Innovating How Users Access NCES Data

Key Functions

The key functions of EdSurvey Version 4.0.7 include the following: 

• Analyzing achievement levels and benchmarks for NAEP and international assessment data
• Correlating variables, including Pearson, Spearman, polyserial, polychoric, and correlation between plausible values, with or without weights applied
• Exploring data using methods to better understand survey attributes and search for variables and levels in codebooks
• Manipulating data, such as subsetting and merging data, as well as renaming and recoding variables
• Processing data, including downloading publicly available data and reading data in R
• Fitting New Conditioning Models direct estimation of student scale scores using the marginal maximum likelihood regression estimation method, an alternative method to the plausible values approach
• Drawing plausible values to enable researchers to use new data that have been merged onto NCES’s existing data and then use a marginal maximum likelihood model directly; researchers can run any EdSurvey analytical function (e.g., summary tables, regressions) with the new plausible values, which further expands how they can work with NCES data after merging in data beyond information contained on existing surveys
• Conducting gap analyses to compare the average, percentile, achievement level, or percentage of survey responses between two groups that potentially share members
• Running linear regression models with or without plausible values as the dependent variable
• Running logistic regression models that allow either a discrete variable or dichotomized plausible values as the dependent variable
• Conducting multilevel modeling (linear and binomial) that use weights at multiple levels and allow plausible values in the dependent variable
• Running quantile regression models that fit a quantile regression model that use weights and variance estimates appropriate for the data
• Running multivariate regression models that extend multiple linear regression to include models with multiple outcome variables
• Calculating percentiles of numeric variables or plausible values
• Implementing the NAEP linking error method to incorporate linking error in variance estimation for NAEP assessments during the transition year from paper-based assessment to digitally based assessment
• Suggesting weights to assist researchers in deciding which weight to use for analyses of ECLS-K:2011 data
• Providing summary statistics, including unweighted and weighted totals; conditional means; percentage of respondents in a category (conditional on an ancillary categorical variable or on the interactions of an arbitrary number of categorical variables); and estimation of scale score means based on plausible values

As the development of EdSurvey progresses, several additional functions like item responses theory and other statistical methods, will be added to the package.

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Guides and Tutorials

• Analyzing NCES Data Using EdSurvey: A User’s Guide
• Introduction to using EdSurvey R Package for Analyzing NCES Large-Scale Data Sets
• EdSurvey: Analyzing Large-Scale Assessment Data – Using NAEP and TIMSS Data as Examples
• EdSurvey: Analysis of the Early Childhood Longitudinal Study, Kindergarten Class of 2010‒11
• Using EdSurvey to Analyse PIAAC Data

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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), which covers topics such as 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), which covers topics such as 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 TIMSS Data (PDF), which 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), which provides an overview of handling NAEP assessment data with and without accommodations and describes methods used to analyze this data.

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Task-Specific Walkthroughs

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

• NAEP DBA-PBA Linking Error With EdSurvey (PDF), which describes how to use EdSurvey to calculate the linking error in NAEP assessments that have combined DBA and PBA formats as well as the NCES method that EdSurvey uses to calculate the linking error.
• Installing the EdSurvey Package on a Restricted-Use Data Computer (PDF), which provides guidance on how to install EdSurvey on a restricted-use data computer without an internet connection.
• Converting Text Data File(s) With Companion SPSS Script to SPSS Data File Format (PDF), which details the process of converting a data file and SPSS script to an SPSS data file for use with EdSurvey
• Using the getData Function in EdSurvey (PDF), which describes the use of the EdSurvey package when extensive data manipulation is required before analysis.
• Using EdSurvey for Trend Analysis (PDF), which describes the methods used in the EdSurvey package to conduct analyses of statistics that change over time in large-scale educational studies.
• Exploratory Data Analysis on NCES Data (PDF), which provides examples of conducting exploratory data analysis on NAEP data
• Calculating Adjusted p-Values From EdSurvey Results (PDF), which describes the basics of adjusting p-values to account for multiple comparisons.
• Producing LaTeX Tables From edsurveyTable Results With edsurveyTable2pdf (PDF), which details the creation of PDF summary tables from summary results using the edsurveyTable2pdf function.

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Methodology Resources

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

• Statistical Methods (PDF), which details estimation procedures of the statistics in the lm.sdf, achievementLevel, and edsurveyTable functions.
• Analyses Using Achievement Levels Based on Plausible Values, which describes the methodology for NAEP achievement level estimation.
• Gap Analysis (PDF), which covers methods for comparing the difference between two statistics for two groups that potentially share members.
• Estimating Percentiles (PDF), which describes the methods used to estimate percentiles.
• Estimating Mixed-Effects Models (PDF), which 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.
• Multivariate Regression (PDF), which details the estimation of multivariate regression models using mvrlm.sdf.
• Running Wald Tests (PDF), which details the use of the Wald test to exam the joint significance of regression coefficients using lm.sdf and glm.sdf.
• Weighted and Unweighted Correlation Methods for Large-Scale Educational Assessment: wCorr Formulas, which 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, and presents simulation evidence to show correctness of the methods, including an examination of bias and consistency.

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