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Direct linkDongho 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
Peer reviewedDongho 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
Download full textWang, Chun; Nydick, Steven W. – Grantee Submission, 2019
Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve (LGC) model (e.g., McArdle, 1988) and extended the assessment of growth to multidimensional IRT models (e.g., Hsieh, von Eye, & Maier, 2010; Huang, 2013) and higher-order IRT models…
Descriptors: Longitudinal Studies, Item Response Theory, Comparative Analysis, Models
Tong, Xin; Zhang, Zhiyong – Grantee Submission, 2017
Growth curve models are widely used for investigating growth and change phenomena. Many studies in social and behavioral sciences have demonstrated that data without any outlying observation are rather an exception, especially for data collected longitudinally. Ignoring the existence of outlying observations may lead to inaccurate or even…
Descriptors: Observation, Models, Statistical Distributions, Monte Carlo Methods
Halpin, Peter F.; Kieffer, Michael J. – Grantee Submission, 2015
The authors outline the application of latent class analysis (LCA) to classroom observational instruments. LCA offers diagnostic information about teachers' instructional strengths and weaknesses, along with estimates of measurement error for individual teachers, while remaining relatively straightforward to implement and interpret. It is…
Descriptors: Multivariate Analysis, Classroom Observation Techniques, Data Analysis, Error of Measurement
Marc Marschark; Debra M. Shaver; Katherine Nagle; Lynn A. Newman – Grantee Submission, 2015
Research suggests that the academic achievement of deaf and hard-of-hearing (DHH) students is the result of a complex interplay of many factors. These factors include characteristics of the students (e.g., hearing thresholds, language fluencies, mode of communication, and communication functioning), characteristics of their family environments…
Descriptors: Predictor Variables, Academic Achievement, Deafness, Hearing Impairments
