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Dongho Shin; Yongyun Shin; Nao Hagiwara – Grantee Submission, 2025
We consider Bayesian estimation of a hierarchical linear model (HLM) from partially observed data, assumed to be missing at random, and small sample sizes. A vector of continuous covariates C includes cluster-level partially observed covariates with interaction effects. Due to small sample sizes from 37 patient-physician encounters repeatedly…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Multivariate Analysis, Data Analysis
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Rashelle J. Musci; Joseph Kush; Elise T. Pas; Catherine P. Bradshaw – Grantee Submission, 2024
Given the increased focus of educational research on what works for whom and under what circumstances over the last decade, educational researchers are increasingly turning toward mixture models to identify heterogeneous subgroups among students. Such data are inherently nested, as students are nested within classrooms and schools. Yet there has…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Nonparametric Statistics, Educational Research
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Avery H. Closser; Adam Sales; Anthony F. Botelho – Grantee Submission, 2024
Emergent technologies present platforms for educational researchers to conduct randomized controlled trials (RCTs) and collect rich data on study students' performance, behavior, learning processes, and outcomes in authentic learning environments. As educational research increasingly uses methods and data collection from such platforms, it is…
Descriptors: Data Analysis, Educational Research, Randomized Controlled Trials, Sampling
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Dongho Shin – Grantee Submission, 2024
We consider Bayesian estimation of a hierarchical linear model (HLM) from small sample sizes. The continuous response Y and covariates C are partially observed and assumed missing at random. With C having linear effects, the HLM may be efficiently estimated by available methods. When C includes cluster-level covariates having interactive or other…
Descriptors: Bayesian Statistics, Computation, Hierarchical Linear Modeling, Data Analysis
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Antonio A. Morgan-López; Lissette M. Saavedra; Heather L. McDaniel; Stephen G. West; Nicholas S. Ialongo; Catherine P. Bradshaw; Alexandra T. Tonigan; Barrett W. Montgomery; Nicole P. Powell; Lixin Qu; Anna C. Yaros; John E. Lochman – Grantee Submission, 2024
Coping Power (CP) is a preventive intervention that focuses on reducing child externalizing problems. Although it is typically delivered in a group format (GCP), individually-delivered CP (ICP) has produced greater mean reductions in externalizing problems. However, standard analysis of randomized trials loses individual-level information…
Descriptors: Coping, Prevention, Intervention, Child Behavior