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Statistical Analyses for Studying Replication: Meta-Analytic Perspectives.

Larry V. Hedges, +1 more
- Vol. 24, Iss: 5, pp 557-570
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TLDR
The authors provide alternative conceptual frameworks that lead to different statistical analyses to test hypotheses about replication, including whether the burden of proof is placed on replication or nonreplication, whether replication is exact or allows for a small amount of "negligible heterogeneity," and whether the studies observed are assumed to be fixed (constituting the entire body of relevant evidence) or are a sample from a universe of possibly relevant studies.
Abstract
Formal empirical assessments of replication have recently become more prominent in several areas of science, including psychology. These assessments have used different statistical approaches to determine if a finding has been replicated. The purpose of this article is to provide several alternative conceptual frameworks that lead to different statistical analyses to test hypotheses about replication. All of these analyses are based on statistical methods used in meta-analysis. The differences among the methods described involve whether the burden of proof is placed on replication or nonreplication, whether replication is exact or allows for a small amount of "negligible heterogeneity," and whether the studies observed are assumed to be fixed (constituting the entire body of relevant evidence) or are a sample from a universe of possibly relevant studies. The statistical power of each of these tests is computed and shown to be low in many cases, raising issues of the interpretability of tests for replication. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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Preprint of Too good to be false: Nonsignificant results revisited

TL;DR: This article examined evidence for false negatives in nonsignificant results in three different ways and concluded that false negatives deserve more attention in the current debate on statistical practices in psychology, and they also proposed the adapted Fisher test to detect the presence of at least one false negative in a set of statistically nonsignificantly results.

Design-Based Approaches to Causal Replication Studies

TL;DR: The paper describes research designs for replication and demonstrates how multiple designs may be combined in systematic replication efforts, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.
Journal ArticleDOI

On the bias of complete‐ and shifting‐case meta‐regressions with missing covariates

TL;DR: In this paper , the authors study the potential magnitude of that bias assuming a log-linear model of missingness and find that the bias can be substantial, as large as Cohen's d = 0.4-0.8 depending on the missingness mechanism.
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Beyond Random Effects: When Small-Study Findings Are More Heterogeneous

TL;DR: In this article , meta-regression methods are introduced that identify whether the magnitude of heterogeneity across study findings is correlated with their standard errors and find that small-sample studies typically have higher heterogeneity.
Peer Review

Replicability Across Multiple Studies

TL;DR: A selective overview of analyses that can be carried out towards establishing replicability of the scientific findings is provided and methods for the setting where a single outcome is examined in multiple studies are described.
References
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Alexander A. Aarts, +290 more
- 28 Aug 2015 - 
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Journal ArticleDOI

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Methods of Meta-Analysis: Correcting Error and Bias in Research Findings

TL;DR: In this article, the authors present a meta-analysis of Artifact Distributions and their impact on study outcomes. But they focus mainly on the second-order sampling error and related issues.
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