Survey Data Analysis and Visualization in R

Brady West

Tuesday, July 11, 2017
12:00 - 1:30 PM CDT/ 1:00 - 2:30 PM EDT/ 10:00 - 11:30 AM PDT

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About This Course:

 

This webinar will present a practical overview of how to use specialized procedures in the R software (e.g., the functions in the survey package) to analyze and visualize survey data. The focus will be on practical examples of real analyses and discussion of R code and output, including techniques for visualizing both unweighted and weighted survey data. Particular attention will be paid to procedures that are designed to account for the complex design features of large national samples. Time will also be devoted to preparing for such analyses, including importing data from other packages and downloading the correct R packages.

 

Learning Objectives:

   

• Identify R packages and procedures that should be used for analyzing and visualizing survey data.
• Import survey data into R, download and install the correct packages, and then write R syntax to perform specific types of analyses and generate specific types of plots.
• Understand how to communicate the results of these analyses, and interpret both the estimates and the plots generated.

About the Instructor:

 
Brady T. West is a Research Associate Professor in the Survey Methodology Program, located within the Survey Research Center at the Institute for Social Research on the University of Michigan-Ann Arbor (U-M) campus. He also serves as a Statistical Consultant on the U-M Consulting for Statistics, Computing, and Analytics Research (CSCAR) team. He earned his PhD from the Michigan Program in Survey Methodology in 2011. Before that, he received an MA in Applied Statistics from the U-M Statistics Department in 2002, being recognized as an Outstanding First-year Applied Masters student, and a BS in Statistics with Highest Honors and Highest Distinction from the U-M Statistics Department in 2001. His current research interests include the implications of measurement error in auxiliary variables and survey paradata for survey estimation, survey nonresponse, interviewer variance, and multilevel regression models for clustered and longitudinal data. He is the lead author of a book comparing different statistical software packages in terms of their mixed-effects modeling procedures (Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition, Chapman Hall/CRC Press, 2014), and he is a co-author of a second book entitled Applied Survey Data Analysis (with Steven Heeringa and Pat Berglund), which was published by Chapman Hall in April 2010 and has a second edition in press that will be available in mid-2017.