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Categorical data analysis as a book category covers methods and applications for analyzing data in which the outcomes are discrete labels rather than continuous measurements — for example yes/no responses, disease categories, survey choices, or ordered ratings. Titles range from introductory textbooks and applied handbooks to advanced references and software guides, and they explain topics such as contingency tables, logistic and multinomial regression, log‑linear and generalized linear models, ordinal methods, model selection and diagnostics, and techniques for clustered or longitudinal categorical data. Books in this category emphasize practical interpretation, study design, and real‑world examples from fields like medicine, social sciences, biology, and marketing, making them useful for students, researchers, and data analysts working with nonnumeric outcome variables.

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