NCES Data R Project - EdSurvey

EdSurvey is an R statistical package designed for the analysis of national and international education data from the National Center for Education Statistics (NCES). The released EdSurvey Version 2.7 includes the following data sources:

  • National Assessment of Educational Progress (NAEP) - up to 2019 NAEP
  • Trends in International Mathematics and Science Study (TIMSS) and TIMSS Advanced - up to 2019 TIMSS
  • Progress in International Reading Literacy Study (PIRLS) and ePIRLS - up to 2016 PIRLS
  • International Computer and Information Literacy Study (ICILS) - up to 2018 ICILS
  • International Civic and Citizenship Education Study (ICCS) - up to 2016 ICCS
  • 1999 Civic Education Study (CivEd)
  • Programme for International Student Assessment (PISA) - up to 2018 PISA
  • PISA Young Adult Follow-up Study
  • Programme for the International Assessment of Adult Competencies (PIAAC) - up to Cycle 1 - Rounds 1 to 3 (2017)
  • Teaching and Learning International Survey (TALIS) - up to 2018
  • Early Childhood Longitudinal Studies (ECLS-K: 1998, ECLS-K: 2011, ECLS-B)
  • Education Longitudinal Study of 2002 (ELS)
  • High School Longitudinal Study of 2009 (HSLS)
  • Beginning Teacher Longitudinal Study (BTLS)

EdSurvey is developed by AIR, commissioned by the NCES. EdSurvey is tailored to the processing and analysis of NCES large-scale education data with appropriate procedures to analyze these data efficiently—taking into account their complex sample survey design and the use of plausible values.

Here is how to get started with EdSurvey:

Installing and Loading EdSurvey

Unless you already have R version 3.5.0 or later, install the latest R version. Users also may want to install RStudio desktop, which has an interface that many find easier to follow.

Inside R, run the following command to install EdSurvey as well as its package dependencies:


Once the package is successfully installed, EdSurvey can be loaded with the following command:


Key Functions

The key functions of EdSurvey Version 2.7 include:

  • data processing, including downloading publicly available data and reading data in R;
  • data manipulation, such as the subsetting and merging of data, as well as renaming and recoding variables;
  • data exploration, including methods to better understand survey attributes and search for variables and levels in codebooks;
  • summary statistics, including unweighted and weighted totals, conditional means, and the percentage of respondents in a category (conditional on an ancillary categorical variable or on the interactions of an arbitrary number of categorical variables), estimation of scale score means based on plausible values;
  • linear regression with or without plausible values as the dependent variable;
  • logistic regression that allows either a discrete variable or dichotomized plausible values as the dependent variable;
  • multilevel models that use weights at multiple levels and allowing plausible values in the dependent variable;
  • direct estimation that estimates student scale scores using the marginal maximum likelihood regression estimation method. An alternative method to the plausible values approach;
  • gap analysis that compares the average, percentile, achievement level, or percentage of survey responses between two groups that potentially share members;
  • percentile that calculates the percentiles of a numeric variable or plausible values;
  • analysis of achievement levels and benchmarks for NAEP and international assessment data;
  • correlations, including Pearson, Spearman, polyserial, polychoric, and correlation between plausible values, with or without weights applied;
  • multivariate regression that extends multiple linear regression to include models with multiple outcome variables
  • quantile regression that fits a quantile regression model that uses weights and variance estimates appropriate for the data; and
  • NAEP linking error method that incorporates linking error in variance estimation for NAEP assessments during transition year from paper-based assessment to digitally based assessment.

As the development of EdSurvey progresses, several additional functions, such as IRT and other statistical methods will be added to the package.

Technical Papers

Book and Journal Publication

Bailey, P., Lee, M., Nguyen, T., & Zhang, T. (2020). Using EdSurvey to Analyse PIAAC Data. In Large-Scale Cognitive Assessment (pp. 209-237). Springer, Cham.

Data Set Specific Overviews

Documents that describe the analysis of specific survey data in the EdSurvey package include the following:

  • Using EdSurvey to Analyze ECLS-K:2011 Data (PDF) describes the methods in analysis of NCES longitudinal data with ECLS-K:2011 data in examples. The vignette covers topics including preparing the R environment, downloading and processing the data, exploring and manipulating data, and running statistical analyses such as summary tables, correlations, and regression models.
  • Using EdSurvey to Analyze NCES Data: An Illustration of Analyzing NAEP Primer (PDF) describes the basics of using the EdSurvey package for analysis of NAEP data. This vignette covers an introduction to the EdSurvey package with topics such as preparing the R environment for processing, creating summary tables, calculating percentiles and achievement levels, running correlations, linear regression and logistic regression, and conducting gap analysis.
  • Using EdSurvey to Analyze TIMSS Data (PDF) describes the methods used in analysis of large-scale educational assessment programs such as Trends in International Mathematics and Science Study (TIMSS) using the EdSurvey package. The vignette covers topics such as preparing the R environment for processing, creating summary tables, running linear regression models, and correlating variables.
  • Using EdSurvey to Analyze NAEP Data With and Without Accommodations (PDF) provides an overview of the use of NAEP data with accommodations and describes methods used to analyze this data.

Task Specific Walkthroughs

Documents providing an overview of functions developed in the EdSurvey package include the following:

Methodology Resources

Documents that describe the statistical methodology used in the EdSurvey package include the following:

Contact and Bug Reports

Please report bugs and other issues on our GitHub repository at