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Ann A. O'Connell; Nivedita Bhaktha; Jing Zhang – Society for Research on Educational Effectiveness, 2021
Background: Counts are familiar outcomes in education research settings, including those involving tests of interventions. Clustered data commonly occur in education research studies, given that data are often collected from students within classrooms or schools. There is a wide array of distributions and models that can be used for clustered…
Descriptors: Hierarchical Linear Modeling, Educational Research, Statistical Distributions, Multivariate Analysis
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Park, Sunyoung; Natasha Beretvas, S. – Journal of Experimental Education, 2021
When selecting a multilevel model to fit to a dataset, it is important to choose both a model that best matches characteristics of the data's structure, but also to include the appropriate fixed and random effects parameters. For example, when researchers analyze clustered data (e.g., students nested within schools), the multilevel model can be…
Descriptors: Hierarchical Linear Modeling, Statistical Significance, Multivariate Analysis, Monte Carlo Methods
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Rhoads, Christopher – Society for Research on Educational Effectiveness, 2016
Current practice for conducting power analyses in hierarchical trials using survey based ICC and effect size estimates may be misestimating power because ICCs are not being adjusted to account for treatment effect heterogeneity. Results presented in Table 1 show that the necessary adjustments can be quite large or quite small. Furthermore, power…
Descriptors: Statistical Analysis, Correlation, Effect Size, Surveys
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Rhoads, Christopher – Journal of Educational and Behavioral Statistics, 2017
Researchers designing multisite and cluster randomized trials of educational interventions will usually conduct a power analysis in the planning stage of the study. To conduct the power analysis, researchers often use estimates of intracluster correlation coefficients and effect sizes derived from an analysis of survey data. When there is…
Descriptors: Statistical Analysis, Hierarchical Linear Modeling, Surveys, Effect Size
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Stapleton, Laura M.; Yang, Ji Seung; Hancock, Gregory R. – Journal of Educational and Behavioral Statistics, 2016
We present types of constructs, individual- and cluster-level, and their confirmatory factor analytic validation models when data are from individuals nested within clusters. When a construct is theoretically individual level, spurious construct-irrelevant dependency in the data may appear to signal cluster-level dependency; in such cases,…
Descriptors: Multivariate Analysis, Factor Analysis, Validity, Models
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Poteat, V. Paul; Heck, Nicholas C.; Yoshikawa, Hirokazu; Calzo, Jerel P. – American Educational Research Journal, 2016
Using youth program models to frame the study of Gay-Straight Alliances (GSAs), we identified individual and structural predictors of greater engagement in these settings with a cross-sectional sample of 295 youth in 33 GSAs from the 2014 Massachusetts GSA Network Survey (69% LGBQ, 68% cisgender female, 68% White, M[subscript age] =16.07).…
Descriptors: Youth Programs, Predictor Variables, Participation, Surveys