Proxy Survey Cost Indicators in Interviewer-Administered Surveys: Are they Actually Correlated with Costs?

Abstract Survey design decisions are—by their very nature—tradeoffs between costs and errors. However, measuring costs is often difficult. Furthermore, surveys are growing more complex. Many surveys require that cost information be available to make decisions during data collection. These complexities create new challenges for monitoring and understanding survey costs. Often, survey cost information lags behind reporting of paradata. Furthermore, in some situations, the measurement of costs at the case level is difficult. Given the time lag in reporting cost information and the difficulty of assigning costs directly to cases, survey designers and managers have frequently turned to proxy indicators for cost. These proxy measures are often based upon level-of-effort paradata. An example of such a proxy cost indicator is the number of attempts per interview. Unfortunately, little is known about how accurately these proxy indicators actually mirror the true costs of the survey. In this article, we examine a set of these proxy indicators across several surveys with different designs, including different modes of interview. We examine the strength of correlation between these indicators and two different measures of costs—the total project cost and total interviewer hours. This article provides some initial evidence about the quality of these proxies as surrogates for the true costs using data from several different surveys with interviewer-administered modes (telephone, face to face) across three organizations (University of Michigan’s Survey Research Center, Westat, US Census Bureau). We find that some indicators (total attempts, total contacts, total completes, sample size) are correlated (average correlation ∼0.60) with total costs across several surveys. These same indicators are strongly correlated (average correlation ∼0.82) with total interviewer hours. For survey components, three indicators (total attempts, sample size, and total miles) are strongly correlated with both total costs (average correlation ∼0.77) and with total interviewer hours (average correlation ∼0.86).