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Brian T. Keller; Craig K. Enders – Grantee Submission, 2023
A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study…
Descriptors: Data Analysis, Hierarchical Linear Modeling, Monte Carlo Methods, Bias
Moeyaert, Mariola; Yang, Panpan; Xu, Xinyun – Grantee Submission, 2021
This study investigated the power of two-level hierarchical linear modeling (HLM) to explain variability in intervention effectiveness between participants in context of single-case experimental design (SCED) research. HLM is a flexible technique that allows the inclusion of participant characteristics (e.g., age, gender, and disability types) as…
Descriptors: Hierarchical Linear Modeling, Intervention, Research Design, Participant Characteristics