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Bias-Adjusted Three-Step Multilevel Latent Class Modeling with Covariates
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Johan Lyrvall; Zsuzsa Bakk; Jennifer Oser; Roberto Di Mari – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for…
Descriptors: Hierarchical Linear Modeling, Statistical Bias, Error of Measurement, Simulation
On the Merits of Longitudinal Multiple Group Modelling: An Alternative to Multilevel Modelling for Intervention Evaluations
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Little, Todd D.; Bontempo, Daniel; Rioux, Charlie; Tracy, Allison – International Journal of Research & Method in Education, 2022
Multilevel modelling (MLM) is the most frequently used approach for evaluating interventions with clustered data. MLM, however, has some limitations that are associated with numerous obstacles to model estimation and valid inferences. Longitudinal multiple-group (LMG) modelling is a longstanding approach for testing intervention effects using…
Descriptors: Longitudinal Studies, Hierarchical Linear Modeling, Alternative Assessment, Intervention
Digital Module 27: Hierarchical Rater Models
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Casabianca, Jodi M. – Educational Measurement: Issues and Practice, 2021
Module Overview: In this digital ITEMS module, Dr. Jodi M. Casabianca provides a primer on the "hierarchical rater model" (HRM) framework and the recent expansions to the model for analyzing raters and ratings of constructed responses. In the first part of the module, she establishes an understanding of the nature of constructed…
Descriptors: Hierarchical Linear Modeling, Rating Scales, Error of Measurement, Item Response Theory
Multilevel Modeling Myths
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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
Stabilizing Subgroup Proficiency Results to Improve the Identification of Low-Performing Schools. Study Snapshot. REL 2023-001
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Regional Educational Laboratory Mid-Atlantic, 2023
This Snapshot highlights key findings from a study that used Bayesian stabilization to improve the reliability (long-term stability) of subgroup proficiency measures that the Pennsylvania Department of Education (PDE) uses to identify schools for Targeted Support and Improvement (TSI) or Additional Targeted Support and Improvement (ATSI). The…
Descriptors: At Risk Students, Low Achievement, Error of Measurement, Measurement Techniques
Phantom Effects in Multilevel Compositional Analysis: Problems and Solutions
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Pokropek, Artur – Sociological Methods & Research, 2015
This article combines statistical and applied research perspective showing problems that might arise when measurement error in multilevel compositional effects analysis is ignored. This article focuses on data where independent variables are constructed measures. Simulation studies are conducted evaluating methods that could overcome the…
Descriptors: Error of Measurement, Hierarchical Linear Modeling, Simulation, Evaluation Methods