Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 6 |
Descriptor
| Meta Analysis | 7 |
| Research Design | 7 |
| Simulation | 7 |
| Effect Size | 4 |
| Comparative Analysis | 3 |
| Intervention | 3 |
| Statistical Bias | 3 |
| Computation | 2 |
| Maximum Likelihood Statistics | 2 |
| Sampling | 2 |
| Statistical Analysis | 2 |
| More ▼ | |
Source
| Journal of Experimental… | 2 |
| Research Synthesis Methods | 2 |
| International Journal of… | 1 |
| Journal of Educational and… | 1 |
| Journal of Experimental… | 1 |
Author
Publication Type
| Journal Articles | 7 |
| Reports - Research | 5 |
| Information Analyses | 2 |
| Reports - Evaluative | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
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
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
Langan, Dean; Higgins, Julian P. T.; Jackson, Dan; Bowden, Jack; Veroniki, Areti Angeliki; Kontopantelis, Evangelos; Viechtbauer, Wolfgang; Simmonds, Mark – Research Synthesis Methods, 2019
Studies combined in a meta-analysis often have differences in their design and conduct that can lead to heterogeneous results. A random-effects model accounts for these differences in the underlying study effects, which includes a heterogeneity variance parameter. The DerSimonian-Laird method is often used to estimate the heterogeneity variance,…
Descriptors: Simulation, Meta Analysis, Health, Comparative Analysis
Adelman, James S.; Estes, Zachary – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
Adelman, Marquis, Sabatos-DeVito, and Estes (2013) collected word naming latencies from 4 participants who read 2,820 words 50 times each. Their recommendation and practice was that R2 targets set for models should take into account subject idiosyncrasies as replicable patterns, equivalent to a subjects-as-fixed-effects assumption. In light of an…
Descriptors: Word Recognition, Naming, Individual Differences, Multiple Regression 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)
Pustejovsky, James E.; Hedges, Larry V.; Shadish, William R. – Journal of Educational and Behavioral Statistics, 2014
In single-case research, the multiple baseline design is a widely used approach for evaluating the effects of interventions on individuals. Multiple baseline designs involve repeated measurement of outcomes over time and the controlled introduction of a treatment at different times for different individuals. This article outlines a general…
Descriptors: Hierarchical Linear Modeling, Effect Size, Maximum Likelihood Statistics, Computation
Peer reviewedSawilowsky, Shlomo; And Others – Journal of Experimental Education, 1994
A Monte Carlo study considers the use of meta analysis with the Solomon four-group design. Experiment-wise Type I error properties and the relative power properties of Stouffer's Z in the Solomon four-group design are explored. Obstacles to conducting meta analysis in the Solomon design are discussed. (SLD)
Descriptors: Meta Analysis, Monte Carlo Methods, Power (Statistics), Research Design

Direct link
