Topic
Ordinal regression
About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.
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TL;DR: In this article, the authors introduce a method for filtering out overall agreement when a researcher's aim is to construct a latent class typology of respondents, that is, a latent-class ordinal regression model with random intercept, and identify segments in the population that differ in their relative preference of particular items over other items in the set.
Abstract: When rating questions are used to measure attitudes or values in survey research a researcher might want to control for the effect of overall agreement with the set of items that is rated The need for controlling for overall agreement arises when the set of items refers to conceptual opposite perspectives, when balanced sets of items are used, or when a researcher is interested in relative preferences rather than overall agreement In this paper, we introduce a method for filtering out overall agreement when a researcher's aim is to construct a latent class typology of respondents, that is, a latent-class ordinal regression model with random intercept With this approach segments in the population are identified that differ in their relative preference of particular items over other items in the set Examples are given on the concepts of locus of control, gender role attitudes and civil morality The examples demonstrate that when an overall agreement is present in the data, the method is able to detect it, and at the same time allows identifying latent classes of respondents that differ in their relative preference of the items being rated
32 citations
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TL;DR: The relationship between population-averaged and cluster-specific parameters for the binary logistic link appears to hold for analogous parameters under the cumulative logit link, and issues in the context of data from two cross-over clinical trials are addressed.
Abstract: We compare population-averaged and cluster-specific models for clustered ordinal data. We consider generalized estimating equations and constrained equations maximum likelihood estimation of population-averaged cumulative logit regression models, and mixed effects estimation of cluster-specific cumulative logit regression models. A previously reported relationship between population-averaged and cluster-specific parameters for the binary logistic link appears to hold for analogous parameters under the cumulative logit link. We address these issues in the context of data from two cross-over clinical trials.
32 citations
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TL;DR: This paper introduces the bipolar PROMETHEE method based on the bipolar Choquet integral, and proposes to apply the Robust Ordinal Regression (ROR) to elicit parameters compatible with preference information provided by the Decision Maker (DM).
Abstract: In this paper we extend the PROMETHEE methods to the case of interacting criteria on a bipolar scale, introducing the bipolar PROMETHEE method based on the bipolar Choquet integral. In order to elicit parameters compatible with preference information provided by the Decision Maker (DM), we propose to apply the Robust Ordinal Regression (ROR). ROR takes into account simultaneously all the sets of parameters compatible with the preference information provided by the DM considering a necessary and a possible preference relation.
32 citations
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TL;DR: A Cultural Consensus Theory approach for ordinal data is developed, leading to a new model for ordered polytomous data that introduces a novel way of measuring response biases and also measures consensus item values, a consensus response scale, item difficulty, and informant knowledge.
Abstract: A Cultural Consensus Theory approach for ordinal data is developed, leading to a new model for ordered polytomous data. The model introduces a novel way of measuring response biases and also measures consensus item values, a consensus response scale, item difficulty, and informant knowledge. The model is extended as a finite mixture model to fit both simulated and real multicultural data, in which subgroups of informants have different sets of consensus item values. The extension is thus a form of model-based clustering for ordinal data. The hierarchical Bayesian framework is utilized for inference, and two posterior predictive checks are developed to verify the central assumptions of the model.
32 citations
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TL;DR: The results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection, and propose a novel, alternative multivariate approach that overcomes limitations.
32 citations