NAEP Data in Focus: Examining the Research

The National Assessment of Educational Progress (NAEP), led by the National Center for Education Statistics within the Institute of Education Sciences, is the only nationally representative and continuing assessment of what America's students know and can do in various subject areas. AIR informs the NAEP program and the research community through our work, NAEP Data in Focus.

The NAEP Data in Focus working papers combine AIR’s expertise and experience not only with the National Assessment of Educational Progress (NAEP), but with other large-scale assessments and survey-based longitudinal studies. The papers are made available to researchers and the public.

NAEP is the largest nationally representative and continuing assessment of what America's students know and can do in various subject areas. Data from NAEP—the “Nation’s Report Card”—can provide insights into a wide range of topics, such as understanding the relationship between students' familiarity with computers and their performance, how NAEP mathematics assessments compare with international assessments, and comparisons of NAEP variables to those used in longitudinal studies.

AIR-NAEP researchers are experts in educational research, psychometrics, and statistics, and have been commissioned by the National Center for Education Statistics, which oversees all NAEP work, to provide expert advice and technical assistance on specific issues related to NAEP. This resource will be updated regularly with the latest AIR papers and work examining NAEP data.

AIR-NAEP Working Papers

Mathematics Motivation and its Relationship with Mathematics Performance: Evidence from the National Assessment for Educational Progress-High School Longitudinal Study of 2019 Overlap Sample

This study investigates how mathematics motivation (mathematics identity, mathematics self-efficacy, and mathematics interest) at grade 9 and 11 related to grade 12 NAEP mathematics performance using nationally representative data.  The overall SEM models reveal that mathematics motivation in grade 11 was statistically significantly associated with grade 12 NAEP mathematics achievement, after controlling for other variables in the model.

Choosing a College STEM Major: The Roles of Motivation, High School STEM Coursetaking, NAEP Mathematics Achievement, and Social Networks

The study uses nationally representative data to investigate how high school STEM motivation, STEM course taking, STEM achievement and social networks are associated with the decision of students who go on to enroll in 4-year colleges to choose a STEM major or not. The study findings highlight the important role of students’ motivation, particularly students’ STEM identity. Thinking and believing in oneself as a scientist and as a person who is good at mathematics were shown to be strongly associated with the choice of a STEM major.

U.S. National and State Trends in Educational Inequality Due to Socioeconomic Status: Evidence from the 2003–17 NAEP

This study examines the trends in educational inequality due to family socioeconomic status (SES) in the United States both at the national level and at the state-level. Specifically, the study focuses on the changes in achievement gaps between high and low SES students between 2003 and 2017 with an additional emphasis on performance trends of low-SES students over time.

Reading Motivation, Reading Achievement, and Reading Achievement Gaps: Evidence from the NAEP 2015 Reading Assessment

This study aims to understand the role that reading motivation plays in middle school reading achievement (including achievement gaps) by analyzing the 2015 grade 8 NAEP reading data. The study focuses on identifying the unique effects of student-level reading motivation and aggregated school-level mean reading motivation on reading achievement.

Weighted and Unweighted Correlation Methods for Large-Scale Educational Assessment: wCorr Formulas

This report introduces the methodology used by the wCorr R package for computing the Pearson, Spearman, polyserial, polyserial, polychoric and tetrachoric correlations, with and without weights applied. Simulation evidence is presented to show correctness of the methods, including an examination of the bias and consistency.

Analyses Using Achievement Levels Based on Plausible Values

Achievement levels are important yardsticks in the National Assessment of Educational Progress (NAEP) program. In this document, we provide a detailed description of the plausible value method and demonstrate the method by replicating the analyses using SAS and comparing the replicated analysis results with those produced with NAEP Data Explorer. It is intended to provide a complete document for NAEP data users to easily adapt the methodologies with other statistical software packages such as SAS, Stata, R, and so on.

A Comparison Study of the Program for International Student Assessment (PISA) 2012 and the National Assessment of Educational Progress (NAEP) 2013 Mathematics Assessments

This study compares the mathematics frameworks and item pools used in the National Assessment of Educational Progress (NAEP) with the Program for International Student Assessment (PISA) and vice versa. In addition to that, differences in item features between the two assessments are described.

Computer Familiarity and Its Relationship to Performance in Three NAEP Digital-Based Assessments

Because NAEP is transitioning its paper-and-pencil assessments to a digital format, it is important to understand the relationship between students' familiarity with computers and their performance. This study, which analyzed three of NAEP's early digital-based assessments, found that home computer access was positively related to student performance.

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Image of George Bohrnstedt
Senior Vice President and Institute Fellow
Managing Researcher