Dyadic analysis for multi-block data in sport surveys analytics

Analyzing sports data has become a challenging issue as it involves not standard data structures coming from several sources and with different formats, being often high dimensional and complex. This paper deals with a dyadic structure (athletes/coaches), characterized by a large number of manifest and latent variables. Data were collected in a survey administered within a joint project of University of Naples Federico II and Italian Swimmer Federation. The survey gathers information about psychosocial aspects influencing swimmers’ performance. The paper introduces a data processing method for dyadic data by presenting an alternative approach with respect to the current used models and provides an analysis of psychological factors affecting the actor/partner interdependence by means of a quantile regression. The obtained results could be an asset to design strategies and actions both for coaches and swimmers establishing an original use of statistical methods for analysing athletes psychological behaviour

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

Dummy covariates in CUB models

In this paper we discuss the use of dummy variables as sensible covariates in a class of statistical models which aim at explaining the subjects’ preferences with respect to several items. After a brief introduction to CUB models, the work considers statistical interpretations of dummy covariates. Then, a simulation study is performed to evaluate the power discrimination of an asymptotic test among sub-populations. Some empirical evidences and concluding remarks end the paper

Fitting measures for ordinal data models

A relevant issue for validating models is the assessment of goodness-of-fit and related measures of predictive ability. When data are nominal, and specifically ordinal, the main problem is the absence of a standard paradigm as in the regression framework for residual variability; in fact, several measures have been proposed. In this contribution we explore fitting measures for ordinal data when these are modelled by a mixture distribution. Some new indexes are evaluated and a comparison with previous proposals is performed by means of simulated and real data sets