T. D. Stanley
YOU?
Author Swipe
An Ethnodrama Exploring Staff Members’ Perspectives on Building Community Partnerships to Support Youth in Restrictive Education Settings Open
This qualitative ethnodrama-based study explores the perspectives of four juvenile justice facility leaders on the role and impact of community partnerships in supporting justice-involved youth in restrictive settings. Despite existing lit…
Robust Bayesian Meta-Regression: Model-Averaged Moderation Analysis in the Presence of Publication Bias Open
Meta-regression is an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations, such as inadequate consideration of model…
Robust Bayesian meta-regression: Model-averaged moderation analysis in the presence of publication bias. Open
Meta-regression is an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations, such as inadequate consideration of model…
Reducing the biases of the conventional meta-analysis of correlations Open
Conventional meta-analyses (both fixed and random effects) of correlations are biased due to the mechanical relationship between the estimated correlation and its standard error. Simulations that are closely calibrated to match actual rese…
Conventional wisdom, meta‐analysis, and research revision in economics Open
Over the past several decades, meta‐analysis has emerged as a widely accepted tool to understand economics research. Meta‐analyses often challenge the established conventional wisdom of their respective fields. We systematically review a w…
Estimating the extent of selective reporting: An application to economics Open
Using a sample of 70,399 published p ‐values from 192 meta‐analyses, we empirically estimate the counterfactual distribution of p ‐values in the absence of any biases. Comparing observed p ‐values with counterfactually expected p ‐values a…
Meta‐analyses of partial correlations are biased: Detection and solutions Open
We demonstrate that all meta‐analyses of partial correlations are biased, and yet hundreds of meta‐analyses of partial correlation coefficients (PCCs) are conducted each year widely across economics, business, education, psychology, and me…
Footprint of publication selection bias on meta‐analyses in medicine, environmental sciences, psychology, and economics Open
Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta‐analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environm…
Exploring open science practices in behavioural public policy research Open
In their book ‘Nudge: Improving Decisions About Health, Wealth and Happiness’, Thaler & Sunstein (2009) argue that choice architectures are promising public policy interventions. This research programme motivated the creation of ‘nudge uni…
Conventional Wisdom, Meta-Analysis, and Research Revision in Economics Open
Over the past several decades, meta-analysis has emerged as a widely accepted tool to understand economics research. Meta-analyses often challenge the established conventional wisdom of their respective fields. We systematically review a w…
Selective and (mis)leading economics journals: Meta‐research evidence Open
We assess statistical power and excess statistical significance among 31 leading economics general interest and field journals using 22,281 parameter estimates from 368 distinct areas of economics research. Median statistical power in lead…
Reducing the biases of the conventional meta-analysis of correlations Open
Conventional meta-analyses (both fixed and random effects) of correlations are biased due to the correlation between the estimated correlation and its standard error. Simulations that are closely calibrated to match actual research conditi…
Meta‐analysis of social science research: A practitioner's guide Open
This article provides concise, nontechnical, step‐by‐step guidelines on how to conduct a modern meta‐analysis, especially in social sciences. We treat publication bias, p‐ hacking, and systematic heterogeneity as phenomena meta‐analysts mu…
Robust Bayesian Meta-Regression — Model-Averaged Moderation Analysis in the Presence of Publication Bias Open
Meta-regression constitutes an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations that include inadequate considera…
Robust Bayesian Meta-Regression: Model-Averaged Moderation Analysis in the Presence of Publication Bias Open
Meta-regression is an essential meta-analytic tool for investigating sources of heterogeneity and assessing the impact of moderators. However, existing methods for meta-regression have limitations, such as inadequate consideration of model…
Meta-Analysis of Social Science Research: A Practitioner’s Guide Open
This paper provides concise, nontechnical, step-by-step guidelines on how to conduct a modern meta-analysis, especially in social sciences. We treat publication bias, p-hacking, and heterogeneity as phenomena meta-analysts must always conf…
Meta-analyses in psychology often overestimate evidence for and size of effects Open
Adjusting for publication bias is essential when drawing meta-analytic inferences. However, most methods that adjust for publication bias do not perform well across a range of research conditions, such as the degree of heterogeneity in eff…
Meta-analyses of partial correlations are biased: Detection and solutions Open
We demonstrate that all meta-analyses of partial correlations are biased, and yet hundreds of meta-analyses of partial correlation coefficients (PCC) are conducted each year widely across economics, business, education, psychology, and med…
Correcting bias in the meta-analysis of correlations Open
We demonstrate that all conventional meta-analyses of correlation coefficients are biased, explain why, and offer solutions. Because the standard error of the correlation coefficient depends on the size of the coefficient, inverse-variance…
Exploring Open Science Practices in Behavioural Public Policy Research Open
In their book `Nudge: Improving Decisions About Health, Wealth and Happiness', Thaler and Sunstein argue that choice architectures are promising public policy interventions. This research programme motivated the creation of so-called `nudg…
Correct standard errors can bias meta‐analysis Open
Partial correlation coefficients are often used as effect sizes in the meta‐analysis and systematic review of multiple regression analysis research results. There are two well‐known formulas for the variance and thereby for the standard er…
Beyond Random Effects: When Small-Study Findings Are More Heterogeneous Open
New meta-regression methods are introduced that identify whether the magnitude of heterogeneity across study findings is correlated with their standard errors. Evidence from dozens of meta-analyses finds robust evidence of this correlation…
Footprint of publication selection bias on meta-analyses in medicine, environmental sciences, psychology, and economics Open
Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environm…
Robust Bayesian meta‐analysis: Model‐averaging across complementary publication bias adjustment methods Open
Publication bias is a ubiquitous threat to the validity of meta‐analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent…
No evidence for nudging after adjusting for publication bias Open
Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms …
Meta-Analyses in Psychology Often Overestimate Evidence for and Size of Effects Open
Adjusting for publication bias is essential when drawing meta-analytic inferences. However,most methods that adjust for publication bias are sensitive to the particular researchconditions, such as the degree of heterogeneity in effect size…