Inflated Discrete Beta Regression Models for Likert and Discrete Rating Scale Outcomes
Cedric Taverne, Philippe Lambert

TL;DR
The paper introduces the Inflated Discrete Beta Regression (IDBR) model, which effectively captures the characteristics of Likert and discrete rating scales, including inflation, skewness, and bounded support, improving prediction accuracy.
Contribution
It proposes a novel IDBR model that jointly models mean, dispersion, and inflation probability for ordinal responses, addressing limitations of existing models.
Findings
IDBR outperforms competing models in prediction accuracy
It effectively models inflation and skewness in ordinal data
Application to Belgian political survey data demonstrates practical utility
Abstract
Discrete ordinal responses such as Likert scales are regularly proposed in questionnaires and used as dependent variable in modeling. The response distribution for such scales is always discrete, with bounded support and often skewed. In addition, one particular level of the scale is frequently inflated as it cumulates respondents who invariably choose that particular level (typically the middle or one extreme of the scale) without hesitation with those who chose that alternative but might have selected a neighboring one. The inflated discrete beta regression (IDBR) model addresses those four critical characteristics that have never been taken into account simultaneously by existing models. The mean and the dispersion of rates are jointly regressed on covariates using an underlying beta distribution. The probability that choosers of the inflated level invariably make that choice is also…
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Taxonomy
TopicsSurvey Sampling and Estimation Techniques · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
