Introduction to Data Integration for Combining Probability and Non-Probability Samples, AAPOR Short Course

Dr. Sixia Chen, Associate Professor in the Department of Biostatistics and Epidemiology, is facilitating a national level virtual course hosted by the AAPOR (American Association for Public Opinion Research) 

Introduction to Data Integration for Combining Probability and Non-Probability Samples

Virtual  |  May 1, 2023 |  2:00 pm – 5:30 pm (EST)

Presenter(s): Sixia Chen, The University of Oklahoma Health Sciences Center, Hudson College of Public Health

Registration: $100

Description: Non-probability samples have been used frequently in practice including education, medical study, and public opinion research. Due to selection bias, naïve estimates without adjustments by using non-probability samples may lead to misleading results. In this course, we will include the following topics: 1. Introduction to probability samples, non-probability samples, and their applications in practice; 2. Calibration weighting approach; 3. Propensity score weighting approaches; 4. Mass imputation approaches; 5. Hybrid approaches by combining both propensity score weighting and mass imputation approaches. For each of the previous topics, we will provide hands on exercises by using some real data applications including National Health Nutrition and Examination Survey, the Behavioral Risk Factor Surveillance System, and National Health Interview Survey by using SAS/R computational codes. Computational codes will be made publicly available for audience to use.

https://portal.aapor.org/integratedEvents/home/INTRODUCTION-TO-DATA-INTEGRATION-FOR-COMBINING-PROBABILITY-AND-NON-PROBABILITY-SAMPLES