EdSurvey: Installation and Use
EdSurvey is an R statistical package developed by AIR commissioned by the National Center for Educational Assessment (NCES). EdSurvey is tailored to the processing and analysis of NCES large-scale education data with appropriate procedures. Here is how to get started with EdSurvey:
Installing and Loading EdSurvey
Unless you already have R version 3.4.0 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. RStudio is available online from rstudio.
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:
Using the EdSurvey
There are several vignettes available to assist in analyzing NCES data. Some of the vignettes are written with National Assessment of Educational Progress (NAEP) Primer data as examples, and other vignettes are relevant to international assessment data.
- Using EdSurvey to Analyze NCES Data 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 the getData Function in EdSurvey describes the use of the EdSurvey package when extensive data manipulation is required before analysis.
- Using EdSurvey to Analyze TIMSS Data is an introduction to the methods used in analysis of international 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 for Trend Analysis describes the methods used in the EdSurvey package to conduct analyses of statistics that change over time in large-scale educational studies.
- Calculating Adjusted p-Values From EdSurvey Results describes the basics of adjusting p values to account for multiple comparisons.
- Exploratory Data Analysis on NCES Data provides examples of conducting exploratory data analysis on NAEP data.
- Using EdSurvey to Analyze NAEP Data With and Without Accommodations provides an overview of the use of NAEP data with accommodations and describes methods used to analyze these data.
- Producing LaTeX Tables From edsurveyTable Results With edsurveyTable2pdf details the creation of pdf summary tables from summary results using the edsurveyTable2pdf function.
Documents that describe the statistical methodology used in the EdSurvey package include the following:
- Analyses Using Achievement Levels Based on Plausible Values describes the methodological approaches for analyses using NAEP achievement levels.
- Methods and Overview of Using EdSurvey for Multivariate Regression details the estimation of multivariate regression models using mvrlm.sdf.
- Methods and Overview of Using EdSurvey for Running Wald Tests describes the use of the Wald test to exam the joint significance of regression coefficients using lm.sdf and glm.sdf.
- Methods Used for Estimating Mixed-Effects Models in EdSurvey describes the methods used to estimate mixed-effects models with plausible values and survey weights, and how to fit different types of mixed-effects models using the EdSurvey package.
- Methods Used for Estimating Percentiles in EdSurvey describes the methods used to estimate percentiles.
- Methods Used for Gap Analysis in EdSurvey covers the methods comparing the difference between two statistics for two groups that potentially share members.
- Statistical Methods Used in EdSurvey details the estimation of the statistics in the lm.sdf, achievementLevel, and edsurveyTable functions.
- Weighted and Unweighted Correlation Methods for Large-Scale Educational Assessment: wCorr Formulas introduces the methodology for computing the Pearson, Spearman, polyserial, polychoric, and tetrachoric correlations, with and without weights applied.
Contact and Bug Reports
Send us your questions and comments via e-mail:
NCES Project Officer:
Current team members: