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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
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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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William R. Dardick; Jeffrey R. Harring – Journal of Educational and Behavioral Statistics, 2025
Simulation studies are the basic tools of quantitative methodologists used to obtain empirical solutions to statistical problems that may be impossible to derive through direct mathematical computations. The successful execution of many simulation studies relies on the accurate generation of correlated multivariate data that adhere to a particular…
Descriptors: Statistics, Statistics Education, Problem Solving, Multivariate Analysis
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Zsuzsa Bakk; Roberto Di Mari; Jennifer Oser; Jouni Kuha – Structural Equation Modeling: A Multidisciplinary Journal, 2022
In this article, we present a two-stage estimation approach applied to multilevel latent class analysis (LCA) with covariates. We separate the estimation of the measurement and structural model. This makes the extension of the structural model computationally efficient. We investigate the robustness against misspecifications of the proposed…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Computation, Measurement
Eric C. Hedberg – Grantee Submission, 2023
In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…
Descriptors: Statistical Analysis, Multivariate Analysis, Randomized Controlled Trials, Research Design
E. C. Hedberg – American Journal of Evaluation, 2023
In cluster randomized evaluations, a treatment or intervention is randomly assigned to a set of clusters each with constituent individual units of observations (e.g., student units that attend schools, which are assigned to treatment). One consideration of these designs is how many units are needed per cluster to achieve adequate statistical…
Descriptors: Statistical Analysis, Multivariate Analysis, Randomized Controlled Trials, Research Design
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Wang, Chun; Zhang, Xue – Grantee Submission, 2019
The relations among alternative parameterizations of the binary factor analysis (FA) model and two-parameter logistic (2PL) item response theory (IRT) model have been thoroughly discussed in literature (e.g., Lord & Novick, 1968; Takane & de Leeuw, 1987; McDonald, 1999; Wirth & Edwards, 2007; Kamata & Bauer, 2008). However, the…
Descriptors: Test Items, Error of Measurement, Item Response Theory, Factor Analysis