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American Association for Public Opinion Research

Webinar Details

Evaluating and Reducing Biases in Mixed Mode Survey Data

Thomas Klausch and Barry Schouten
Thursday, November 15, 2018
12:00 - 1:30 PM CST/ 1:00 - 2:30 PM EST/ 10:00 - 11:30 AM PST

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About This Course:

In recent years survey researchers have shown increased interest in using mixed-mode designs that combine more than one mode of administration for data collection in a survey. The primary motivations for using mixed-mode include increasing response rates, reducing selection bias, and saving on survey administration costs. A well-known problem of mixed-mode designs is that survey questions are associated with different measurement error bias when posed under different modes. These differences are a risk for comparability between population subgroups and in time. Survey practitioners using mixed mode survey data, therefore, are interested in understanding how estimates obtained from mode-specific respondent sets are affected by mode differences in selection or measurement bias. In this course, we describe statistical methodology that can be used to estimate and to reduce these bias components. We focus on the data collection design stage and the estimation stage, and assume questionnaires as optimized and fixed. We start by the definitions of selection and measurement effects and describe how these relate to survey biases. Subsequently, we give overviews on covariate based estimation, the instrumental variable method, the re-interview method, and time-series stabilization. Particular attention is given to the assumptions of the estimation approaches to enable practitioners to decide which methodology is suitable for their design.

 

Webinar Level:
Introductory

 

Learning Objectives:   

  • Selection, measurement, and total effects and how they relate to survey biases
  • Currently available options for estimating or stabilizing mode effects
  • The assumptions of the various methods and how to scrutinize them in practice

About the Instructors:

 
Thomas Klausch, PhD, is post-doctoral researcher at the Department of Epidemiology and Biostatistics at Amsterdam University Medical Centers. He has worked on optimal treatment assignment, causal inference techniques, nonresponse, and measurement error in the context of survey statistics and clinical research. Thomas’s work on mixed-mode surveys has been published e.g. in the Journal of the Royal Statistical Society: Series A and Journal of Survey Statistics and Methodology.

 
Barry Schouten, PhD, is senior methodologist at Statistics Netherlands and professor at Utrecht University with a special chair on Mixed-mode surveys. He worked on methodology for nonresponse analysis, reduction and adjustment before turning to design and analysis of multi-mode surveys. Since 2016, he is one of the coordinators of the WIN program in which Utrecht University and Statistics Netherlands investigate ICT innovation using smartphones, sensors and wearables.