Within the last couple weeks, I have received two online survey requests. One survey came from a counselor based professional organization and the second came from a military veteran’s organization – I am members of both groups. The surveys arrived within a week of each other, I do not recall the specific goals of each, however the surveys collected demographic data then they continued to collect Likert scale information on awareness of services, member needs, and member opinions. What I found most striking about these surveys is in the demographic detail collected. In one of the surveys, gender was not a forced dichotomous choice. The survey had space for the surveyed member to write in their gender. When I discussed with others, they all assumed incorrectly that it was the counseling organization that had the option when in actuality it was the veteran’s organization. I was not as surprised as others because even though the military occasionally fails in its treatment of service members and veterans, once they make a decision like their recent inclusion and recognition of the LGBQT community, they tend to make changes immediately. This was actually the first survey I had the did not have the forced dichotomous choice. However, after completing the readings on CRT and methodology I wonder about the process that the researcher will use to analyze the collected data. I also considered what questions might arise, concerns and insights if this survey is viewed through a CRT lens.
To me this is a great example of how even in quantitative research subjectivity is part of the research process. While quantitative researchers may claim, “the data speaks for itself” as we recently discussed in class, in reality researchers must make choices in the process. Take the survey that allows open ended responses on gender. I am not an expert in the categories that may be submitted in response to this question however it is not hard for me to imagine that there may be a number of response possibilities that may have low number of respondents. In quantitative analysis if the n is small, often getting accurate statistical analysis becomes difficult. This may force the researcher to group categories that are not male or female together in one or more new categories or worse create an “other” category. Ultimately in the subjective process of grouping these respondents together, the ability to recognize the self-identification of individuals may be lost. Additionally, the results may have the potential of further “othering” the respondents. While the respondents to the survey may think that they are being recognized by their individualized identity, because of the requirements for statistical analysis this may be an inaccurate assumption.
Considering the reading on Critical Race Quantitative Intersectionality (CRQI) Covarrubias and Velez (2013) discussed several considerations when doing quantitative research from a CRT perspective. One consideration is that quantitative research and statistics is “based on a statistician’s understanding of the world” (Covarrubias and Velez, 2013, pp. 272). As mentioned previously we do not know the researcher(s) understanding of gender and yet the researcher(s) will make decisions about how to explore the data and present findings based on their personal understanding. Therefore understanding the researcher(s) positionality is a critical component to consider when viewing any reports generated from the data collected.
I wonder what the researcher(s) were planning when they used this open-ended question for gender. Were they hoping to gain insight on needs of a population they had not previously considered or were they simply trying to comply with the new military directive of inclusion without really thinking through the possibilities. What research exists that guides researcher on how to proceed ethically in statistical analysis for open-ended gender surveys?
The survey may or may not set a new course for surveys in the future however it is obvious that if researchers are to be inclusive and recognize individuals typically excluded from being able to self-identify, procedures that ethically and respectfully acknowledge their identity are needed. As a doctoral student that is drawn to quantitative and mixed-methods research, this survey raised many questions for me. While it does not appear (quick Google Scholar search) that there is a lot of research that has previously explored the methods, I hope that as we move forward that considerations and guidelines are published that reflect more inclusivity and provide guidance for future researchers.