Wim Van Den Noortgate

Mechanisms of masked priming: a meta-analysis.

The extent to which unconscious information can influence behavior has been a topic of considerable debate throughout the history of psychology. A frequently used method for studying subliminal processing is the masked priming paradigm. The authors focused on studies in which this paradigm was used. Their aim was twofold: first, to assess the magnitude of subliminal priming across the literature and to determine whether subliminal primes are processed semantically, and second, to examine potential moderators of priming effects. The authors found significant priming in their analyses, indicating that unconsciously presented information can influence behavior. Furthermore, priming was observed under circumstances in which a nonsemantic interpretation could not fully explain the effects, suggesting that subliminally presented information can be processed semantically. Nonetheless, the nonsemantic processing of primes is enhanced and priming effects are boosted when the experimental context allows the formation of automatic stimulus-response mappings. This quantitative review also revealed several moderators that influence the strength of priming. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

The effects of early prevention programs for families with young children at risk for physical child abuse and neglect: a meta-analysis

In this article, a meta-analysis is presented on 40 evaluation studies of early prevention programs for families with young children at risk for physical child abuse and neglect with mostly nonrandomized designs. The main aim of all programs was to prevent physical child abuse and neglect by providing early family support. For the meta-analysis, a multilevel approach was used. A significant overall positive effect was found, pointing to the potential usefulness of these programs. The study demonstrated a significant decrease in the manifestation of abusive and neglectful acts and a significant risk reduction in factors such as child functioning, parent-child interaction, parent functioning, family functioning, and context characteristics.

The Relation Between Short-Term Emotion Dynamics and Psychological Well-Being: A Meta-Analysis

Not only how good or bad people feel on average, but also how their feelings fluctuate across time is crucial for psychological health. The last 2 decades have witnessed a surge in research linking various patterns of short-term emotional change to adaptive or maladaptive psychological functioning, often with conflicting results. A meta-analysis was performed to identify consistent relationships between patterns of short-term emotion dynamics-including patterns reflecting emotional variability (measured in terms of within-person standard deviation of emotions across time), emotional instability (measured in terms of the magnitude of consecutive emotional changes), and emotional inertia of emotions over time (measured in terms of autocorrelation)-and relatively stable indicators of psychological well-being or psychopathology. We determined how such relationships are moderated by the type of emotional change, type of psychological well-being or psychopathology involved, valence of the emotion, and methodological factors. A total of 793 effect sizes were identified from 79 articles (N = 11,381) and were subjected to a 3-level meta-analysis. The results confirmed that overall, low psychological well-being co-occurs with more variable (overall ρ̂ = -.178), unstable (overall ρ̂ = -.205), but also more inert (overall ρ̂ = -.151) emotions. These effect sizes were stronger when involving negative compared with positive emotions. Moreover, the results provided evidence for consistency across different types of psychological well-being and psychopathology in their relation with these dynamical patterns, although specificity was also observed. The findings demonstrate that psychological flourishing is characterized by specific patterns of emotional fluctuations across time, and provide insight into what constitutes optimal and suboptimal emotional functioning. (PsycINFO Database Record

Three-level meta-analysis of dependent effect sizes

Although dependence in effect sizes is ubiquitous, commonly used meta-analytic methods assume independent effect sizes. We describe and illustrate three-level extensions of a mixed effects meta-analytic model that accounts for various sources of dependence within and across studies, because multilevel extensions of meta-analytic models still are not well known. We also present a three-level model for the common case where, within studies, multiple effect sizes are calculated using the same sample. Whereas this approach is relatively simple and does not require imputing values for the unknown sampling covariances, it has hardly been used, and its performance has not been empirically investigated. Therefore, we set up a simulation study, showing that also in this situation, a three-level approach yields valid results: Estimates of the treatment effects and the corresponding standard errors are unbiased.

Hierarchical linear models for the quantitative integration of effect sizes in single-case research

In this article, the calculation of effect size measures in single-case research and the use of hierarchical linear models for combining these measures are discussed. Special attention is given to meta-analyses that take into account a possible linear trend in the data. We show that effect size measures that have been proposed for this situation appear to be systematically affected by the duration of the experiment and fail to distinguish between effects on level and slope. To avoid these flaws, we propose to perform a multivariate meta-analysis on the standardized ordinary least squares regression coefficients from the study-specific regression equations describing the response variable.

A multilevel meta-analysis of single-subject experimental design studies

A multilevel approach is proposed to synthesize the results of single-subject experimental research by combining either raw data or effect-size measures. A multilevel meta-analysis of single-subject data allows us to make inferences not only about the effects for the participants that are investigated, but also about the generality of the effects. It also permits a systematic exploration of the possible moderating effect of study and participant characteristics. We also discuss the possibility of combining single-subject experimental research and group-comparison research in one single meta-analysis, and the difficulties involved, especially in making effect-size measures comparable for both kinds of studies.

Multilevel Meta-Analysis: A Comparison with Traditional Meta-Analytical Procedures

In this article, the authors compare the multilevel meta-analysis approach with the more traditional meta-analytical approaches. After a description and comparison of the under-lying models and some of the major techniques, the results of the multilevel approach are compared with those of the traditional approaches, using a simulation study. The results of the simulation study suggest that the maximum likelihood multilevel approach is in general superior to the fixed-effects approaches, unless only a small number of studies is available. For models without moderators, the results of the multilevel approach, however, are not substantially different from the results of the traditional random-effects approaches.

Cross-classification multilevel logistic models in psychometrics.

In IRT models, responses are explained on the basis of person and item effects. Person effects are usually defined as a random sample from a population distribution. Regular IRT models therefore can be formulated as multilevel models, including a within-person part and a between-person part. In a similar way, the effects of the items can be studied as random parameters, yielding multilevel models with a within-item part and a between-item part. The combination of a multilevel model with random person effects and one with random item effects leads to a cross-classification multilevel model, which can be of interest for IRT applications. The use of cross-classification multilevel logistic models will be illustrated with an educational measurement application.

Global processing takes time: A meta-analysis on local–global visual processing in ASD.

What does an individual with autism spectrum disorder (ASD) perceive first: the forest or the trees? In spite of 30 years of research and influential theories like the weak central coherence (WCC) theory and the enhanced perceptual functioning (EPF) account, the interplay of local and global visual processing in ASD remains only partly understood. Research findings vary in indicating a local processing bias or a global processing deficit, and often contradict each other. We have applied a formal meta-analytic approach and combined 56 articles that tested about 1,000 ASD participants and used a wide range of stimuli and tasks to investigate local and global visual processing in ASD. Overall, results show no enhanced local visual processing nor a deficit in global visual processing. Detailed analysis reveals a difference in the temporal pattern of the local-global balance, that is, slow global processing in individuals with ASD. Whereas task-dependent interaction effects are obtained, gender, age, and IQ of either participant groups seem to have no direct influence on performance. Based on the overview of the literature, suggestions are made for future research.

Assessing and explaining differential item functioning using logistic mixed models

Although differential item functioning (DIF) theory traditionally focuses on the behavior of individual items in two (or a few) specific groups, in educational measurement contexts, it is often plausible to regard the set of items as a random sample from a broader category. This article presents logistic mixed models that can be used to model uniform DIF, treating the item effects and their interaction with groups (DIF) as random. In a similar way, the group effects can be modeled as random instead of fixed, if the groups can be considered a random sample from a population of groups. The models can, furthermore, be adapted easily for modeling DIF over individual persons rather than over groups, or for modeling the differential functioning of groups of items instead of individual items. It is shown that the logistic mixed model approach is not only a comprehensive and economical way to detect these different kinds of DIF, it also encourages us to explore possible explanations of DIF by including group or item covariates in the model.Although differential item functioning (DIF) theory traditionally focuses on the behavior of individual items in two (or a few) specific groups, in educational measurement contexts, it is often plausible to regard the set of items as a random sample from a broader category. This article presents logistic mixed models that can be used to model uniform DIF, treating the item effects and their interaction with groups (DIF) as random. In a similar way, the group effects can be modeled as random instead of fixed, if the groups can be considered a random sample from a population of groups. The models can, furthermore, be adapted easily for modeling DIF over individual persons rather than over groups, or for modeling the differential functioning of groups of items instead of individual items. It is shown that the logistic mixed model approach is not only a comprehensive and economical way to detect these different kinds of DIF, it also encourages us to explore possible explanations of DIF by including group or item covariates in the model.