Adjusting for Publication Bias in JASP and R: Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00562627" target="_blank" >RIV/67985807:_____/22:00562627 - isvavai.cz</a>

Result on the web

<a href="https://dx.doi.org/10.1177/25152459221109259" target="_blank" >https://dx.doi.org/10.1177/25152459221109259</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/25152459221109259" target="_blank" >10.1177/25152459221109259</a>

Result language

angličtina

Original language name

Adjusting for Publication Bias in JASP and R: Selection Models, PET-PEESE, and Robust Bayesian Meta-Analysis

Original language description

Meta-analyses are essential for cumulative science, but their validity can be compromised by publication bias. To mitigate the impact of publication bias, one may apply publication-bias-adjustment techniques such as precision-effect test and precision-effect estimate with standard errors (PET-PEESE) and selection models. These methods, implemented in JASP and R, allow researchers without programming experience to conduct state-of-the-art publication-bias-adjusted meta-analysis. In this tutorial, we demonstrate how to conduct a publication-bias-adjusted meta-analysis in JASP and R and interpret the results. First, we explain two frequentist bias-correction methods: PET-PEESE and selection models. Second, we introduce robust Bayesian meta-analysis, a Bayesian approach that simultaneously considers both PET-PEESE and selection models. We illustrate the methodology on an example data set, provide an instructional video (https://bit.ly/pubbias) and an R-markdown script (https://osf. io/uhaew/), and discuss the interpretation of the results. Finally, we include concrete guidance on reporting the meta-analytic results in an academic article.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Advances in Methods and Practices in Psychological Science

ISSN

2515-2459

e-ISSN

—

Volume of the periodical

5

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

1-19

UT code for WoS article

000853051200001

EID of the result in the Scopus database

2-s2.0-85134908639