An appraisal of small area estimation methods to explore expansion of state subgroup reporting on NAEP

Abstract This study explores the use of Small Area Estimation (SAE) methods to expand reporting of state-level subgroup performance on National Assessment of Educational Progress (NAEP) assessments without requiring the assessment program to draw larger samples of students and expand test administration. To evaluate the SAE methods, small area estimates of mean subgroup achievement formed from samples too small to meet NAEP’s current reporting requirements are compared with design-based estimates of mean subgroup achievement that satisfy the program’s minimum sample size requirement for reporting. Results demonstrate that several SAE methods yield estimates of mean achievement that are more accurate and reliable than estimates of mean achievement that satisfy NAEP’s current reporting requirement. Policy implications and future directions for research are discussed.

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Publication Details

Journal
Large-scale Assessments in Education
Published
2026-07-22
DOI
https://doi.org/10.1186/s40536-026-00306-w
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
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article

An appraisal of small area estimation methods to explore expansion of state subgroup reporting on NAEP

Sinan Yavuz, Paulina Grekov, David Bamat
Large-scale Assessments in Education
Statistical Methods and Bayesian Inference
article

An appraisal of small area estimation methods to explore expansion of state subgroup reporting on NAEP

Sinan Yavuz, Paulina Grekov, David Bamat
article en

Abstract

Abstract This study explores the use of Small Area Estimation (SAE) methods to expand reporting of state-level subgroup performance on National Assessment of Educational Progress (NAEP) assessments without requiring the assessment program to draw larger samples of students and expand test administration. To evaluate the SAE methods, small area estimates of mean subgroup achievement formed from samples too small to meet NAEP’s current reporting requirements are compared with design-based estimates of mean subgroup achievement that satisfy the program’s minimum sample size requirement for reporting. Results demonstrate that several SAE methods yield estimates of mean achievement that are more accurate and reliable than estimates of mean achievement that satisfy NAEP’s current reporting requirement. Policy implications and future directions for research are discussed.

Large-scale Assessments in EducationVol. 14(1)
American Institutes for Research (US), University of Wisconsin–Madison (US)
Quality Education
Openalex Percentile: Top 6%
Statistical Methods and Bayesian Inference
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