Results 11 to 20 of about 526,028 (331)
Learning Bayesian Networks from Ordinal Data [PDF]
, 2020 Bayesian networks are a powerful framework for studying the dependency structure of variables in a complex system. The problem of learning Bayesian networks is tightly associated with the given data type. Ordinal data, such as stages of cancer, rating scale survey questions, and letter grades for exams, are ubiquitous in applied research.
Xiang Ge Luo, Giusi Moffa, Jack Kuipers
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Drawbacks of Normalization by Percentile Ranks in Citation Impact Studies [PDF]
Journal of Library and Information Studies, 2022 This paper discusses drawbacks of the percentile rank method for citation impact normalization which have hitherto been neglected in the bibliometrics literature.
Paul Donner
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Using the weighted Kendall Distance to analyze rank data in psychology [PDF]
Tutorials in Quantitative Methods for Psychology, 2021 Although the Kendall distance is a standard metric in computer science, it is less widely used in psychology. We demonstrate the usefulness of the Kendall distance for analyzing psychological data that take the form of ranks, lists, or orders of items ...
van Doorn, Johnny +2 more
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ORDINAL LOGISTIC REGRESSION MODEL AND CLASSIFICATION TREE ON ORDINAL RESPONSE DATA
Barekeng, 2022 Logistic regression (LR) is a model that associates the relationship between category-type response variables with quantitative or quantitative and qualitative predictor variables. The prediction of the LR model is in the form of probability.
Jajang Jajang +2 more
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Multiple Ordinal Correlation Based on Kendall’s Tau Measure: A Proposal
Mathematics, 2021 The joint analysis of various ordinal variables is necessary in many experimental studies within research fields such as sociology and psychology. Therefore, the necessary measures of multiple ordinal dependence must be easy to interpret and facilitate ...
Juan M. Muñoz-Pichardo +3 more
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Bivariate Distributions Underlying Responses to Ordinal Variables
Psych, 2021 The association between two ordinal variables can be expressed with a polychoric correlation coefficient. This coefficient is conventionally based on the assumption that responses to ordinal variables are generated by two underlying continuous latent ...
Laura Kolbe, Frans Oort, Suzanne Jak
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Model-Assisted and Model-Calibrated Estimation for Class Frequencies with Ordinal Outcomes
Revstat Statistical Journal, 2022 This paper considers new techniques for complex surveys in the case of estimation of proportions when the variable of interest has ordinal outcomes. Ordinal modelassisted and ordinal model-calibrated estimators are introduced for class frequencies in a ...
Maria del Mar Rueda +3 more
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Inequality Comparisons with Ordinal Data [PDF]
Review of Income and Wealth, 2019 Non‐intersection of appropriately defined Generalized Lorenz (GL) curves is equivalent to a unanimous ranking of distributions of ordinal data by all Cowell and Flachaire (Economica, 2017) indices of inequality and by a new index based on GL curve areas.
Stone Center on Socio-Economic Inequality +1 more
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Ordinalysis: Interpretability of multidimensional ordinal data
SoftwareX, 2023 Ordinalysis is a software that enables dimension reduction, visualization and quantitative ordinality analysis of ordinal data. It is provided as a standalone executable file with a video tutorial.
Mouad Zine-El-Abidine +2 more
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Frontiers in Marine Science, 2021 Species distribution model (SDM) is a crucial tool for forecasting ranges of species and mirroring habitat references and quality. Different types of species distribution data have been commonly used in SDMs regarding different purposes and availability,
Jing Luan +7 more
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