Mastering Categorical Data Analysis with SAS

Overview

Learners will identify categorical data types, analyze distributions and associations, apply exact tests, construct logistic regression models, and evaluate model performance using SAS. This comprehensive course builds the full skill set needed to work confidently with categorical data across real-world analytical scenarios. Through hands-on demonstrations, learners gain practical experience generating frequency tables, interpreting crosstabulations, computing Fisher’s Exact p-values, and applying ordinal association techniques. The course then advances into logistic regression, where learners explore odds ratios, probability transformations, model assumptions, effect analysis, and multivariable modeling strategies. Learners also strengthen their predictive analytics skills by using backward elimination, ODS Graphics, honest assessment principles, and data-splitting frameworks. What makes this course unique is its end-to-end coverage of categorical analytics—from foundational concepts to advanced modeling—paired with step-by-step SAS guidance using real examples and interpretable outputs. By the end of the course, learners will be able to design, execute, and validate categorical models with confidence, accuracy, and professional-level competence.

Syllabus

Foundations of Categorical Data Analysis

This module introduces learners to the essential concepts of categorical data analysis using SAS. It builds foundational skills in identifying categorical variables, exploring their distributions, generating frequency tables, interpreting crosstabulations, and evaluating relationships through association tests. Learners gain the analytical groundwork needed to apply more advanced categorical techniques later in the course.

Mastering Exact Tests & Advanced Frequency Tools

This module equips learners with deeper statistical tools for categorical analysis, including Fisher’s Exact Test, exact p-value computation, ordinal association measures, and advanced use of the SAS FREQ procedure. Learners also explore the importance of custom formatting and rank-based corrections for enhanced analytical clarity and accuracy.

Logistic Regression Essentials

This module introduces learners to the core principles of logistic regression, including odds ratios, probability transformations, the logit function, and the assumptions underpinning the modeling process. Learners progress through parameter estimation, reference category specification, and foundational model evaluation, gaining the skills needed to build reliable logistic models.

Advanced Logistic Modelling & Assessment

In this module, learners delve into multivariable logistic regression, categorical predictor coding, reference cell techniques, effect analysis, and visual diagnostics using ODS Graphics. The module concludes with advanced model validation practices, including backward elimination, honest assessment principles, data splitting strategies, and the creation of missing indicators.