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Christian Röver; David Rindskopf; Tim Friede – Research Synthesis Methods, 2024
The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of [tau], the between-study standard deviation, and the shrunken estimates of the study effects as a…
Descriptors: Graphs, Meta Analysis, Bayesian Statistics, Visualization
Riley, Richard D.; Ensor, Joie; Hattle, Miriam; Papadimitropoulou, Katerina; Morris, Tim P. – Research Synthesis Methods, 2023
Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g.,…
Descriptors: Data Analysis, Meta Analysis, Models, Computation
T. D. Stanley; Hristos Doucouliagos; Tomas Havranek – Research Synthesis Methods, 2024
We demonstrate that all meta-analyses of partial correlations are biased, and yet hundreds of meta-analyses of partial correlation coefficients (PCCs) are conducted each year widely across economics, business, education, psychology, and medical research. To address these biases, we offer a new weighted average, UWLS[subscript +3]. UWLS[subscript…
Descriptors: Meta Analysis, Correlation, Bias, Sample Size
Rrita Zejnullahi; Larry V. Hedges – Research Synthesis Methods, 2024
Conventional random-effects models in meta-analysis rely on large sample approximations instead of exact small sample results. While random-effects methods produce efficient estimates and confidence intervals for the summary effect have correct coverage when the number of studies is sufficiently large, we demonstrate that conventional methods…
Descriptors: Robustness (Statistics), Meta Analysis, Sample Size, Computation
Kollin W. Rott; Gert Bronfort; Haitao Chu; Jared D. Huling; Brent Leininger; Mohammad Hassan Murad; Zhen Wang; James S. Hodges – Research Synthesis Methods, 2024
Meta-analysis is commonly used to combine results from multiple clinical trials, but traditional meta-analysis methods do not refer explicitly to a population of individuals to whom the results apply and it is not clear how to use their results to assess a treatment's effect for a population of interest. We describe recently-introduced causally…
Descriptors: Meta Analysis, Causal Models, Outcomes of Treatment, Medical Research
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
Furuya-Kanamori, Luis; Lin, Lifeng; Kostoulas, Polychronis; Clark, Justin; Xu, Chang – Research Synthesis Methods, 2023
Limiting the search date is a common approach utilised in therapeutic/interventional rapid reviews. Yet the accuracy of pooled estimates is unknown when applied to rapid reviews of diagnostic test accuracy studies. Data from all systematic reviews of diagnostic test accuracy studies published in the Cochrane Database of Systematic Reviews, until…
Descriptors: Diagnostic Tests, Accuracy, Meta Analysis, Item Banks
Li, Hua; Shih, Ming-Chieh; Song, Cheng-Jie; Tu, Yu-Kang – Research Synthesis Methods, 2023
Network meta-analysis combines direct and indirect evidence to compare multiple treatments. As direct evidence for one treatment contrast may be indirect evidence for other treatment contrasts, biases in the direct evidence for one treatment contrast may affect not only the estimate for this particular treatment contrast but also estimates of…
Descriptors: Network Analysis, Meta Analysis, Bias, Evidence
Schmid, Matthias; Friede, Tim; Klein, Nadja; Weinhold, Leonie – Research Synthesis Methods, 2023
Recent years have seen the development of many novel scoring tools for disease prognosis and prediction. To become accepted for use in clinical applications, these tools have to be validated on external data. In practice, validation is often hampered by logistical issues, resulting in multiple small-sized validation studies. It is therefore…
Descriptors: Probability, Meta Analysis, Time, Test Validity
Süreyya Yörük; Sedat Sen – Creativity Research Journal, 2023
The Creative Achievement Questionnaire (CAQ) is widely used to measure the creative achievement levels of individuals. Previous studies reported a varying range of reliability coefficients for the CAQ. To this date, no study has investigated the variability in the reliability coefficients of the CAQ. A random-effects reliability generalization…
Descriptors: Reliability, Generalization, Meta Analysis, Creativity
James Pustejovsky; Jingru Zhang; Elizabeth Tipton – Society for Research on Educational Effectiveness, 2023
Background/Context: In meta-analyses examining educational interventions, researchers seek to understand the distribution of intervention impacts, in order to draw generalizations about what works, for whom, and under what conditions. One common way to examine equity implications in such reviews is through moderator analysis, which involves…
Descriptors: Meta Analysis, Effect Size, Statistics, Regression (Statistics)
David Pérez-Castejón; María Begoña Vigo-Arrazola – European Journal of Teacher Education, 2024
Changing attitudes and perceptions allied to the values of diversity and inclusive education is a recognised challenge in ITE (Initial Teacher Education). Using meta-ethnographic methods, this article aims to describe how preservice teachers' attitudes or perceptions towards inclusive education can be developed during ITE. The results show the…
Descriptors: Inclusion, Preservice Teachers, Teacher Attitudes, Meta Analysis
Henry H. Zink; Ethan R. Van Norman; David A. Klingbeil – Psychology in the Schools, 2024
Single-case design (SCD) is a quantitative experimental technique in which participants serve as their own control. The use of an effect size in SCD allows evaluation of outcomes as well as comparison of outcomes via meta-analyses. Characteristics of SCD research make the selection of an appropriate effect size complicated. Additionally, there are…
Descriptors: Research Design, Case Studies, Effect Size, Academic Ability
Peter J. Godolphin; Nadine Marlin; Chantelle Cornett; David J. Fisher; Jayne F. Tierney; Ian R. White; Ewelina Rogozinska – Research Synthesis Methods, 2024
Individual participant data (IPD) meta-analyses of randomised trials are considered a reliable way to assess participant-level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment-covariate…
Descriptors: Meta Analysis, Randomized Controlled Trials, Statistical Analysis, Participant Characteristics
Maya B. Mathur – Research Synthesis Methods, 2024
Meta-analyses can be compromised by studies' internal biases (e.g., confounding in nonrandomized studies) as well as publication bias. These biases often operate nonadditively: publication bias that favors significant, positive results selects indirectly for studies with more internal bias. We propose sensitivity analyses that address two…
Descriptors: Meta Analysis, Attribution Theory, Publications, Bias

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