Level-of-effort paradata include information such as the number and timing of attempts and whether there was ever resistance on a sampled case. These types of data are very useful for predicting the probability of response. However, in order to be useful for nonresponse adjustment purposes, data from the sampling frame and paradata need to predict response and the survey variables of interest. Whether level-of-effort paradata will predict survey variables is an empirical question for any specific survey. We examine the utility of these data for nonresponse adjustment purposes in a large, national survey of health and financial measures. Through a series of models and comparisons of alternative weights, we conclude that although the level-of-effort paradata are very useful for predicting the probability of response, for this survey they are not predictive of key survey outcomes and are, therefore, excluded from the adjustment models.