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Beyond Jacobson and Truax: Estimation of Clinical Significance Trajectories in the Coping Power Intervention Using Measurement Error-Corrected Multilevel Modeling
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Antonio A. Morgan-López; Lissette M. Saavedra; Heather L. McDaniel; Stephen G. West; Nicholas S. Ialongo; Catherine P. Bradshaw; Alexandra T. Tonigan; Barrett W. Montgomery; Nicole P. Powell; Lixin Qu; Anna C. Yaros; John E. Lochman – Grantee Submission, 2024
Coping Power (CP) is a preventive intervention that focuses on reducing child externalizing problems. Although it is typically delivered in a group format (GCP), individually-delivered CP (ICP) has produced greater mean reductions in externalizing problems. However, standard analysis of randomized trials loses individual-level information…
Descriptors: Coping, Prevention, Intervention, Child Behavior
Multilevel Modeling Resolves Ambiguities in Analyses of Discipline Disproportionality: A Demonstration Comparing Title 1 Montessori and Non-Montessori Schools
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Lee LeBoeuf; Jacob Goldstein-Greenwood; Angeline S. Lillard – Journal of Research on Educational Effectiveness, 2024
Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study…
Descriptors: Discipline, Disproportionate Representation, Measurement Techniques, Hierarchical Linear Modeling
Multilevel Modeling Resolves Ambiguities in Analyses of Discipline Disproportionality: A Demonstration Comparing Title 1 Montessori and Non-Montessori Schools
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Lee LeBoeuf; Jacob Goldstein-Greenwood; Angeline S Lillard – Grantee Submission, 2023
Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study…
Descriptors: Discipline, Disproportionate Representation, Measurement Techniques, Hierarchical Linear Modeling
Leveraging Modern Machine Learning to Reduce Chronic Absenteeism in Early Childhood
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Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence
Stabilizing Subgroup Proficiency Results to Improve the Identification of Low-Performing Schools. REL 2023-001
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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
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
Stabilizing Subgroup Proficiency Results to Improve the Identification of Low-Performing Schools. Appendixes. REL 2023-001
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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