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Small Sample Methods in Multilevel Analysis
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Yasuhiro Yamamoto; Yasuo Miyazaki – Journal of Experimental Education, 2025
Bayesian methods have been said to solve small sample problems in frequentist methods by reflecting prior knowledge in the prior distribution. However, there are dangers in strongly reflecting prior knowledge or situations where much prior knowledge cannot be used. In order to address the issue, in this article, we considered to apply two Bayesian…
Descriptors: Sample Size, Hierarchical Linear Modeling, Bayesian Statistics, Prior Learning
Multiple Imputation to Estimate Hierarchical Models from Data Missing at Random: Latent Covariates, Random Coefficients, and Statistical Interactions
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Yongyun Shin; Stephen W. Raudenbush – Grantee Submission, 2025
Consider the conventional multilevel model Y=C[gamma]+Zu+e where [gamma] represents fixed effects and (u,e) are multivariate normal random effects. The continuous outcomes Y and covariates C are fully observed with a subset Z of C. The parameters are [theta]=([gamma],var(u),var(e)). Dempster, Rubin and Tsutakawa (1981) framed the estimation as a…
Descriptors: Hierarchical Linear Modeling, Maximum Likelihood Statistics, Sampling, Error of Measurement
Beyond Primary Assessment on the WISC-V: An Investigation of the Structural Validity of the Ancillary Scores
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Ryan J. McGill; Stefan C. Dombrowski; Gary L. Canivez – Psychology in the Schools, 2025
The present study examined the posited structure of the Wechsler Intelligence Scale for Children--Fifth Edition (WISC-V) ancillary index scores with normative sample participants aged 6-16 years (N = 2200) using a series of confirmatory factor analyzes (CFA) with maximum likelihood estimation. CFA results supported the retention of auditory…
Descriptors: Children, Intelligence Tests, Test Validity, Scores
Semiparametric Structural Equation Models with Interval-Censored Data
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Hongxi Li; Shuwei Li; Liuquan Sun; Xinyuan Song – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social,…
Descriptors: Statistical Analysis, Statistical Studies, Structural Equation Models, Intervals