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Heterogeneity Estimation in Meta-Analysis: Investigating Methods for Dependent Effect Size Estimates
Jingru Zhang; James E. Pustejovsky – Society for Research on Educational Effectiveness, 2024
Background/Context: In meta-analysis examining educational intervention, characterizing heterogeneity and exploring the sources of variation in synthesized effects have become increasingly prominent areas of interest. When combining results from a collection of studies, statistical dependency among their effects size estimates will arise when a…
Descriptors: Meta Analysis, Investigations, Effect Size, Computation
Rüttenauer, Tobias; Ludwig, Volker – Sociological Methods & Research, 2023
Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Statistical Bias, Computation
Singh, Akansha; Uwimpuhwe, Germaine; Li, Mengchu; Einbeck, Jochen; Higgins, Steve; Kasim, Adetayo – International Journal of Research & Method in Education, 2022
In education, multisite trials involve randomization of pupils into intervention and comparison groups within schools. Most analytical models in multisite educational trials ignore that the impact of an intervention may be school dependent. This study investigates the impact of statistical models on the uncertainty associated with an effect size…
Descriptors: Randomized Controlled Trials, Effect Size, Hierarchical Linear Modeling, Least Squares Statistics
Lorah, Julie – Practical Assessment, Research & Evaluation, 2022
Applied educational researchers may be interested in exploring random slope effects in multilevel models, such as when examining individual growth trajectories with longitudinal data. Random slopes are effects for which the slope of an individual-level coefficient varies depending on group membership, however these effects can be difficult to…
Descriptors: Effect Size, Hierarchical Linear Modeling, Longitudinal Studies, Maximum Likelihood Statistics
Rights, Jason D.; Sterba, Sonya K. – New Directions for Child and Adolescent Development, 2021
Developmental researchers commonly utilize multilevel models (MLMs) to describe and predict individual differences in change over time. In such growth model applications, researchers have been widely encouraged to supplement reporting of statistical significance with measures of effect size, such as R-squareds ("R[superscript 2]") that…
Descriptors: Effect Size, Longitudinal Studies, Hierarchical Linear Modeling, Computation
Demirtas-Zorbaz, Selen; Akin-Arikan, Cigdem; Terzi, Ragip – School Effectiveness and School Improvement, 2021
School climate is one of the variables that affect academic achievement, but the level of correlation between school climate and academic achievement differs. On the basis of these inconsistent results between school climate and academic achievement, researchers can use meta-analyses to shed light on relevant literature. The present study ran a…
Descriptors: Educational Environment, Student Attitudes, Academic Achievement, Hierarchical Linear Modeling
Moeyaert, Mariola; Yang, Panpan – Grantee Submission, 2021
This study introduces an innovative meta-analytic approach, two-stage multilevel meta-analysis that considers the hierarchical structure of single-case experimental design (SCED) data. This approach is unique as it is suitable to include moderators at the intervention level, participant level, and study level, and is therefore especially…
Descriptors: Hierarchical Linear Modeling, Meta Analysis, Research Design, Case Studies
Lydia Bradford – ProQuest LLC, 2024
In randomized control trials (RCT), the recent focus has shifted to how an intervention yields positive results on its intended outcome. This aligns with the recent push of implementation science in healthcare (Bauer et al., 2015) but goes beyond this. RCTs have moved to evaluating the theoretical framing of the intervention as well as differing…
Descriptors: Hierarchical Linear Modeling, Mediation Theory, Randomized Controlled Trials, Research Design
Denson, Nida; Bowman, Nicholas A.; Ovenden, Georgia; Culver, K. C.; Holmes, Joshua M. – Journal of Diversity in Higher Education, 2021
Colleges and universities play a critical role in shaping intergroup dynamics in an era of increasing racial tensions in the United States. Diversity courses may serve as one important approach for preparing college students for participation in an equitable and just society, since this coursework holds a unique position at many institutions to…
Descriptors: Diversity, Courses, Outcomes of Education, Meta Analysis
Luo, Wen; Li, Haoran; Baek, Eunkyeng; Chen, Siqi; Lam, Kwok Hap; Semma, Brandie – Review of Educational Research, 2021
Multilevel modeling (MLM) is a statistical technique for analyzing clustered data. Despite its long history, the technique and accompanying computer programs are rapidly evolving. Given the complexity of multilevel models, it is crucial for researchers to provide complete and transparent descriptions of the data, statistical analyses, and results.…
Descriptors: Hierarchical Linear Modeling, Multivariate Analysis, Prediction, Research Problems