Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach

Often providers of a program or a service have detailed information about their clients, but only very limited information about potential clients. Likewise, ecologists frequently have extensive knowledge regarding habitats where a given animal or plant species is known to be present, but they lack comparable information on habitats where they are certain not to be present. In epidemiology, comprehensive information is routinely collected about patients who have been diagnosed with a given disease; however, commensurate information may not be available for individuals who are known to be free of the disease. While it may be highly beneficial to learn about the determinants of participation (in a program or service) or presence (in a habitat or of a disease), the lack of a comparable sample of observations on subjects that are not participants (or that are non-present) precludes the application of standard qualitative response models, such as logit or probit. In this paper, we present some new qualitative response estimators that can be applied by combining information from a primary sample of participants with a general sample from the overall population. Our new estimators rival the best existing estimators for use control sampling. Furthermore, these new estimators can be applied to stratified samples even when the stratification criteria are unknown. The estimators are also readily generalized to accommodate polychotomous response problems. Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach. Available from: https://www.researchgate.net/publication/317731280_Modeling_Qualitative_Outcomes_by_Supplementing_Participant_Data_with_General_Population_Data_A_Calibrated_Qualitative_Response_Estimation_Approach [accessed Jun 28, 2017].