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Download full textForrow, Lauren; Starling, Jennifer; Gill, Brian – Regional Educational Laboratory Mid-Atlantic, 2023
The Every Student Succeeds Act requires states to identify schools with low-performing student subgroups for Targeted Support and Improvement or Additional Targeted Support and Improvement. Random differences between students' true abilities and their test scores, also called measurement error, reduce the statistical reliability of the performance…
Descriptors: At Risk Students, Low Achievement, Error of Measurement, Measurement Techniques
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
Regional Educational Laboratory Mid-Atlantic, 2023
The "Stabilizing Subgroup Proficiency Results to Improve the Identification of Low-Performing Schools" study 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)…
Descriptors: At Risk Students, Low Achievement, Error of Measurement, Measurement Techniques
Direct linkCho, Sun-Joo; Preacher, Kristopher J. – Educational and Psychological Measurement, 2016
Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…
Descriptors: Error of Measurement, Error Correction, Multivariate Analysis, Hierarchical Linear Modeling
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
Schweig, Jonathan – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2013
Measuring school and classroom environments has become central in a nation-wide effort to develop comprehensive programs that measure teacher quality and teacher effectiveness. Formulating successful programs necessitates accurate and reliable methods for measuring these environmental variables. This paper uses a generalizability theory framework…
Descriptors: Error of Measurement, Hierarchical Linear Modeling, Educational Environment, Classroom Environment
