To combat nonresponse, many surveys repeatedly follow up with nonrespondents, often targeting a response rate or fixed number of cases.  Acknowledging returns diminish with each wave of data collection, a recently proposed stopping rule in the literature aims at determining when the current wave’s impact on a key survey estimate is inconsequential.  The rule employs explicit imputation models, however, which require predictive covariates known for all sample units.  This paper describes a stopping rule similar in spirit but applicable to surveys that reweight respondent records to adjust for nonresponse.  The two methods are compared using data from a Web-based employee satisfaction survey.  The weighting rule proves more conservative in the sense that it dictates more waves of data collection should occur.  It is argued the difference is attributable to how the covariance of adjacent wave respondent data is incorporated.