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The Measure Matters: Examining Achievement Gaps on Cognitively Demanding Reading and Mathematics Assessments MET SES LSA

Author(s):
Kevelson, Marisol J. C.
Publication Year:
2019
Report Number:
RR-19-43
Source:
ETS Research Report, ETS Policy Information Center Report
Document Type:
Report
Page Count:
28
Subject/Key Words:
Achievement Gaps, Racial Achievement Gaps, Achievement Tests, State Testing Programs, Constructed-Response Tests, Item Format, Measures of Effective Teaching (MET), Socioeconomic Status (SES), Large-Scale Assessments (LSA), Educational Equality, Multilevel Modeling, Reading Assessment, Mathematics Assessment

Abstract

This study presents estimates of Black–White, Hispanic–White, and income achievement gaps using data from two different types of reading and mathematics assessments: constructed-response assessments that were likely more cognitively demanding and state achievement tests that were likely less cognitively demanding (i.e., composed solely or largely of multiple-choice items). Specifically, the study utilized multilevel modeling of data from over 25,000 fourth- through eighth-grade students participating in the 6-state Measures of Effective Teaching (MET) study of 2009–2010, including data from the state reading and mathematics achievement tests used in MET districts at that time and data from the Stanford Achievement Test Open-Ended Reading Assessment (SAT-9OE) and the Balanced Assessment of Mathematics (BAM). The latter two assessments, consisting entirely of constructed-response items, were selected by MET researchers to assess learning outcomes, such as those included in the Common Core State Standards, deemed more cognitively complex than those assessed by state achievement tests at the time. The investigator found that estimated Black–White, Hispanic–White, and income achievement gaps were smaller on the SAT-9OE than on state reading assessments, before accounting for other relevant factors. Estimates of Black–White and Hispanic–White mathematics achievement gaps were slightly larger using BAM data, whereas the estimated income achievement gap was slightly smaller using BAM data. In later models, prior student academic achievement and average student subject-specific prior achievement accounted for portions of these estimated achievement gaps.

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