Analyzing NAEP Data Using R

Session Type: Professional Development Course

Abstract

This course will introduce the unique design features of National Assessment of Educational Progress (NAEP) data to researchers and provide guidance in data analysis strategies that they require, including the selection and use of appropriate plausible values, sampling weights, and variance estimation procedures (i.e., jackknife approaches). The course will provide participants with hands-on practice training in analyzing public-use NAEP data files using the R package EdSurvey, which was developed for analyzing national and international large-scale assessment data with complex psychometric and sampling designs. The knowledge and analytic approach presented in this course can be applied to analyzing other large-scale data with plausible values. This course is designed for individuals in government, universities, the private sector, and nonprofit organizations who are interested in learning how to analyze large-scale assessment data, especially NAEP and NAEP-like data. Participants should have at least basic knowledge of R software (e.g., have taken an entry level training on R programming) as well as statistical techniques including statistical inference and multiple regression. Having working knowledge of Item Response Theory and sampling theory is preferred. Participants need to bring a laptop preloaded with the latest version of the R software to participate in the hands-on portion.

Sub Unit

• Professional Development and Training Committee

Instructors

• Emmanuel Sikali, U.S. Department of Education

• Paul Dean Bailey, American Institutes for Research

• Lauren Harrell, National Center for Education Statistics

• Ting Zhang, American Institutes for Research