Towards Ordinal Data Science [PDF]
Transactions on Graph Data and Knowledge, 2023 Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small.
Stumme, Gerd +2 more
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Intuitionistic Fuzzy Synthetic Measure on the Basis of Survey Responses and Aggregated Ordinal Data [PDF]
Entropy, 2021 The paper addresses the problem of complex socio-economic phenomena assessment using questionnaire surveys. The data are represented on an ordinal scale; the object assessments may contain positive, negative, no answers, a “difficult to say” or “no ...
Bartłomiej Jefmański +2 more
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Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data [PDF]
Frontiers in Artificial Intelligence, 2021 Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets.
Alexandra L. Young +15 more
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Entropy-Based Uncertainty-Aware Exploratory Factor Analysis for Ordinal Data: Application to Tramway Cultural Tourism Evaluation [PDF]
EntropyBackground: Perception-based evaluation using Likert-scale survey data is widely applied in tourism and transport research, yet conventional point-valued encoding imposes artificial precision and overlooks ambiguity between adjacent ordinal categories ...
Jiaozi Pu, Yaxin Shi
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Graphical Models for Ordinal Data. [PDF]
J Comput Graph Stat, 2015 A graphical model for ordinal variables is considered, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian distribution. The relationships between these ordinal variables are then described by the underlying Gaussian graphical model and can be inferred by estimating the ...
Guo J, Levina E, Michailidis G, Zhu J.
europepmc +4 more sources
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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The Missing Medians: Exclusion of Ordinal Data from Meta-Analyses. [PDF]
PLoS ONE, 2015 BackgroundMeta-analyses are considered the gold standard of evidence-based health care, and are used to guide clinical decisions and health policy. A major limitation of current meta-analysis techniques is their inability to pool ordinal data.
Toby B Cumming +2 more
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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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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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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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