Earlier work found that – compared to participants who adopted constrictive body postures – participants who adopted expansive body postures reported feeling more powerful, showed an increase in testosterone and a decrease in cortisol, and displayed an increased tolerance for risk. However, these power pose effects have recently come under considerable scrutiny. Here, we present a Bayesian meta-analysis of six preregistered studies from this special issue, focusing on the effect of power posing on felt power. Our analysis improves on standard classical meta-analyses in several ways. First and foremost, we considered only preregistered studies, eliminating concerns about publication bias. Second, the Bayesian approach enables us to quantify evidence for both the alternative and the null hypothesis. Third, we use Bayesian model-averaging to account for the uncertainty with respect to the choice for a fixed-effect model or a random-effect model. Fourth, based on a literature review, we obtained an empirically informed prior distribution for the between-study heterogeneity of effect sizes. This empirically informed prior can serve as a default choice not only for the investigation of the power pose effect but for effects in the field of psychology more generally. For effect size, we considered a default and an informed prior. Our meta-analysis yields very strong evidence for an effect of power posing on felt power. However, when the analysis is restricted to participants unfamiliar with the effect, the meta-analysis yields evidence that is only moderate.

Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models and solve some of the issues often encountered in classical maximum likelihood estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error, bias, coverage rates, and quantiles of the estimates. In this article, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors-with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature, and all code for conducting the prior sensitivity analysis is available in the online supplemental materials. (PsycINFO Database Record

We present a data set containing 705 between-study heterogeneity estimates τ2 as reported in 61 articles published in Psychological Bulletin from 1990-2013. The data set also includes information about the number and type of effect sizes, the Q- and I2-statistics, and publication bias. The data set is stored in the Open Science Framework repository (https://osf.io/wyhve/) and can be used for several purposes:(1) to compare a specific heterogeneity estimate to the distribution of between-study heterogeneity estimates in psychology; (2) to construct an informed prior distribution for the between-study heterogeneity in psychology; (3) to obtain realistic population values for Monte Carlo simulations investigating the performance of meta-analytic methods

When meta‐analyzing heterogeneous bodies of literature, meta‐regression can be used to account for potentially relevant between‐studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non‐convergence. To overcome these challenges, we introduce Bayesian Regularized Meta‐Analysis (BRMA), which selects relevant moderators from a larger set of candidates by shrinking small regression coefficients towards zero with regularizing (LASSO or horseshoe) priors. This method is suitable when there are many potential moderators, but it is not known beforehand which of them are relevant. A simulation study compared BRMA against state‐of‐the‐art random effects meta‐regression using restricted maximum likelihood (RMA). Results indicated that BRMA outperformed RMA on three metrics: BRMA had superior predictive performance, which means that the results generalized better; BRMA was better at rejecting irrelevant moderators, and worse at detecting true effects of relevant moderators, while the overall proportion of Type I and Type II errors was equivalent to RMA. BRMA regression coefficients were slightly biased towards zero (by design), but its residual heterogeneity estimates were less biased than those of RMA. BRMA performed well with as few as 20 studies, suggesting its suitability as a small sample solution. We present free open source software implementations in the R‐packagepema(for penalized meta‐analysis) and in the stand‐alone statistical program JASP. An applied example demonstrates the use of the R‐package.

Bayesian estimation of multilevel structural equation models (MLSEMs) offers advantages in terms of sample size requirements and computational feasibility, but does require careful specification of the prior distribution especially for the random effects variance parameters. The traditional “non-informative” conjugate choice of an inverse-Gamma prior with small hyperparameters has been shown time and again to be problematic. In this paper, we investigate alternative, more robust prior distributions for the doubly latent categorical multilevel model. In contrast to multilevel models without latent variables, MLSEMs have multiple random effects variance parameters both for the multilevel structure and for the latent variable structure. It is therefore even more important to construct reasonable priors for these parameters. We find that, although the robust priors outperform the traditional inverse-Gamma prior, their hyperparameters do require careful consideration.