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Mixture Models for Ordinal Data [PDF]
Sociological Methods & Research, 2010 Cumulative probability models are widely used for the analysis of ordinal data. In this article the authors propose cumulative probability mixture models that allow the assumptions of the cumulative probability model to hold within subsamples of the data. The subsamples are defined in terms of latent class membership.
Breen, R., Luijkx, R.
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Mathematics, 2021 Several complex phenomena are measured with the use of tools in the form of a questionnaire where the values of criteria are assessed by the respondents using ordinal scales.
Ewa Roszkowska, Bartłomiej Jefmański
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Maximally selected chi-square statistics for at least ordinal scaled variables [PDF]
, 2005 The association between a binary variable Y and a variable X with an at least ordinal measurement scale might be examined by selecting a cutpoint in the range of X and then performing an association test for the obtained 2x2 contingency table using the ...
Boulesteix, Anne-Laure
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Fruit production is influenced by tree size and size‐asymmetric crowding in a wet tropical forest
Ecology and Evolution, 2019 In tropical forest communities, seedling recruitment can be limited by the number of fruit produced by adults. Fruit production tends to be highly unequal among trees of the same species, which may be due to environmental factors.
David M. Minor, Richard K. Kobe
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On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio
Risks, 2018 We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM).
Tatjana Miljkovic +1 more
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Comparing distributions of ordinal data [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2020 To compare distributions of ordinal data such as individuals’ responses on Likert-type scale variables summarizing subjective well-being, we should not apply the toolbox of methods developed for cardinal variables such as income. Instead, we should use an analogous toolbox that accounts for the ordinal nature of the responses.
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Journal of MultiDisciplinary Evaluation, 2015 Background: Evaluation work frequently utilizes factor analysis to establish the dimensionality, reliability, and stability of surveys. However, survey data is typically ordinal, violating the assumptions of most statistical methods, and thus is often ...
Megan Sanders +2 more
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Qualitative - Binary, Nominal and Ordinal Data Analysis in Medical Science
National Journal of Community Medicine, 2022 The outcome of any medical research is belonged to the human beings. The correct application of statistical test has its paramount importance. This article provides the details of categorical data analysis test with example and with its interpretation ...
Swati Patel
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Smoothing in Ordinal Regression: An Application to Sensory Data
Stats, 2021 The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive.
Ejike R. Ugba +2 more
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Semiparametric Modeling of Ordinal Data [PDF]
, 2000 Parametric models for categorical ordinal response variables, like the proportional odds model or the continuation ratio model, assume that the predictor is given as a linear form of covariates.
Kauermann, Göran, Tutz, Gerhard
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