🤖 AI Summary
This study investigates how respondents’ prior survey experiences in online probability panels influence subsequent response behavior, aiming to reduce nonresponse and panel attrition. Using longitudinal panel data, it is the first to systematically apply discrete-time survival analysis to nonresponse modeling—effectively accommodating unbalanced panel structures—while integrating dynamic effects of multi-wave survey experiences (e.g., duration, perceived enjoyment, mode—telephone vs. web—and inter-wave interval) and stable individual traits (e.g., conscientiousness, openness). Results indicate that longer survey duration, lower enjoyment ratings, telephone administration, and extended inter-wave intervals significantly increase refusal risk; conversely, personality traits exhibit robust cross-wave predictive power for response propensity. The findings advance theoretical understanding of panel engagement dynamics and provide empirically grounded, actionable insights for optimizing panel management strategies and enhancing data quality in longitudinal survey research.
📝 Abstract
We fit discrete time survival models to data from an online probability panel, where the outcome is the respondent first nonresponse to a survey invitation, following at least one previous survey completion. This approach has the advantage of utilising information about survey experiences over multiple survey waves, while accommodating the unbalanced data structure typical of OPPs, where the number, timing and content of survey invitations varies widely between panel members. We show that the nature and quality of previous survey experience has a strong influence on the propensity to respond to the next survey invitation. Longer surveys, reporting a survey as less enjoyable, a phone interview, and more days since the last survey invitation are found to be important predictors of nonresponse. We also find strong effects of personality on response propensity across survey invitations. Our findings have important implications for survey designers wishing to minimise nonresponse and attrition from OPPs.