Predicting panel attrition using multiple operationalisations of response time

Abstract

Panel attrition is a major problem for panel survey infrastructures. When panelists attrit from a panel survey, the infrastructure is faced with (i) the costs of recruiting new respondents, (ii) a broken timeline of existing data, and (iii) potential nonresponse bias. Previous studies have shown that panel attrition can be predicted using respondents' response time. However, response time has been operationalised in multiple ways, such as (i) the number of days it takes respondents to participate, (ii) the number of contact attempts made by the data collection organisation, and (iii) the proportion of respondents who have participated prior to a given respondent. Due to the different operationalisations of response time, it is challenging to identify the best measurement to use for predicting panel attrition. In the present study, we used data from the GESIS Panel - which is a German probability-based mixed-mode (i.e., web and mail) panel survey - to compare different operationalisations of response time using multiple logistic random-effects models. We found both that the different operationalisations have similar relationships to attrition and that our models correctly predict a similar amount of attrition.