Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.

Higher educated workers: better jobs but less satisfied?

Abstract: Purpose – The purpose of the paper is to clarify the mixed empirical results concerning the association between educational level and job satisfaction. It seeks to test whether the positive relationship between educational level and job satisfaction is caused by indicators of job quality.Design/methodology/approach – Three models are estimated. In the first model, the impact of the educational level on job satisfaction is examined using an ordinal regression analysis. The second model estimates the impact of the educational level on indicators of job quality, using the appropriate technique (OLS or binary logit). The third model reveals the “true” impact of the educational level on job satisfaction, when the job quality indicators are added as independent variables. Survey data on Flemish youth in their first job are used.Findings – The results show that higher educated workers are more satisfied than their lower educated counterparts, because they have a job of better quality. When one controls for all j...

Projection-Based Ensemble Learning for Ordinal Regression

Abstract: The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper proposes an ensemble methodology specifically adapted to this type of problem, which is based on computing different classification tasks through the formulation of different order hypotheses. Every single model is trained in order to distinguish between one given class (k) and all the remaining ones, while grouping them in those classes with a rank lower than k, and those with a rank higher than k. Therefore, it can be considered as a reformulation of the well-known one-versus-all scheme. The base algorithm for the ensemble could be any threshold (or even probabilistic) method, such as the ones selected in this paper: kernel discriminant analysis, support vector machines and logistic regression (LR) (all reformulated to deal with ordinal regression problems). The method is seen to be competitive when compared with other state-of-the-art methodologies (both ordinal and nominal), by using six measures and a total of 15 ordinal datasets. Furthermore, an additional set of experiments is used to study the potential scalability and interpretability of the proposed method when using LR as base methodology for the ensemble.

Measures of Association for Bivariate Ordinal Hypotheses

Abstract: The appropriate choice of a measure of association is more than merely a purist's concern, for the conclusions reached in the analysis of a given set of data can depend crucially on the measure employed. A general framework has been proposed for constructing measures of association for bivariate ordinal hypotheses. The concern is with the problem of a researcher, who has formulated a bivariate hypothesis—that is, a proposition asserting a relation between two variables—and must choose an appropriate measure of association. A bivariate ordinal hypothesis asserts a relation between two ordinal variables. Although ordinal variables have a number of unsatisfactory characteristics, they are likely to remain a prominent feature of empirical social research for some time to come. For ordinal data, numerical algebraic models are unavailable, and so three general types of ordinal relations are identified: no-reversals, asymmetric, and strict.