If individuals are classified into ordered categories, which are defined from the outset, it may happen that the majority belong to a single category. If a market researcher is interested in the correlation between the classification and individual characteristics, then the natural question is whether one needs to collect data for all individuals in that particular category. This question is dealt within the context of the ordered logit model. It is shown that there is no need to consider all those individuals and that a fraction of the individuals is needed. All that is required is a simple modification of the log likelihood, which is based on Bayes’ theorem. The proposed method is illustrated using simulated data and data concerning risk profiles of customers of an investment bank.

Additional Metadata
Keywords Bayes’ theorem, ordered logit model, selective sampling
Persistent URL dx.doi.org/10.1016/S0167-9473(02)00063-4, hdl.handle.net/1765/11476
Journal Computational Statistics & Data Analysis
Citation
Fok, D, & Franses, Ph.H.B.F. (2002). Ordered Logit Analysis For Selectively Sampled Data. Computational Statistics & Data Analysis, 40(3), 477–497. doi:10.1016/S0167-9473(02)00063-4