Proposing a Quantitative Framework for Persona Set Diversity Measurement as a Pathway to Inclusive User Representation

Abstract

Personas represent user segments to support design decisions. A persona set, the collection of personas built from a user population, should cover the range of segments in that population. However, quantifying persona-set diversity remains understudied. To address this shortcoming, we introduce the Persona Set Diversity Measurement (PSDM) task and systematically evaluate five diversity metrics for versatility, consistency, holism, and scalability. Our experiments across three diversity levels (low, medium, high) of simulated persona datasets indicate that Rao’s Quadratic Entropy performed best among the five evaluated metrics under the tested conditions, a finding supported by additional tests with a real-world social media dataset on user attitudes. We make the source code available to quantify persona profile information from observed attributes and to measure diversity in any quantifiable persona set. This contribution provides researchers and practitioners with empirical tools to quantify and compare attribute variation across persona sets.