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The purpose of the project is to identify how to measure various types of institutional support as it pertains to underrepresented and underserved populations in colleges of engineering and science. We are grounding this investigation in the Model of Co-Curricular Support, a conceptual framework that emphasizes the breadth of assistance currently used to support undergraduate students in engineering and science. The results from our study will help prioritize the elements of institutional support that should appear somewhere in a college’s suite of support efforts to improve engineering and science learning environments and design effective programs, activities, and services. Our poster will present: 1) an overview of the instrument development process; 2) evaluation of the prototype for face and content validity from students and experts; and 3) instrument revision and data collection to determine test validity and reliability across varied institutional contexts. In evaluating the initial survey, we included multiple rounds of feedback from students and experts, receiving feedback from 46 participants (38 students, 8 administrators). We intentionally sampled for representation across engineering and science colleges; gender identity; race/ethnicity; international student status; and transfer student status. The instrument was deployed for the first time in Spring 2018 to the institutional project partners at three universities. It was completed by 722 students: 598 from University 1, 51 from University 2, and 123 from University 3. We tested the construct validity of these responses using a minimum residuals exploratory factor analysis and correlation. A preliminary data analysis shows evidence of differences in perception on types of support college of engineering and college of science students experience. The findings of this preliminary analysis were used to revise the instrument further prior to the next round of testing. Our target sample for the next instrument deployment is 2,000 students, so we will survey ~13,000 students based on a 15% anticipated response rate. Following data collection, we will use confirmatory factor analysis to continue establishing construct validity and report on the stability of constructs emerging from our piloting on a new student sample(s). We will also investigate differences across these constructs by subpopulations of students.