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2012
In the next two chapters we consider the kinds of categorical outcomes frequently encountered in epidemiological practice. Categorical variables are those that take on discrete values only. When there are only two possible values, such as survival vs. death, exposed vs. unexposed, or diseased vs. non-diseased, we can refer to them as dichotomous.
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In the next two chapters we consider the kinds of categorical outcomes frequently encountered in epidemiological practice. Categorical variables are those that take on discrete values only. When there are only two possible values, such as survival vs. death, exposed vs. unexposed, or diseased vs. non-diseased, we can refer to them as dichotomous.
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
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Innovations in research and clinical care using patient‐generated health data
Ca-A Cancer Journal for Clinicians, 2020H S L Jim +2 more
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Graph Enhanced Fuzzy Clustering for Categorical Data Using a Bayesian Dissimilarity Measure
IEEE Transactions on Fuzzy Systems, 2023Chuanbin Zhang +2 more
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Improving Fit Indices in Structural Equation Modeling with Categorical Data
Multivariate Behavioral Research, 2021Victoria Savalei
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Fuzzy Rough Attribute Reduction for Categorical Data
IEEE Transactions on Fuzzy Systems, 2020Changzhong Wang +2 more
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A categorical data clustering framework on graph representation
Pattern Recognition, 2022Liang Bai, JiYe Liang
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Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models
Journal of Memory and Language, 2008T Florian Jaeger
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