Appropriate Design-Based Analysis of Complex Sample Survey Data
Wednesday, 5/22/2019, 9:00am to 12:00pm. ARCHIVED EVENT
Location: UM North Campus Research Complex Building 10, G063/G064
The University of Michigan Institute for Healthcare Policy and Innovation (IHPI) is excited to have Brady West speak on issues related to planning for and analyzing complex survey data.
This seminar will provide attendees with an overview of theoretically appropriate approaches to analyzing survey data collected from samples with complex designs. After a conceptual overview of the main issues that analysts need to account for when analyzing these types of data sets, we will discuss case studies in analytic error, examining the prevalence with which incorrect analyses occur in scientific publications and reviewing real examples of what can go wrong when performing these types of analyses incorrectly. We will follow this discussion with real examples of alternative approaches to analyzing data from the National Inpatient Sample, considering the implications of the alternative approaches for subsequent population inference related to descriptive parameters and regression models. Example SAS and Stata code will be provided and explained for all of the real examples.
If you would like to watch the live stream please visit: https://bluejeans.com/7343957307. Note that there are a limited number of streaming spaces, provided on a first come, first served basis.
If you need accommodations to participate in this event or have any questions, please contact email@example.com or firstname.lastname@example.org. Thanks!!!
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 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 effects, 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), the second edition of which was published by Chapman Hill in June 2017.