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Response Propensity Modeling (RPM) is an empirical process that identifies a multivariate statistical model to predict the likelihood (propensity) that a given element in an initial sample will cooperate with a forthcoming survey request. This predicted probability ranges from 0 to 1 and reflects an element’s unique combination of characteristics that are expected to affect the relative likelihood of obtaining a response from the element. RPM is consistent with Leverage-Salience Theory in that it is used to tailor different recruitment strategies to different sampled elements, rather than using a one-size-fits-all (OSFA) approach whereby all sampled elements receive the same recruitment strategies. The appeal of using an RPM approach to allocating differential recruitment strategies is that, in theory, it should perform better than an OSFA approach, in terms of gaining a higher overall response rate, gaining a more representative unweighted final sample, and being more cost-effective. As described here, RPM fits into a Tailored Design Method of trying to most efficaciously utilize total survey costs to reduce total survey error. We explain what we mean by RPM, compare it to past work on allocating differential recruitment strategies within an initial sample, and explain the three-stage process used to identify, implement/test, and refine the response propensity model.

The Use of Response Propensity Modeling (RPM) for Allocating Differential Survey Recruitment Strategies: Purpose, Rationale, and Implementation