What is “maximum representation”?
The concept and practice of “maximum representation” was generated by the OMAD assessment/data personnel (Gene Kim and Arlyn Arquiza) around 2010, when we found out the traditional data query methods inadvertently excluded some of the multiracial/multiethnic students to specific events, and possible communities within UW.
“The concept AND practice of maximum representation is utilized/operates under the premise, one unique student can represent and possess two or more ethnic/racial identities, and we are to respect and include these students’ multiple identities in order to enhance the probability of inclusion to various academic and non-academic communities/memberships/opportunities.”
To put it simply, maximum representation addresses some of the concerns related to the “two or more races” category and Hispanic/Latinx “override” from the National Center for Education Statistics (Integrated Postsecondary Education Data System).
For example, when a single unique multiracial/multiethnic graduating senior who self-identified as African American and Native American and Pacific Islander, via traditional data query method may have only received one electronic invitation to the black graduation event. Utilizing the concept and the data query method of maximum representation (via R and Python), our OMAD assessment data personnel (Gene Kim and Arlyn Arquiza) made sure our multiracial/multiethnic graduating seniors mentioned received multiple electronic invitations to all three (African American, Native American, and Pacific Islander) graduation events.
As noted, the concept and practice of maximum representation is a method where we count/include/respect the person’s MULTIPLE racial/ethnic identities independently from counting the number of unique individuals.
Maximum representation have also been utilized for reporting diversity in enrollment, graduation rates, and admissions data analysis as well as to for the past several years by the Assessment and Research at OMAD (AROMAD) team members.
As an example, please observe the simple hypothetical bar graph below. For IPEDS standard, there could be an AI/AN population below who self-identified as AI/AN only (Where there are only 14 students), however, if you point your curser to the darker blue rectangle, it provides the “maximum representation” of AI/AN students who have self-identified as AN/AN only as well as AI/AN and Black, AI/AN and Filipino, AI/AN & PI & Black and so on, where the count of representation is 36 instead of 14. When operating in maximum representation, obviously we cannot utilize percentages since one unique student can represent multiple population groups.