this solution. Findings are discussed in the context of clinical assessment and implementation of growth mixture models.     

Low working memory capacity is only spuriously related to poor reading comprehension

Accounts of comprehension failure, whether in the case of readers with poor skill or when syntactic complexity is high, have overwhelmingly implicated working memory capacity as the key causal factor. However, extant research suggests that this position is not well supported by evidence on the span of active memory during online sentence processing, nor is it well motivated by models that make explicit claims about the memory mechanisms that support language processing. The current study suggests that sensitivity to interference from similar items in memory may provide a better explanation of comprehension failure. Through administration of a comprehensive skill battery, we found that the previously observed association of working memory with comprehension is likely due to the collinearity of working memory with many other reading-related skills, especially IQ. In analyses which removed variance shared with IQ, we found that receptive vocabulary knowledge was the only significant predictor of comprehension performance in our task out of a battery of 24 skill measures. In addition, receptive vocabulary and non-verbal memory for serial order—but not simple verbal memory or working memory—were the only predictors of reading times in the region where interference had its primary affect. We interpret these results in light of a model that emphasizes retrieval interference and the quality of lexical representations as key determinants of successful comprehension.     

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.