Subgroup analysis is common in higher education research on diverse populations within larger datasets, where researchers will use processes like categorization, comparison, and interpretation. However, there are important issues related to how to best communicate the value of these studies to the public related to representation, social power, and utility of this research. Strategies such as disaggregation, effect coding, and significance testing allow researchers to highlight and contextualize success pathways in these types of studies. The purpose of this chapter is to provide definitions, examples, and tradeoffs for these research design processes, issues, and strategies when analyzing data from subgroups of larger datasets. The chapter begins with a discussion of research on subgroups and critical masses for analysis. Each subsequent section includes definitions and examples in higher education for these strategies. Undergirding the examination in each chapter are applied examples from the 2021 and 2022 data of the National Survey of Student Engagement (NSSE), investigating the engagement of disabled, queer, and unhoused students. Each section ends with tradeoffs to consider when using these tools.