USE OF THE LOGIT SCALING APPROACH TO TEST FOR RANK-ORDER AND FATIGUE EFFECTS IN STATED PREFERENCE DATA

The scaling approach is a statistical estimation method which allows f or differences in the amount of unexplained variation in different typ es of data which can then be used together in analysis. In recent year s, this approach has been tested and recommended in the context of com bining Stated Preference and Revealed Preference data. The paper provi des a description of the approach and a historical overview. The scali ng approach can also be used to identify systematic differences in the variance of choices within a single Stated Preference data set due to the way in which the hypothetical choice situations are presented or the responses are obtained. The paper presents the results of two case studies - one looking at rank order effect and the other at fatigue e ffect. Scale effects appear to exist in both cases: the amount of unex plained variance is shown to increase as rankings become lower, and as the number of pairwise choices completed becomes greater. The implica tions of these findings for the use of SP ranking tasks and repeated p airwise choice tasks are discussed.