Published Date: March 05, 2024

Summary: This article evaluates the use of dynamic adaptive design methods to target outbound computer-assisted telephone interviewing (CATI) in the California Health Interview Survey (CHIS). CHIS 2022 implemented a dynamic adaptive design in which predictive models were used to end dialing early for some cases. 

For addresses that received outbound CATI follow-up, dialing was paused after three calls. A response propensity (RP) model was applied to predict the probability that the address would respond to continued dialing, based on the outcomes of the first three calls. Low-RP addresses were permanently retired with no additional dialing, while the rest continued through six or more attempts. Authors used a difference-in-difference design to evaluate the effect of the adaptive design on calling effort, completion rates, and the demographic composition of respondents. 

Findings: Authors find that the adaptive design reduced the mean number of calls per sampled unit by about 14% (relative to a modeled no-adaptive-design counterfactual) with a minimal reduction in the completion rate and no strong evidence of changes in the prevalence of target demographics. This suggests that RP modeling can meaningfully distinguish between addressed-based sample units for which additional dialing is and is not productive, helping to control outbound dialing costs without compromising sample representativeness.

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