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B1372
Title: Modelling of dispersion and response styles in ordinal regression Authors:  Gerhard Tutz - LMU (Germany) [presenting]
Abstract: In ordinal regression the focus is typically on location effects, potential variation in the distribution of the probability mass over response categories referring to stronger or weaker concentration in the middle is mostly ignored. If dispersion effects are present but ignored goodness-of-fit suffers and, more severely, biased estimates of location effects are to be expected. A model specification is proposed that explicitly links varying dispersion to explanatory variables. It is able to explain why frequently some variables are found to have category-specific effects if the cumulative type model is used. For repeated measurements, which are used in survey-based research, dispersion effects may be seen as response styles that represent specific answering patterns of respondents. We consider an extension of the Partial Credit Model that explicitly accounts for response styles. A common problem in partial proportional odds models is the selection of the effect type, each covariate can be equipped with either a simple, global effect or a more flexible and complex effect which is specific to the response categories. A general penalty framework is proposed that allows for an automatic, data-driven selection between global and category-specific effects in all types of ordinal regression models.