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Ha, Cheyeon – International Journal of Research & Method in Education, 2023
This study aims to introduce network meta-analysis (NMA) to provide educational researchers with an extended view of the reviewing educational research. Meta-analytic methods have been widely used in educational research reviews. However, weaknesses have emerged in the multi-group comparison analysis of educational studies where different…
Descriptors: Comparative Analysis, Network Analysis, Meta Analysis, Intervention
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
Senior, Alistair M.; Viechtbauer, Wolfgang; Nakagawa, Shinichi – Research Synthesis Methods, 2020
Meta-analyses are often used to estimate the relative average values of a quantitative outcome in two groups (eg, control and experimental groups). However, they may also examine the relative variability (variance) of those groups. For such comparisons, two relatively new effect size statistics, the log-transformed "variability ratio"…
Descriptors: Meta Analysis, Effect Size, Research Design, Simulation
Kontopantelis, Evangelos – Research Synthesis Methods, 2018
Background: Individual patient data (IPD) meta-analysis allows for the exploration of heterogeneity and can identify subgroups that most benefit from an intervention (or exposure), much more successfully than meta-analysis of aggregate data. One-stage or two-stage IPD meta-analysis is possible, with the former using mixed-effects regression models…
Descriptors: Patients, Medical Research, Meta Analysis, Intervention
Abrandt Dahlgren, Madeleine; Fenwick, Tara; Hopwood, Nick – Teaching in Higher Education, 2016
Despite the widespread interest in using and researching simulation in higher education, little discussion has yet to address a key pedagogical concern: difficulty. A "sociomaterial" view of learning, explained in this paper, goes beyond cognitive considerations to highlight dimensions of material, situational, representational and…
Descriptors: Simulation, Higher Education, Social Theories, Experiential Learning
Harris, Nicolette; Welch Bacon, Cailee E. – Athletic Training Education Journal, 2019
Objective: To systematically review current literature to determine whether active learning is more successful than passive learning at producing cognitive skills in health care professions students. Data Sources: An electronic search was conducted in 4 databases: EBSCO-CINAHL, EBSCO-Sport Discus, Educational Resources Information Center, and…
Descriptors: Active Learning, Cognitive Ability, Retention (Psychology), Blended Learning
Cook, David A.; Hatala, Rose – Advances in Health Sciences Education, 2015
Many education research studies employ small samples, which in turn lowers statistical power. We re-analyzed the results of a meta-analysis of simulation-based education to determine study power across a range of effect sizes, and the smallest effect that could be plausibly excluded. We systematically searched multiple databases through May 2011,…
Descriptors: Educational Research, Comparative Analysis, Sample Size, Meta Analysis
Ugille, Maaike; Moeyaert, Mariola; Beretvas, S. Natasha; Ferron, John M.; Van den Noortgate, Wim – Journal of Experimental Education, 2014
A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect…
Descriptors: Effect Size, Statistical Bias, Sample Size, Regression (Statistics)