Exploring the Role of Categorical Variable Encoding Techniques in Statistical Modeling and Data Interpretation

Posted: Sep 12, 2025

• Eli Ward
• Elijah Rivera
• Ella Adams

Abstract

Categorical variables represent a fundamental component of statistical modeling across numerous disciplines, from social sciences to machine learning applications. The process of encoding these categorical variables into numerical representations suitable for statistical algorithms constitutes a critical preprocessing step that has received surprisingly limited systematic investigation. Traditional approaches to categorical encoding have primarily emphasized computational efficiency and model performance metrics, often overlooking the profound implications that encoding choices exert on statistical interpretation and analytical validity. This research addresses this significant gap by developing a comprehensive framework for evaluating categorical encoding techniques through multiple dimensions of statistical integrity and interpretative transparency.