Using Statistical Process Control to Understand Variation in Computer-Assisted Personal Interviewing Data

• Activity Number: 383
• Type: Invited
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Robyn Sirkis*+ and Matt Jans and Jim Dahlhamer and Renee M. Gindi and Benjamin Duffey
• Companies: U.S. Census Bureau and U.S. Census Bureau and Centers for Disease Control and Prevention and Centers for Disease Control and Prevention and NORC at the University of Chicago
• Keywords: Paradata ; Statistical Process Control ; NHIS

Abstract

This paper discusses research using statistical process control with paradata obtained during data collection for the National Health Interview Survey (NHIS). Statistical process control (SPC) involves using statistical techniques to measure and analyze variation in operational processes. The goal with this approach is not to simply monitor, but to improve the quality of the process over time. For this paper, we group interviewers into statistical clusters based on census tract level housing unit and respondent demographic characteristics and produce control charts which examine the variation of the process over time for each cluster. We address advanced SPC techniques such as multivariate charting. Indicators of data quality used in the paper are item nonresponse and interview duration. The charts are intended to demonstrate how survey managers can use paradata to monitor the data collection process using SPC principles and techniques.