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Exploring Mode Effect Adjustment Approaches for a Web and Face-to-face Survey

Abstract Number:

2996 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Brian Wells (1), Sara Lafia (1), Martha McRoy (1)

Institutions:

(1) NORC at the University of Chicago, N/A

Co-Author(s):

Sara Lafia  
NORC at the University of Chicago
Martha McRoy  
NORC at the University of Chicago

First Author:

Brian Wells  
NORC at the University of Chicago

Presenting Author:

Brian Wells  
NORC at the University of Chicago

Abstract Text:

Moving to a multimode survey design has many benefits over a face-to-face design, including making participation more convenient and reducing data collection costs. However, transitioning from a single, interviewer-administered mode to a mixed-mode design with self-administration can lead to measurement differences caused by mode effects. These changes make it hard for repeated cross-sectional surveys to maintain trends and may necessitate mode effect adjustments. This paper explores a set of mode effect adjustments for a recently transitioned mixed-mode survey.
The General Social Survey (GSS) was a face-to-face survey for nearly 50 years. Given the increasing cost of in-person collection and accelerated by the COVID-19 pandemic, the 2022 GSS was fielded as a multimode study, with respondents completing the survey via web, face-to-face, or phone. We examine the impact of various adjustments to key trend items including logistic regression, multiple imputation using chained equations, implied utility-multiple imputation, and adaptive mode adjustment. Our findings expand the mode effect literature and provide guidance to surveys with longstanding trends moving to a multimode design.

Keywords:

mixed-mode|mode effects|multimode design|measurement error|nonresponse|data quality

Sponsors:

Survey Research Methods Section

Tracks:

Survey Methodology/Modes

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