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Brannick, Michael T.; French, Kimberly A.; Rothstein, Hannah R.; Kiselica, Andrew M.; Apostoloski, Nenad – Research Synthesis Methods, 2021
Tolerance intervals provide a bracket intended to contain a percentage (e.g., 80%) of a population distribution given sample estimates of the mean and variance. In random-effects meta-analysis, tolerance intervals should contain researcher-specified proportions of underlying population effect sizes. Using Monte Carlo simulation, we investigated…
Descriptors: Meta Analysis, Credibility, Intervals, Effect Size
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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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Domínguez Islas, Clara; Rice, Kenneth M. – Research Synthesis Methods, 2022
Bayesian methods seem a natural choice for combining sources of evidence in meta-analyses. However, in practice, their sensitivity to the choice of prior distribution is much less attractive, particularly for parameters describing heterogeneity. A recent non-Bayesian approach to fixed-effects meta-analysis provides novel ways to think about…
Descriptors: Bayesian Statistics, Evidence, Meta Analysis, Statistical Inference
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Weber, Frank; Knapp, Guido; Glass, Änne; Kundt, Günther; Ickstadt, Katja – Research Synthesis Methods, 2021
There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study…
Descriptors: Meta Analysis, Computation, Intervals, Statistical Analysis
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Baek, Eunkyeng; Luo, Wen; Henri, Maria – Journal of Experimental Education, 2022
It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…
Descriptors: Meta Analysis, Hierarchical Linear Modeling, Comparative Analysis, Statistical Inference
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Verde, Pablo E.; Ohmann, Christian – Research Synthesis Methods, 2015
Researchers may have multiple motivations for combining disparate pieces of evidence in a meta-analysis, such as generalizing experimental results or increasing the power to detect an effect that a single study is not able to detect. However, while in meta-analysis, the main question may be simple, the structure of evidence available to answer it…
Descriptors: Randomized Controlled Trials, Bayesian Statistics, Comparative Analysis, Evidence
Kennedy, Robert L. – 1988
Sixty-seven educational research journals were investigated to determine the frequency of usage of inferential statistical techniques therein. The most frequently used statistical methodologies in the literature reviewed, which utilized inferential approaches, are the following: analysis of variance, correlation, t-test, multiple analysis of…
Descriptors: Content Analysis, Educational Research, Literature Reviews, Meta Analysis