Assessment of Zero Inflated Mixture Model for ordinal data

Excess of zeros is a commonly encountered phenomenon that limits the use of traditional regression models for analysing ordinal data in contexts where respondents express a graduated perception on a specific item or experiments identify levels of (de)increasing assessments. The zero counts could be due to either simply being absent (structural zeros) or present with low frequency but not observed because of sampling variation (sampling zeros). The focus of the contribution is on modelling ordinal data in both the case that a population has excess zero counts and also consists of several sub-populations in the non-zero counts. The proposed zero-inflated mixture models account for both excess of zeros and heterogeneity. It is tailored to discriminate between structured and unstructured zeros by setting particular emphasis on the uncertainty concerning the evaluation process. The performance of the proposed model is assessed through simulation studies and empirical survey data.