Building on Tradition: Approaches to More Inclusive Data Analysis

Abstract

Institutional research and assessment depends heavily on our ability to characterize the students we study into categories and on our inclination to generalize the results. Although this work is necessary for understanding student experiences, it does present challenges for critical and inclusive approaches to data analysis. In this session, we will discuss common issues and solutions associated with inclusive data analysis by investigating a series of data analysis examples that feature small sample sizes for marginalized students. We will discuss traditional variable-centered versus person-centered methodological approaches, strategies for creating groups to use in comparative analyses, challenges in quantitatively capturing aspects of identity, and tips for communicating the results, validity, and data quality of such analyses to broad audiences.