Figure 1. Within-Grade Percent Change in Grade Level Enrollments in CO (2020-21).

Measuring Public School Enrollment Changes During the COVID-19 Pandemic

April 15, 2022

Introduction The COVID-19 pandemic led to historic changes in public school enrollment in the US. There has been considerable interest in quantifying the magnitude of these changes, and in understanding their causes and consequences (e.g., Dee & Murphy, 2021). An analysis of enrollment data across all 50 states and the...

CADRE report

Descriptive Analyses of Connect for Success, Multi-Tiered Systems of Support, and Accountability Pathways

April 8, 2022

This is an addendum to a report prepared for the Colorado Department of Education (CDE) by the Center for Assessment, Design, Research & Evaluation (CADRE) at CU Boulder (Shear et al., 2021). CDE requested an addendum to the 2021 CADRE report presenting similar descriptive analyses for schools that participated in the Connect for Success (CFS) program and providing initial descriptive analyses of academic outcomes for schools participating in two other Empowering Action for School Improvement (EASI) programs: The Multi-Tiered Systems of Supports (MTSS) and Accountability Pathways Planning and Implementation (Pathways).

CADRE report

Evaluation of Colorado School Turnaround Network and School Turnaround Leadership Development Grants: Descriptive Analysis of 2015- 2020 Cohorts

April 2, 2021

For this report, we analyzed longitudinal administrative data collected by the Colorado Department of Education to track the academic performance of schools. The analyses contained in this report focus on the set of schools receiving School Turnaround Network (STN) and School Turnaround Leadership Development (STLD) supports.

CADRE report

Comparison of 2019 Cohort and Baseline Student Growth Percentiles

Dec. 14, 2020

This report focuses on one particular technical choice CDE must make when deciding which version of the SGP model to use in measuring student growth, namely whether these measures should be based on so-called cohort or baseline-referenced SGPs.

Between-Year Stability of Growth Percentiles : Technical Brief #3

Jan. 16, 2020

Link to Resource: Between-Year Stability of Growth Percentiles : Technical Brief #3 Authors: Benjamin R. Shear and Elena Diaz-Bilello The purpose of this research brief for the Colorado Department of Education Accountability & Data Analysis Unit is to examine the stability of year‐to‐year median growth percentiles by assessment content area...

Growth Percentiles, Achievement & Demographics: Technical Brief #1 - 2019

Jan. 16, 2020

Link to Resource: Growth Percentiles, Achievement & Demographics: Technical Brief #1 - 2019 Author: Benjamin R. Shear and Elena Diaz-Bilello The purpose of this research brief for the Colorado Department of Education Accountability & Data Analysis is to address to what extent are school‐level Median Growth Percentiles correlated with current...

Factors Impacting Growth Percentile Variability: Technical Brief #2 - 2019

Jan. 16, 2020

Link to Resource: Factors Impacting Growth Percentile Variability: Technical Brief #2 - 2019 Author: Benjamin R. Shear and Elena Diaz-Bilello The purpose of this research brief for the Colorado Department of Education Accountability & Data Analysis Unit is to address the question, compared to achievement, how much of the variability...

Summary of Median Growth Percentile Analyses: Descriptive Statistics, Demographics Correlations, and Stability Analyses

Jan. 16, 2020

Link to Resource: Summary of Median Growth Percentile Analyses: Descriptive Statistics, Demographics Correlations, and Stability Analyses Authors: Benjamin R. Shear Citation: Shear, B.R. (2019). Summary of median growth percentile analyses: Descriptive statistics, demographics correlations, and stability analyses. Boulder, CO: The Center for Assessment, Design, Research and Evaluation (CADRE).

Using Administrative Test-Score Data to Study Educational Opportunity in the US

Keynote address presented by Benjamin Shear at the St John’s University in Queens, New York at the 7th Annual Leadership Symposium on Mar. 16, 2019. In this talk Dr. Shear will discuss the Stanford Education Data Archive (SEDA), a publicly available dataset containing information about student achievement for nearly all...

Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data

By Benjamin Shear. When contextual features of test-taking environments differentially affect item responding for different test-takers and these features vary across test administrations, they may cause differential item functioning (DIF) that varies across test administrations. Because many common DIF detection methods ignore potential DIF variance, this paper proposes the use of random coefficient hierarchical logistic regression (RC-HLR) models to test for both uniform DIF and DIF variance simultaneously.

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