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Raykov, Tenko; DiStefano, Christine – Educational and Psychological Measurement, 2022
A latent variable modeling-based procedure is discussed that permits to readily point and interval estimate the design effect index in multilevel settings using widely circulated software. The method provides useful information about the relationship of important parameter standard errors when accounting for clustering effects relative to…
Descriptors: Hierarchical Linear Modeling, Correlation, Evaluation, Research Design
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Huang, Francis L. – School Psychology Quarterly, 2018
The use of multilevel modeling (MLM) to analyze nested data has grown in popularity over the years in the study of school psychology. However, with the increase in use, several statistical misconceptions about the technique have also proliferated. We discuss some commonly cited myths and golden rules related to the use of MLM, explain their…
Descriptors: Hierarchical Linear Modeling, School Psychology, Misconceptions, Correlation
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Raykov, Tenko; Marcoulides, George A. – Educational and Psychological Measurement, 2015
A latent variable modeling procedure that can be used to evaluate intraclass correlation coefficients in two-level settings with discrete response variables is discussed. The approach is readily applied when the purpose is to furnish confidence intervals at prespecified confidence levels for these coefficients in setups with binary or ordinal…
Descriptors: Correlation, Computation, Statistical Analysis, Hierarchical Linear Modeling
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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
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Theobald, Elli – CBE - Life Sciences Education, 2018
Discipline-based education researchers have a natural laboratory--classrooms, programs, colleges, and universities. Studies that administer treatments to multiple sections, in multiple years, or at multiple institutions are particularly compelling for two reasons: first, the sample sizes increase, and second, the implementation of the treatments…
Descriptors: Educational Research, Hierarchical Linear Modeling, Program Implementation, Predictor Variables
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Peugh, James L. – Journal of Early Adolescence, 2014
Applied early adolescent researchers often sample students (Level 1) from within classrooms (Level 2) that are nested within schools (Level 3), resulting in data that requires multilevel modeling analysis to avoid Type 1 errors. Although several articles have been published to assist researchers with analyzing sample data nested at two levels, few…
Descriptors: Early Adolescents, Research, Hierarchical Linear Modeling, Data Analysis
Anderson, Daniel – Behavioral Research and Teaching, 2012
This manuscript provides an overview of hierarchical linear modeling (HLM), as part of a series of papers covering topics relevant to consumers of educational research. HLM is tremendously flexible, allowing researchers to specify relations across multiple "levels" of the educational system (e.g., students, classrooms, schools, etc.).…
Descriptors: Hierarchical Linear Modeling, Educational Research, Case Studies, Longitudinal Studies
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Hedges, Larry V.; Hedberg, E. C.; Kuyper, Arend M. – Educational and Psychological Measurement, 2012
Intraclass correlations are used to summarize the variance decomposition in populations with multilevel hierarchical structure. There has recently been considerable interest in estimating intraclass correlations from surveys or designed experiments to provide design parameters for planning future large-scale randomized experiments. The large…
Descriptors: Correlation, Computation, Hierarchical Linear Modeling, Reading Achievement