Ordinal responses in the form of ratings arise frequently in social sciences, marketing and business applications where preferences, opinions and perceptions play a major role. Main examples concern customers/users' satisfaction analysis where it is common to collect rater's evaluation on a hedonic scale, along with a set of covariates (both categorical and quantitative) that characterize the respondent and/or the item/service. In this framework, the ordinal nature of the response has to be properly taken into account when the interest is in the understanding of different response patterns in terms of subjects' covariates. In this spirit, a model-based tree procedure for ordinal scores is illustrated: its structure is based on a class of mixture models for ordinal rating data that implies a twofold analysis in terms of feeling and uncertainty and effective graphical visualization of results. The flexibility of the chosen modelling framework entails that the splitting criterion can be customized according to the purposes of the study and the available data, without disregarding uncertainty. Thus, the selection of variables yielding to the best partitioning results is driven by fitting measures or classical likelihood and deviance measurements, for instance. The contribution proposes to investigate the features of varying decision rules and thus implicitly addresses the problem of selecting the model-based tree that provides the most adequate and satisfying overview of response profiles. Comparison with alternative model-based approaches is also outlined.

On the choice of a model-based tree for ordinal scores / Cappelli, Carmela; Simone, Rosaria; DI IORIO, Francesca. - (2018). (Intervento presentato al convegno 5th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop tenutosi a Creta , Grecia nel 12-16 Giugno 2018).

On the choice of a model-based tree for ordinal scores

Cappelli Carmela
;
Simone Rosaria;Di Iorio Francesca
2018

Abstract

Ordinal responses in the form of ratings arise frequently in social sciences, marketing and business applications where preferences, opinions and perceptions play a major role. Main examples concern customers/users' satisfaction analysis where it is common to collect rater's evaluation on a hedonic scale, along with a set of covariates (both categorical and quantitative) that characterize the respondent and/or the item/service. In this framework, the ordinal nature of the response has to be properly taken into account when the interest is in the understanding of different response patterns in terms of subjects' covariates. In this spirit, a model-based tree procedure for ordinal scores is illustrated: its structure is based on a class of mixture models for ordinal rating data that implies a twofold analysis in terms of feeling and uncertainty and effective graphical visualization of results. The flexibility of the chosen modelling framework entails that the splitting criterion can be customized according to the purposes of the study and the available data, without disregarding uncertainty. Thus, the selection of variables yielding to the best partitioning results is driven by fitting measures or classical likelihood and deviance measurements, for instance. The contribution proposes to investigate the features of varying decision rules and thus implicitly addresses the problem of selecting the model-based tree that provides the most adequate and satisfying overview of response profiles. Comparison with alternative model-based approaches is also outlined.
2018
9786185180294
On the choice of a model-based tree for ordinal scores / Cappelli, Carmela; Simone, Rosaria; DI IORIO, Francesca. - (2018). (Intervento presentato al convegno 5th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop tenutosi a Creta , Grecia nel 12-16 Giugno 2018).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/729328
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