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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
Minju Hong – ProQuest LLC, 2022
Reliability indicates the internal consistency of a test. In educational studies, reliability is a key feature for a test. Researchers have proposed many traditional reliability estimates, such as coefficient alpha and coefficient omega. However, traditional reliability indices do not deal with the data hierarchy, even though the multilevel…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Factor Structure, Test Reliability
Han Du; Brian Keller; Egamaria Alacam; Craig Enders – Grantee Submission, 2023
In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). A multilevel mediation model is used as an illustrative example to compare different types of DIC and WAIC. More specifically, the study compares the…
Descriptors: Bayesian Statistics, Models, Comparative Analysis, Probability
Lorah, Julie – Practical Assessment, Research & Evaluation, 2022
Applied educational researchers may be interested in exploring random slope effects in multilevel models, such as when examining individual growth trajectories with longitudinal data. Random slopes are effects for which the slope of an individual-level coefficient varies depending on group membership, however these effects can be difficult to…
Descriptors: Effect Size, Hierarchical Linear Modeling, Longitudinal Studies, Maximum Likelihood Statistics
Shen, Ting; Konstantopoulos, Spyros – Practical Assessment, Research & Evaluation, 2022
Large-scale assessment survey (LSAS) data are collected via complex sampling designs with special features (e.g., clustering and unequal probability of selection). Multilevel models have been utilized to account for clustering effects whereas the probability weighting approach (PWA) has been used to deal with design informativeness derived from…
Descriptors: Sampling, Weighted Scores, Hierarchical Linear Modeling, Educational Research
Craig K. Enders – Grantee Submission, 2023
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of "Psychological Methods." Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of…
Descriptors: Data, Research, Theories, Regression (Statistics)
Nagy, Gabriel; Ulitzsch, Esther – Educational and Psychological Measurement, 2022
Disengaged item responses pose a threat to the validity of the results provided by large-scale assessments. Several procedures for identifying disengaged responses on the basis of observed response times have been suggested, and item response theory (IRT) models for response engagement have been proposed. We outline that response time-based…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Predictor Variables, Classification
Yongyun Shin; Stephen W. Raudenbush – Grantee Submission, 2023
We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R[subscript obs] and C[subscript obs]. However, if the model for R given C includes random…
Descriptors: Maximum Likelihood Statistics, Hierarchical Linear Modeling, Error of Measurement, Statistical Distributions