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Gamon Savatsomboon; Phamornpun Yurayat; Ong-art Chanprasitchai; Warawut Narkbunnum; Jibon Kumar Sharma; Surapol Svetsomboon – Journal of Practical Studies in Education, 2024
The paper has three major objectives. The first objective of the paper is to synthesize and define common categories of meta-analysis. The second objective is to propose a way to comprehend these common categories of meta-analysis through learning from their respective generic conceptual frameworks. The third objective is to point out which R…
Descriptors: Classification, Meta Analysis, Computer Software, Educational Research
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Liping Guo; Sarah Miller; Wenjie Zhou; Zhipeng Wei; Junjie Ren; Xinyu Huang; Xin Xing; Howard White; Kehu Yang – Campbell Systematic Reviews, 2025
Background: A systematic review is a type of literature review that uses rigorous methods to synthesize evidence from multiple studies on a specific topic. It is widely used in academia, including medical and social science research. Social science is an academic discipline that focuses on human behaviour and society. However, consensus regarding…
Descriptors: Foreign Countries, Literature Reviews, Social Science Research, Meta Analysis
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Röver, Christian; Friede, Tim – Research Synthesis Methods, 2022
The variance-stabilizing Freeman-Tukey double arcsine transform was originally proposed for inference on single proportions. Subsequently, its use has been suggested in the context of meta-analysis of proportions. While some erratic behavior has been observed previously, here we point out and illustrate general issues of monotonicity and…
Descriptors: Meta Analysis, Research Problems, Statistical Analysis
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Maxi Schulz; Malte Kramer; Oliver Kuss; Tim Mathes – Research Synthesis Methods, 2024
In sparse data meta-analyses (with few trials or zero events), conventional methods may distort results. Although better-performing one-stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one-stage models by re-analysing…
Descriptors: Meta Analysis, Data Analysis, Research Methodology, Research Problems
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Micaela Sánchez-Martín; Marta Gutiérrez-Sánchez; Eva María Olmedo-Moreno; Fernando Navarro-Mateu – Cogent Education, 2024
Introduction: Concerns about the risk of bias (RoB) of Meta-analysis (MAs) have grown in parallel with the exponential increase in the number of publications in science. However, this has not been properly assessed in Education. The aims were to evaluate the RoB of MAs in Education and to identify potential predictors of a lower RoB. Methods:…
Descriptors: Literature Reviews, Meta Analysis, Bias, Research Problems
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Maya B. Mathur – Research Synthesis Methods, 2024
As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with…
Descriptors: Meta Analysis, Research Reports, Research Methodology, Research Problems
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Micheal Sandbank; Kristen Bottema-Beutel; Ya-Cing Syu; Nicolette Caldwell; Jacob I. Feldman; Tiffany Woynaroski – Autism: The International Journal of Research and Practice, 2024
We conducted a multi-pronged investigation of different types of reporting bias in autism early childhood intervention research. First, we investigated the prevalence of reporting failures of completed trials registered on clinicaltrials.gov, and found that only 7% of registered trials were updated with results on the registration platform and…
Descriptors: Literature Reviews, Meta Analysis, Autism Spectrum Disorders, Children
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Stanley, T. D.; Doucouliagos, Hristos; Ioannidis, John P. A. – Research Synthesis Methods, 2022
Recent, high-profile, large-scale, preregistered failures to replicate uncover that many highly-regarded experiments are "false positives"; that is, statistically significant results of underlying null effects. Large surveys of research reveal that statistical power is often low and inadequate. When the research record includes selective…
Descriptors: Meta Analysis, Replication (Evaluation), Statistical Analysis, Research Problems
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Seo, Michael; Furukawa, Toshi A.; Karyotaki, Eirini; Efthimiou, Orestis – Research Synthesis Methods, 2023
Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies,…
Descriptors: Prediction, Models, Patients, Data Analysis
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Schauer, Jacob M.; Lee, Jihyun; Diaz, Karina; Pigott, Therese D. – Research Synthesis Methods, 2022
Missing covariates is a common issue when fitting meta-regression models. Standard practice for handling missing covariates tends to involve one of two approaches. In a complete-case analysis, effect sizes for which relevant covariates are missing are omitted from model estimation. Alternatively, researchers have employed the so-called…
Descriptors: Statistical Bias, Meta Analysis, Regression (Statistics), Research Problems
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Thomas Cook; Mansi Wadhwa; Jingwen Zheng – Society for Research on Educational Effectiveness, 2023
Context: A perennial problem in applied statistics is the inability to justify strong claims about cause-and-effect relationships without full knowledge of the mechanism determining selection into treatment. Few research designs other than the well-implemented random assignment study meet this requirement. Researchers have proposed partial…
Descriptors: Observation, Research Design, Causal Models, Computation
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Papadimitropoulou, Katerina; Riley, Richard D.; Dekkers, Olaf M.; Stijnen, Theo; le Cessie, Saskia – Research Synthesis Methods, 2022
Meta-analysis is a widely used methodology to combine evidence from different sources examining a common research phenomenon, to obtain a quantitative summary of the studied phenomenon. In the medical field, multiple studies investigate the effectiveness of new treatments and meta-analysis is largely performed to generate the summary (average)…
Descriptors: Effect Size, Meta Analysis, Evidence, Medicine
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Bramley, Paul; López-López, José A.; Higgins, Julian P. T. – Research Synthesis Methods, 2021
Standard meta-analysis methods are vulnerable to bias from incomplete reporting of results (both publication and outcome reporting bias) and poor study quality. Several alternative methods have been proposed as being less vulnerable to such biases. To evaluate these claims independently we simulated study results under a broad range of conditions…
Descriptors: Meta Analysis, Bias, Research Problems, Computation
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Weissgerber, Sophia C.; Brunmair, Matthias; Rummer, Ralf – Educational Psychology Review, 2021
In the 2018 meta-analysis of "Educational Psychology Review" entitled "Null effects of perceptual disfluency on learning outcomes in a text-based educational context" by Xie, Zhou, and Liu, we identify some errors and inconsistencies in both the methodological approach and the reported results regarding coding and effect sizes.…
Descriptors: Meta Analysis, Research Problems, Research Methodology, Coding
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Prathiba Natesan Batley; Erica B. McClure; Brandy Brewer; Ateka A. Contractor; Nicholas John Batley; Larry Vernon Hedges; Stephanie Chin – Grantee Submission, 2023
N-of-1 trials, a special case of Single Case Experimental Designs (SCEDs), are prominent in clinical medical research and specifically psychiatry due to the growing significance of precision/personalized medicine. It is imperative that these clinical trials be conducted, and their data analyzed, using the highest standards to guard against threats…
Descriptors: Medical Research, Research Design, Data Analysis, Effect Size
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