Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 2 |
Descriptor
Author
| Choi, Kilchan | 1 |
| Kim, Jinok | 1 |
Publication Type
| Reports - Descriptive | 2 |
| Journal Articles | 1 |
Education Level
| Elementary Education | 2 |
| Middle Schools | 2 |
| Early Childhood Education | 1 |
| Grade 3 | 1 |
| Grade 5 | 1 |
| High Schools | 1 |
| Intermediate Grades | 1 |
| Junior High Schools | 1 |
| Primary Education | 1 |
| Secondary Education | 1 |
Audience
Location
| Pennsylvania | 1 |
Laws, Policies, & Programs
| No Child Left Behind Act 2001 | 1 |
Assessments and Surveys
| Iowa Tests of Basic Skills | 1 |
What Works Clearinghouse Rating
Peer reviewed
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
Direct linkChoi, 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
