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Forrow, 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
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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
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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
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Allen, Jeff – Applied Measurement in Education, 2017
Using a sample of schools testing annually in grades 9-11 with a vertically linked series of assessments, a latent growth curve model is used to model test scores with student intercepts and slopes nested within school. Missed assessments can occur because of student mobility, student dropout, absenteeism, and other reasons. Missing data…
Descriptors: Achievement Gains, Academic Achievement, Growth Models, Scores
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Wang, Qiu; Diemer, Matthew A.; Maier, Kimberly S. – Educational and Psychological Measurement, 2013
This study integrated Bayesian hierarchical modeling and receiver operating characteristic analysis (BROCA) to evaluate how interest strength (IS) and interest differentiation (ID) predicted low–socioeconomic status (SES) youth's interest-major congruence (IMC). Using large-scale Kuder Career Search online-assessment data, this study fit three…
Descriptors: Bayesian Statistics, Socioeconomic Status, Student Interests, Gender Differences
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Yuan, Kun; McCaffrey, Daniel F.; Savitsky, Terrance D. – Society for Research on Educational Effectiveness, 2013
Standardized teaching observation protocols have become increasingly popular in evaluating teaching in recent years. One of such protocols that has gained substantial interest from researchers and practitioners is the Classroom Assessment Scoring System-Secondary (CLASSS). According to the developer, CLASS-S has three domains of teacher-student…
Descriptors: Factor Structure, Bayesian Statistics, Multivariate Analysis, Teacher Student Relationship
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Vaughn, Brandon K. – Journal on School Educational Technology, 2008
This study considers the importance of contextual effects on the quality of assessments on item bias and differential item functioning (DIF) in measurement. Often, in educational studies, students are clustered in teachers or schools, and the clusters could impact psychometric issues yet are largely ignored by traditional item analyses. A…
Descriptors: Test Bias, Educational Assessment, Educational Quality, Context Effect