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Michael Borenstein – Research Synthesis Methods, 2024
In any meta-analysis, it is critically important to report the dispersion in effects as well as the mean effect. If an intervention has a moderate clinical impact "on average" we also need to know if the impact is moderate for all relevant populations, or if it varies from trivial in some to major in others. Or indeed, if the…
Descriptors: Meta Analysis, Error Patterns, Statistical Analysis, Intervention
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Warne, Russell T. – Journal of Advanced Academics, 2022
Recently, Picho-Kiroga (2021) published a meta-analysis on the effect of stereotype threat on females. Their conclusion was that the average effect size for stereotype threat studies was d = .28, but that effects are overstated because the majority of studies on stereotype threat in females include methodological characteristics that inflate the…
Descriptors: Sex Stereotypes, Females, Meta Analysis, Effect Size
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Park, Sunyoung; Beretvas, S. Natasha – Journal of Experimental Education, 2019
The log-odds ratio (ln[OR]) is commonly used to quantify treatments' effects on dichotomous outcomes and then pooled across studies using inverse-variance (1/v) weights. Calculation of the ln[OR]'s variance requires four cell frequencies for two groups crossed with values for dichotomous outcomes. While primary studies report the total sample size…
Descriptors: Sample Size, Meta Analysis, Statistical Analysis, Efficiency
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Maeda, Yukiko; Harwell, Michael R. – Mid-Western Educational Researcher, 2016
The "Q" test is regularly used in meta-analysis to examine variation in effect sizes. However, the assumptions of "Q" are unlikely to be satisfied in practice prompting methodological researchers to conduct computer simulation studies examining its statistical properties. Narrative summaries of this literature are available but…
Descriptors: Meta Analysis, Q Methodology, Effect Size, Research Methodology
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Bernard, Robert M.; Borokhovski, Eugene; Schmid, Richard F.; Tamim, Rana M. – Journal of Computing in Higher Education, 2014
This article contains a second-order meta-analysis and an exploration of bias in the technology integration literature in higher education. Thirteen meta-analyses, dated from 2000 to 2014 were selected to be included based on the questions asked and the presence of adequate statistical information to conduct a quantitative synthesis. The weighted…
Descriptors: Meta Analysis, Bias, Technology Integration, Higher Education
Ottenbacher, Kenneth J. – American Journal on Mental Retardation, 1991
An analysis of 41 recently published articles in the "American Journal on Mental Retardation" and the "Journal of Mental Deficiency Research" found a higher than expected probability of Type I research errors, suggesting that approximately 20 percent of the statistically significant results may be erroneous. (DB)
Descriptors: Error Patterns, Mental Retardation, Meta Analysis, Research Methodology