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Liu, Yixing; Levy, Roy; Yel, Nedim; Schulte, Ann C. – School Effectiveness and School Improvement, 2023
Although there is recognition that there may be differential outcomes for groups of students within schools, examination of outcomes for subgroups presents challenges to researchers and policymakers. It complicates analytic procedures, particularly when the number of students per school in the subgroup is small. We explored five alternatives for…
Descriptors: Growth Models, Hierarchical Linear Modeling, School Effectiveness, Academic Achievement
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Boedeker, Peter – Practical Assessment, Research & Evaluation, 2017
Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum…
Descriptors: Hierarchical Linear Modeling, Maximum Likelihood Statistics, Bayesian Statistics, Computation
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Leckie, George – Journal of Educational and Behavioral Statistics, 2018
The traditional approach to estimating the consistency of school effects across subject areas and the stability of school effects across time is to fit separate value-added multilevel models to each subject or cohort and to correlate the resulting empirical Bayes predictions. We show that this gives biased correlations and these biases cannot be…
Descriptors: Value Added Models, Reliability, Statistical Bias, Computation
Martinkova, Patricia; Goldhaber, Dan – Center for Education Data & Research, 2015
Inter-rater reliability, commonly assessed by intra-class correlation coefficient ICC, is an important index for describing the extent to which there is consistency amongst two or more raters in assigned measures. In organizational research, the data structure is often hierarchical and designs deviate substantially from the ideal of a balanced…
Descriptors: Teacher Selection, Interrater Reliability, Public School Teachers, Hierarchical Linear Modeling
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Gray, Geraldine; McGuinness, Colm; Owende, Philip; Carthy, Aiden – Journal of Learning Analytics, 2014
Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and…
Descriptors: Psychometrics, Data Analysis, Academic Achievement, Postsecondary Education
Jeon, Minjeong – ProQuest LLC, 2012
Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…
Descriptors: Hierarchical Linear Modeling, Computation, Measurement, Maximum Likelihood Statistics
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Huang, Hung-Yu; Wang, Wen-Chung – Educational and Psychological Measurement, 2014
In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Computation, Test Reliability