A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein. Research Synthesis Methods. https://t.co/45SxwKi5Z0
A basic introduction to fixed-effect and random-effects models for meta-analysis.
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Jan 2019 – May 2025Where it's discussed
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News6 articles
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YouTube3 videos
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The overall tone indicates a recognition of the publication's relevance, exemplified by users discussing related tools like RevMan and software recommendations, or asking clarifying questions about model weighting. The moderate interest and engagement are reflected in comments like 'I think I got it' and mentions of software, suggesting curiosity and practical considerations rather than deep theoretical critique.
Most discussions acknowledge the importance and relevance of the publication, with some users explicitly referencing the authors or content positively.
The discussions show moderate interest, some users expressing intent to explore or receive the publication rather than engaging deeply with its content.
Several users mention tools, software, and basic concepts, indicating some level of engagement but limited in-depth analysis of the models themselves.
The impact appears moderate, as the discussions mostly serve informational or promotional purposes without widespread critical debate or application examples.
Abstract in brief
- Fixed-effect and random-effects models are two main approaches used in meta-analysis, each based on different assumptions about the data.
- While these models use similar formulas and can sometimes produce similar estimates, they are not interchangeable because they serve different purposes and are based on fundamentally different principles.
- Choosing the correct model is crucial for accurate statistical estimation and meaningful interpretation, as it influences the context and goals of the analysis.]
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Most liked on X and BlueskyReceive a copy of “A basic introduction to fixed-effect and random-effects models for meta-analysis” in your inbox! https://t.co/a4evKCOwNW Authors: Michael Borenstein, Larry V. Hedges, Julian P.T. Higgins, Hannah Rothstein #fixedeffectmodel #randomeffectsmodel #metaanalysis https://t.co/6RJKsWMTxR
Logan J, Webb J, Singh NK, Tanner N, Barrett K, Wall M, Walsh B, Ayala AP. Scoping review search practices in the social sciences: A scoping review. Res Synth Methods. 2024 Aug 12. doi: 10.1002/jrsm.1742. Epub ahead of print. PMID: 39135430.
RT @triadsou: A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian…
RT @triadsou: A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian…
@CaAl @EvidenceRobot https://t.co/YJn501L98b In terms of software, RevMan is excellent - easy and simple to use. Would recommend checking out @SRToolbox for loads of other meta-analysis tools though!
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Meta-Analysis Types Explained: Fixed vs Random Effects Models
Beginner-friendly overview of various meta-analysis methods, focusing on fixed effects and random effects models. Understand the differences, purposes, and when to apply each approach for accurate results.
Understanding Fixed and Random Effects Models in Meta-Analysis
Learn about fixed-effect and random-effects models used in meta-analysis, their underlying assumptions, and the importance of selecting the correct model to ensure accurate and meaningful results.
Choosing Between Fixed-Effect and Random-Effects Models in Meta-Analysis
Choosing between fixed-effect and random-effects models can drastically impact your meta-analysis results. These models are based on different assumptions about data heterogeneity, and selecting the correct one is…
Logan J, Webb J, Singh NK, Tanner N, Barrett K, Wall M, Walsh B, Ayala AP. Scoping review search practices in the social sciences: A scoping review. Res Synth Methods. 2024 Aug 12. doi: 10.1002/jrsm.1742. Epub ahead of print. PMID: 39135430.
@APoppick Alright, I found this. I think I got it. You basically just add the total variation to the group variation to your weights of the means, right? And when the variation for whole data is large everything has the same weight and when its small..... https://t.co/fof4fcK12h
Receive a copy of “A basic introduction to fixed-effect and random-effects models for meta-analysis” in your inbox! https://t.co/a4evKCOwNW Authors: Michael Borenstein, Larry V. Hedges, Julian P.T. Higgins, Hannah Rothstein #fixedeffectmodel #randomeffectsmodel #metaanalysis https://t.co/6RJKsWMTxR
RT @triadsou: A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian…
RT @triadsou: A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian…
RT @triadsou: A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian…
RT @triadsou: A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian…
A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein. Research Synthesis Methods. https://t.co/45SxwKi5Z0
Receive a copy of “A basic introduction to fixed-effect and random-effects models for meta-analysis” in your inbox https://t.co/a4evKCOwNW Authors: Michael Borenstein, Larry V. Hedges, Julian P.T. Higgins, Hannah Rothstein #fixedeffectmodel #randomeffectsmodel #metaanalysis https://t.co/KhiwOROIic
@CaAl @EvidenceRobot https://t.co/YJn501L98b In terms of software, RevMan is excellent - easy and simple to use. Would recommend checking out @SRToolbox for loads of other meta-analysis tools though!