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Eli Ben-Michael; Lindsay Page; Luke Keele – Grantee Submission, 2024
In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem…
Descriptors: Research Design, Statistical Data, Multivariate Analysis, Observation
Yuqi Gu; Elena A. Erosheva; Gongjun Xu; David B. Dunson – Grantee Submission, 2023
Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying,…
Descriptors: Multivariate Analysis, Item Response Theory, Bayesian Statistics, Models
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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
Keller, Brian T. – Grantee Submission, 2021
In this paper, we provide an introduction to the factored regression framework. This modeling framework applies the rules of probability to break up or "factor" a complex joint distribution into a product of conditional regression models. Using this framework, we can easily specify the complex multivariate models that missing data…
Descriptors: Regression (Statistics), Models, Multivariate Analysis, Computation
McNeish, Daniel; Bauer, Daniel J. – Grantee Submission, 2020
Deciding which random effects to retain is a central decision in mixed effect models. Recent recommendations advise a maximal structure whereby all theoretically relevant random effects are retained. Nonetheless, including many random effects often leads to nonpositive definiteness. A typical remedy is to simplify the random effect structure by…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Factor Analysis, Matrices
Zhou, Jianing; Bhat, Suma – Grantee Submission, 2021
Consistency of learning behaviors is known to play an important role in learners' engagement in a course and impact their learning outcomes. Despite significant advances in the area of learning analytics (LA) in measuring various self-regulated learning behaviors, using LA to measure consistency of online course engagement patterns remains largely…
Descriptors: Models, Online Courses, Learner Engagement, Learning Processes
Daniel McNeish; Laura M. Stapleton; Rebecca D. Silverman – Grantee Submission, 2017
In psychology and the behavioral sciences generally, the use of the hierarchical linear model (HLM) and its extensions for discrete outcomes are popular methods for modeling clustered data. HLM and its discrete outcome extensions, however, are certainly not the only methods available to model clustered data. Although other methods exist and are…
Descriptors: Hierarchical Linear Modeling, Social Science Research, Multivariate Analysis, Error Patterns
Cain, Meghan K.; Zhang, Zhiyong; Yuan, Ke-Hai – Grantee Submission, 2017
Nonnormality of univariate data has been extensively examined previously (Blanca et al., 2013; Micceri, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of…
Descriptors: Multivariate Analysis, Probability, Statistical Distributions, Psychological Studies
Bottge, Brian A.; Cohen, Allan S.; Choi, Hye-Jeong – Grantee Submission, 2017
In this article, we describe results of a reanalysis of two randomized studies that tested the effects of enhanced anchored instruction (EAI) on the fractions computation performance of students in special education resource rooms and inclusive mathematics classrooms. Latent class analysis and latent transition analysis classified students…
Descriptors: Mathematics Instruction, Intervention, Resource Room Programs, Inclusion