In this evidence-based practice paper, we discuss ways for researchers and educators to more sensitively, accurately, and effectively collect demographic information on surveys. Identifying variables that capture diversity more broadly is vital in understanding the variety of ways in which students participate in and experiencing engineering education. We frame this discussion through publically available statistics that suggest the potential error in common approaches employed for demographic collection. While basic questions about participants’ sex and ethnicity are standard items in assessment and data collection, these questions only develop a limited representation and potentially present an inaccurate accounting of students’ social identities and honest self-expression. Classic demographic measurement approaches classify students on broad, general, and historically driven elements of diversity typically defined by others rather than individual students. Unfortunately, simply asking a participant to self-identify their gender dichotomously or select from a pre-defined set of ethnicity options has the potential to record information that does not completely or accurately represent a student’s self-identified characteristics or a researchers latent purpose. Alternatively, asking questions via simple open-ended queries both maintains any problem represented in the phrasing of the question as well as presents a major loss in efficiency by requiring a post-collection coding step. In this paper we discuss three major topics through reviews of literature, emergent cultural norms, and suggestions for better practices. First, we will cover the framing of demographic questions to gather the intended information (i.e., differentiating how the student experiences the world and how the world experiences the student). Second, we address ordering of demographic questions and the extended capability provided by modern online collection tools. Finally, using the lessons of parts one and two we offer some examples of improved ways of collecting a variety of demographic information such as gender identity, ethnicity, language, sexual orientation, disability status, and socioeconomic status. The examples will show how researchers can be more sensitive to issues of diversity while at the same time improving research quality.
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