As indicated in “Beyond Rigor: Accurate Data”, there are a variety of gender linked triggers that can impact the quality of an evaluation. The language that is used when participants are asked to do a task or take a survey can impact participants' responses, as can the physical environment or whether demographic information is asked at the beginning or end of a survey. Even the choice to use the more culturally-based term “gender” rather than the more biologically- based term “sex”1 can have an impact. The cultural norms that define “feminine” and “masculine” behavior are powerful. Evaluators need to be aware of gender stereotypes and actively work to reduce or eliminate them in all aspects of the evaluation.
Evaluators need to remember that women are not a homogeneous group and neither are men. Rather than just looking at sex or gender, evaluators need to make decisions about what other demographic information should be included in the evaluation. Because of the dramatic change in gender roles in the United States in the past 30 years, individual age can be an important variable to consider when looking at gender. Individual perceptions and experiences with Science, Technology, Engineering and Mathematics (STEM) education and training, and with women in STEM can be quite different for 20 year olds than for 40 year olds. Since issues and experiences of women and men of color can be quite different than those of White women and men, race and ethnicity are other important variables to consider in the analysis. Based on the evaluation questions and the populations being served, other demographic variables such as geographic location, type of educational institution, or military service might need to be included.
Practically speaking, because women are much more apt to change their names, it is more difficult to track women participants. If follow-up is going to be done, evaluators should consider collecting information that is less apt to change such as parents' names and contact information, personal cell phone numbers, and e-mail addresses.
While women comprise 47% of the workforce2, there remain some differences in women and men's work-life patterns including some women leaving the workforce for a time and then returning. Evaluation measures need to be constructed so that any such differences come out and can be used in the analysis as appropriate.
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