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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Advances in Methods and Practices in Psychological Science

ISSN

2515-2459

e-ISSN

—

Svazek periodika

5

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

000853051200001

EID výsledku v databázi Scopus

2-s2.0-85134908639