More Comprehensive and Inclusive Approaches to Demographic Data Collection

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

Understanding First Generation College Student Experiences and Interaction with Belongingness, Identity, and Social Capital: An Explanatory Mixed Method Study

This master’s thesis is a mixed method explanatory study focusing on First Generation College student’s (FGS) engineering degree experiences. Constructs used to understand their experiences were future time perspective, belongingness, engineering identity, social capital, and social identity complexity. An upper level engineering students’ communications class was surveyed at a western land grant institution. Analysis showed FGS had more engineering belongingness than peers having at least one parent graduate college. The qualitative population was then upper level engineering FGS who reported high belongingness. Data showed the five interview participants communicated belongingness in terms of engineering identity. They became an engineer when they had experiences using engineering knowledge. Participants often accessed parents and family to make academic and career decisions, but some accessed more individuals (i.e. professors, engineers, peers). Lastly, participants appeared to compartmentalize their FGS identity to outside the engineering classroom while they formed their engineering identity through the degree program

Gendered Interests in Electrical, Computer, and Biomedical Engineering: Intersections with Career Outcome Expectations

The current study finds that female-identified students report stronger associations between “helping others” and interest in bioengineering/biomedical engineering than non-females, while they report less interest in electrical and computer engineering overall, with similar associations to factors such as “inventing/designing things” than non-females. Background: While women have made gains in STEM, electrical and computer engineering programs award 13% of their Bachelor\u27s degrees to women while bioengineering/biomedical engineering programs award over 40%. Prior work suggests that women\u27s persistent under-representation in electrical and computer engingeering may be due to them being drawn into other disciplines. Women persist in engineering at similar rates as men, so a better understanding of early college attitudes is needed. Research Questions: (1) How are career outcome expectations associated to electrical engineering, computer engineering, and bioengineering/biomedical engineering? (2) What are females\u27 interests in electrical engineering, computer engineering, and bioengineering/biomedical engineering? (3) Are outcome expectations and major interests distinct for female-identified students? Methodology: Regression analyses were conducted on multiply-imputed data of introductory engineering students at four public universities in the U.S. Findings: Students associate inventing/designing things and “developing new knowledge and skills” to electrical engineering, and associate inventing/designing things and “working with people” (negative) to computer engineering. Students associate helping others and “supervising others” (negative) to bioengineering/biomedical engineering. Female-identified students are less interested in electrical and computer engineering, more interested in bioengineering/biomedical engineering, and associate helping others to bioengineering/biomedical engineering more strongly