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Viechtbauer, Wolfgang; López-López, José Antonio – Research Synthesis Methods, 2022
Heterogeneity is commonplace in meta-analysis. When heterogeneity is found, researchers often aim to identify predictors that account for at least part of such heterogeneity by using mixed-effects meta-regression models. Another potentially relevant goal is to focus on the amount of heterogeneity as a function of one or more predictors, but this…
Descriptors: Meta Analysis, Models, Predictor Variables, Computation
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Van Lissa, Caspar J.; van Erp, Sara; Clapper, Eli-Boaz – Research Synthesis Methods, 2023
When meta-analyzing heterogeneous bodies of literature, meta-regression can be used to account for potentially relevant between-studies differences. A key challenge is that the number of candidate moderators is often high relative to the number of studies. This introduces risks of overfitting, spurious results, and model non-convergence. To…
Descriptors: Bayesian Statistics, Regression (Statistics), Maximum Likelihood Statistics, Meta Analysis
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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
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Jewsbury, Paul A.; Bowden, Stephen C. – Journal of Psychoeducational Assessment, 2017
Fluency is an important construct in clinical assessment and in cognitive taxonomies. In the Cattell-Horn-Carroll (CHC) model, Fluency is represented by several narrow factors that form a subset of the long-term memory encoding and retrieval (Glr) broad factor. The CHC broad classification of Fluency was evaluated in five data sets, and the CHC…
Descriptors: Memory, Construct Validity, Cognitive Processes, Factor Analysis
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Schwieren, Juliane; Barenberg, Jonathan; Dutke, Stephan – Psychology Learning and Teaching, 2017
The testing effect is a robust empirical finding in the research on learning and instruction, demonstrating that taking tests during the learning phase facilitates later retrieval from long-term memory. Early evidence came mainly from laboratory studies, though in recent years applied educational researchers have become increasingly interested in…
Descriptors: Testing, Meta Analysis, Outcomes of Education, Recall (Psychology)
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Karakaya-Ozyer, Kubra; Aksu-Dunya, Beyza – International Journal of Research in Education and Science, 2018
Structural equation modeling (SEM) is one of the most popular multivariate statistical techniques in Turkish educational research. This study elaborates the SEM procedures employed by 75 educational research articles which were published from 2010 to 2015 in Turkey. After documenting and coding 75 academic papers, categorical frequencies and…
Descriptors: Literature Reviews, Structural Equation Models, Educational Technology, Multivariate Analysis
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Cook, Bryan G.; Dupuis, Danielle N.; Jitendra, Asha K. – Journal of Learning Disabilities, 2017
When classifying the evidence base of practices, special education scholars typically appraise study quality to identify and exclude from consideration in their reviews unacceptable-quality studies that are likely biased and might bias review findings if included. However, study quality appraisals used in the process of identifying evidence-based…
Descriptors: Investigations, Evidence Based Practice, Experimental Programs, Special Education