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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
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
Ziqian Xu – Grantee Submission, 2022
With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at…
Descriptors: Research Problems, Bayesian Statistics, Structural Equation Models, Data Analysis
Jennifer Hill; George Perrett; Vincent Dorie – Grantee Submission, 2023
Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…
Descriptors: Causal Models, Statistical Inference, Artificial Intelligence, Data Analysis
Vincent Dorie; George Perrett; Jennifer L. Hill; Benjamin Goodrich – Grantee Submission, 2022
A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…
Descriptors: Statistical Inference, Causal Models, Artificial Intelligence, Data Analysis
Kaplan, David; Chen, Jianschen; Yavuz, Sinan; Lyu, Weicong – Grantee Submission, 2022
The purpose of this paper is to demonstrate and evaluate the use of "Bayesian dynamic borrowing"(Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case…
Descriptors: Bayesian Statistics, Models, Prediction, Accuracy
David Kaplan; Jianshen Chen; Weicong Lyu; Sinan Yavuz – Grantee Submission, 2023
The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…
Descriptors: Bayesian Statistics, Longitudinal Studies, Children, Surveys

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
Du, Han; Enders, Craig; Keller, Brian; Bradbury, Thomas N.; Karney, Benjamin R. – Grantee Submission, 2022
Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as…
Descriptors: Bayesian Statistics, Data Analysis, Computer Simulation, Sample Size
Lijin Zhang; Xueyang Li; Zhiyong Zhang – Grantee Submission, 2023
The thriving developer community has a significant impact on the widespread use of R software. To better understand this community, we conducted a study analyzing all R packages available on CRAN. We identified the most popular topics of R packages by text mining the package descriptions. Additionally, using network centrality measures, we…
Descriptors: Computer Software, Programming Languages, Data Analysis, Visual Aids
Xu, Ziqian; Hai, Jiarui; Yang, Yutong; Zhang, Zhiyong – Grantee Submission, 2022
Social network data often contain missing values because of the sensitive nature of the information collected and the dependency among the network actors. As a response, network imputation methods including simple ones constructed from network structural characteristics and more complicated model-based ones have been developed. Although past…
Descriptors: Social Networks, Network Analysis, Data Analysis, Bayesian Statistics
Sainan Xu; Jing Lu; Jiwei Zhang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational…
Descriptors: Educational Assessment, Bayesian Statistics, Statistical Inference, Item Response Theory
Weber, Sebastian; Gelman, Andrew; Lee, Daniel; Betancourt, Michael; Vehtari, Aki; Racine-Poon, Amy – Grantee Submission, 2018
Throughout the different phases of a drug development program, randomized trials are used to establish the tolerability, safety and efficacy of a candidate drug. At each stage one aims to optimize the design of future studies by extrapolation from the available evidence at the time. This includes collected trial data and relevant external data.…
Descriptors: Bayesian Statistics, Data Analysis, Drug Therapy, Pharmacology
Enders, Craig K.; Keller, Brian T.; Levy, Roy – Grantee Submission, 2018
Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables,…
Descriptors: Hierarchical Linear Modeling, Behavioral Science Research, Computer Software, Bayesian Statistics
Arenson, Ethan A.; Karabatsos, George – Grantee Submission, 2017
Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…
Descriptors: Bayesian Statistics, Item Response Theory, Nonparametric Statistics, Models