Topic
Ordinal regression
About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, an alternative to multiple regression that is appropriate when the dependent variable is ordinal is suggested, by treating the problem as one in discriminant analysis by discriminating the pairs of subjects whose ordinal relations are in one direction from those with relations in the other.
Abstract: An alternative to multiple regression that is appropriate when the dependent variable is ordinal is suggested. The goal of the system is to predict correctly as many as possible of the binary ordinal relations on the dependent variable. This can be done by treating the problem as one in discriminant analysis by discriminating the pairs of subjects whose ordinal relations are in one direction from those with relations in the other. The bases of prediction can be raw score differences on predictors, their rank differences, or their directions of difference. For each, it is possible to find a system of weights that approximately maximizes discrimination. These turn out to depend on the variables' co-variances, on their rank correlations, and on their tau correlations, respectively. It is also possible to estimate the odds that any given relation is in a particular direction. A solution for the weights that exactly maximizes probability of correct ordinal prediction is available in the case of predicting from directions of difference. An example is given.
24 citations
TL;DR: A multidimensional latent space representation with the purpose of relaxing this projection, where the different classes are arranged based on concentric hyperspheres, each class containing the previous classes in the ordinal scale.
24 citations
TL;DR: In this paper, the authors investigate user versus provider perceptions of MAS services using the DeLone and McLean information system success model and the theoretical lens of social perception theory, and find that there are significant perceptual differences about MAS service quality by users versus providers.
Abstract: Purpose – The purpose of this paper is to investigate user versus provider perceptions of management accounting system (MAS) services using the DeLone and McLean information system success model and the theoretical lens of social perception theory.Design/methodology/approach – Quantitative survey data were collected and analyzed using ordinal regression. Qualitative interview data concerning user‐provider perceptions of MAS service information quality, importance, use, and satisfaction were utilized to corroborate and explain the data analysis.Findings – The results suggest that there are significant perceptual differences about MAS service quality by users versus providers. For this organization, the paper identifies what these differences are, why they exist, and how organizations may identify and narrow identified gaps.Research limitations/implications – The paper is based on a case study that may not be generalizable to broader populations. It uses a cross‐sectional, correlational, self‐report survey,...
24 citations
TL;DR: In this article, a new measure of ordinal variation, the LSQ, is developed using a geometric representation involving the cumulative distribution function, and connections among it and previously suggested measures, the LOV, IOV, and COV, are clarified.
Abstract: A new measure of ordinal variation, the LSQ, is developed using a geometric representation involving the cumulative distribution function. Connections among it and previously suggested measures, the LOV, IOV, and COV, are clarified. This geometric perspective helps demonstrate that all these statistics measure the distance between the observed cumulative distribution and that corresponding to the maximally dispersed distribution, given the sample size and the number of categories for the ordinal variable. From this perspective, it is clear that none of these measures relies on supra-ordinal assumptions concerning intercategory distances. Recent questions concerning scale invariance and unreasonable values for these measures are also clarified.
24 citations