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Stephan B. Bruns; Teshome K. Deressa; T. D. Stanley; Chris Doucouliagos; John P. A. Ioannidis – Research Synthesis Methods, 2024
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 allows us to estimate how many p-values are published as being statistically significant when they should have been…
Descriptors: Meta Analysis, Research Reports, Research Design, Microeconomics
Griebler, Ursula; Dobrescu, Andreea; Ledinger, Dominic; Klingenstein, Pauline; Sommer, Isolde; Emprechtinger, Robert; Persad, Emma; Gadinger, Arianna; Trivella, Marialena; Klerings, Irma; Nussbaumer-Streit, Barbara – Research Synthesis Methods, 2023
The Cochrane Rapid Review Methods Group (RRMG) first released interim guidance in March 2020 to support authors in conducting rapid reviews (RRs). The objective of this mixed-methods study was to assess the adherence and investigate authors' understanding of the RRMG guidance. We identified all documents citing the Interim Cochrane RRMG guidance…
Descriptors: Guidance, Authors, COVID-19, Pandemics
Rebecca Whittle; Joie Ensor; Miriam Hattle; Paula Dhiman; Gary S. Collins; Richard D. Riley – Research Synthesis Methods, 2024
Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a…
Descriptors: Data, Individual Characteristics, Participant Characteristics, Meta Analysis
Mikkel Helding Vembye; James Eric Pustejovsky; Therese Deocampo Pigott – Research Synthesis Methods, 2024
Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power…
Descriptors: Sample Size, Robustness (Statistics), Effect Size, Social Science Research
Shimonovich, Michal; Pearce, Anna; Thomson, Hilary; Katikireddi, Srinivasa Vittal – Research Synthesis Methods, 2022
In fields (such as population health) where randomised trials are often lacking, systematic reviews (SRs) can harness diversity in study design, settings and populations to assess the evidence for a putative causal relationship. SRs may incorporate causal assessment approaches (CAAs), sometimes called 'causal reviews', but there is currently no…
Descriptors: Evidence, Synthesis, Causal Models, Public Health
Brunner, Martin; Keller, Lena; Stallasch, Sophie E.; Kretschmann, Julia; Hasl, Andrea; Preckel, Franzis; Lüdtke, Oliver; Hedges, Larry V. – Research Synthesis Methods, 2023
Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences. Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational…
Descriptors: Meta Analysis, Surveys, Research Design, Educational Research
Hong, Sanghyun; Reed, W. Robert – Research Synthesis Methods, 2021
The purpose of this study is to show how Monte Carlo analysis of meta-analytic estimators can be used to select estimators for specific research situations. Our analysis conducts 1620 individual experiments, where each experiment is defined by a unique combination of sample size, effect size, effect size heterogeneity, publication selection…
Descriptors: Monte Carlo Methods, Meta Analysis, Research Methodology, Experiments