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

Oregon Convention Center 

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.

mixed-mode

mode effects

multimode design

measurement error

nonresponse

data quality 

Survey Research Methods Section