Meta-analysis is widely accepted as the preferred method to synthesize research findings in various disciplines. This paper provides an introduction to when and how to conduct a meta-analysis. Several practical questions, such as advantages of meta-analysis over conventional narrative review and the number of studies required for a meta-analysis, are addressed. Common meta-analytic models are then introduced. An artificial dataset is used to illustrate how a meta-analysis is conducted in several software packages. The paper concludes with some common pitfalls of meta-analysis and their solutions. The primary goal of this paper is to provide a summary background to readers who would like to conduct their first meta-analytic study.

Being touched by another person influences our readiness to empathize with and support that person. We asked whether this influence arises from somatosensory experience, the proximity to the person and/or an attribution of the somatosensory experience to the person. Moreover, we were interested in whether and how touch affects the processing of ensuing events. To this end, we presented neutral and negative pictures with or without gentle pressure to the participants' forearm. In Experiment 1, pressure was applied by a friend, applied by a tactile device and attributed to the friend, or applied by a tactile device and attributed to a computer. Across these conditions, touch enhanced event-related potential (ERP) correlates of picture processing. Pictures elicited a larger posterior N100 and a late positivity discriminated more strongly between pictures of neutral and negative content when participants were touched. Experiment 2 replicated these findings while controlling for the predictive quality of touch. Experiment 3 replaced tactile contact with a tone, which failed to enhance N100 amplitude and emotion discrimination reflected by the late positivity. This indicates that touch sensitizes ongoing cognitive and emotional processes and that this sensitization is mediated by bottom-up somatosensory processing. Moreover, touch seems to be a special sensory signal that influences recipients in the absence of conscious reflection and that promotes prosocial behavior.

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Abstract. Machine learning tools are increasingly used in social sciences and policy fields due to their increase in predictive accuracy. However, little research has been done on how well the models of machine learning methods replicate across samples. We compare machine learning methods with regression on the replicability of variable selection, along with predictive accuracy, using an empirical dataset as well as simulated data with additive, interaction, and non-linear squared terms added as predictors. Methods analyzed include support vector machines (SVM), random forests (RF), multivariate adaptive regression splines (MARS), and the regularized regression variants, least absolute shrinkage and selection operator (LASSO), and elastic net. In simulations with additive and linear interactions, machine learning methods performed similarly to regression in replicating predictors; they also performed mostly equal or below regression on measures of predictive accuracy. In simulations with square terms, machine learning methods SVM, RF, and MARS improved predictive accuracy and replicated predictors better than regression. Thus, in simulated datasets, the gap between machine learning methods and regression on predictive measures foreshadowed the gap in variable selection. In replications on the empirical dataset, however, improved prediction by machine learning methods was not accompanied by a visible improvement in replicability in variable selection. This disparity is explained by the overall explanatory power of the models. When predictors have small effects and noise predominates, improved global measures of prediction in a sample by machine learning methods may not lead to the robust selection of predictors; thus, in the presence of weak predictors and noise, regression remains a useful tool for model building and replication.

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