Analysing interviewer call record data using a multilevel discrete-time event history modleling approach

In recent years, survey agencies have started to collect detailed call record data, including information on the timing and outcome of each interviewer call to a household. In interview-based household surveys, such information may be used to inform effective interviewer calling behaviours, critical in achieving cooperation and reducing the likelihood of refusal. However, call record data can be complex and it is not always clear how best to model such data. In this paper, we present a general framework for the analysis of call-record data using multilevel event history modelling. A multilevel multinomial logistic regression approach is proposed in which the different possible outcomes at each call are modelled jointly, accounting for the clustering of calls within households and interviewers. Of particular interest are the influences of time-varying characteristics on the outcome of a call. The analysis of interviewer call record data is illustrated using paradata from several household surveys with the aim of informing the process leading to cooperation or refusal in face-to-face surveys.