May 1, 2024
3 minute read
Categorical data is a type of data that can be divided into distinct categories. It is often used to represent qualitative data, such as gender, race, or occupation. Categorical data can be either nominal or ordinal. Nominal data is data that can be divided into categories that have no inherent order. For example, gender is a nominal variable that can be divided into the categories of male and female. Ordinal data is data that can be divided into categories that have an inherent order. For example, occupation is an ordinal variable that can be divided into the categories of unskilled, semi-skilled, and skilled.
Why Learn Categorical Data?
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Find a path to becoming a Categorical Data. Learn more at:
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Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Categorical Data.
This comprehensive textbook lays out the foundations of categorical data analysis, providing a detailed overview of data collection, analysis, and interpretation techniques. It is highly relevant for gaining a broad understanding of the topic.
This highly acclaimed textbook covers both regression and multilevel/hierarchical models for analyzing categorical data. It offers a comprehensive overview of statistical modeling techniques, including Bayesian approaches.
This specialized book delves into the analysis of ordinal categorical data. It provides a comprehensive overview of models and techniques, such as proportional odds models and cumulative logit models, making it a valuable resource for researchers working with ordered categorical data.
This advanced textbook focuses on the statistical analysis of ordinal data. It provides a comprehensive overview of models and techniques, including nonparametric methods, making it a valuable resource for researchers working with complex ordinal data.
This specialized book covers statistical methods for analyzing ordinal data, which is often encountered in social science research. It provides a comprehensive overview of models and techniques for understanding and interpreting ordinal data.
This comprehensive reference focuses on statistical methods for analyzing rates and proportions, covering topics such as Poisson regression, logistic regression, and survival analysis. It provides a solid foundation for researchers working with categorical data involving rates or proportions.
Introduces the use of R software for categorical data analysis. It covers a wide range of topics, from basic descriptive statistics to advanced modeling techniques, making it a useful resource for both beginners and experienced researchers using R.
This user-friendly book presents categorical data analysis through real-world examples and case studies. It provides a practical approach to understanding and applying statistical techniques for categorical data.
This practical guide focuses on using Stata software for categorical data analysis. It provides clear instructions and examples for model estimation, interpretation, and diagnostic testing, making it a valuable resource for applied researchers.
This practical guide provides step-by-step instructions for conducting categorical data analysis using SPSS software. It covers a wide range of models and techniques, making it a valuable resource for researchers with limited statistical background.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/2z6vxw/categorical
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