Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Addressing selective reporting bias in meta-analysis of dependent effect sizes: A tutorial in R. |
| Συγγραφείς: |
Chen M; Department of Educational Psychology, University of Texas at Austin., Pustejovsky JE; Department of Educational Psychology, University of Wisconsin-Madison. |
| Πηγή: |
Psychological methods [Psychol Methods] 2026 Jul 06. Date of Electronic Publication: 2026 Jul 06. |
| Publication Model: |
Ahead of Print |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: American Psychological Association Country of Publication: United States NLM ID: 9606928 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-1463 (Electronic) Linking ISSN: 1082989X NLM ISO Abbreviation: Psychol Methods Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: Washington, DC : American Psychological Association, c1996- |
| Περίληψη: |
Selective reporting bias arises when findings from primary research studies are incompletely reported and where the likelihood that a result is reported depends on the magnitude or statistical significance of the effect. When applied to selectively reported data, conventional meta-analytic models can produce systematically biased parameter estimates. Various statistical methods have been proposed for dealing with selective reporting in univariate meta-analysis, where each primary study contributes a single effect size estimate. However, fewer methods have been developed for handling selective reporting and publication bias in meta-analysis with dependent effect sizes, which are a common feature of meta-analyses in psychology. In this tutorial, we provide a guide for how to investigate potential selective reporting in meta-analyses of dependent effects, focusing on diagnosis and correction of selection reporting bias. We review several recently developed methods including a regression-based adjustment technique, a step-function selection model, and a sensitivity analysis approach. We demonstrate the implementation of these methods in the R statistical environment using data from two recent meta-analyses as examples. We discuss the application and interpretation of the methods, highlighting their strengths and limitations. (PsycInfo Database Record (c) 2026 APA, all rights reserved). |
| Entry Date(s): |
Date Created: 20260706 Latest Revision: 20261019 |
| Update Code: |
20261019 |
| DOI: |
10.1037/met0000841 |
| PMID: |
42406458 |
| Βάση Δεδομένων: |
MEDLINE |