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Direct linkChoi, Kilchan – Asia Pacific Education Review, 2020
This paper proposes a teacher effect change model in the form of a latent variable regression 5-level hierarchical model (LVR-HM5). Using multiple years of student achievement data, the LVR-HM5 attempts to simultaneously estimate teacher effect as well as teacher initial status and the gap parameter to model the change of such latent parameters…
Descriptors: Teacher Effectiveness, Regression (Statistics), Academic Achievement, Time
Download full textRegional 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
Choi, Kilchan; Kim, Jinok – Journal of Educational and Behavioral Statistics, 2019
This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual assessment scores over grades for students who are nested within cohorts within schools, the LVR-HM4 attempts to simultaneously model two types of…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Longitudinal Studies, Cohort Analysis
