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“If you aren’t White, Asian or Indian, you aren’t an engineer”: racial micro aggressions in STEM education (Quantitative Article Critique)

Introduction

The USA is rapidly approaching the “majority-minority” tipping point. It is projected that by 2044, non-Hispanic White residents will make up less than half of the US population (Colby & Ortman, 2015). This article analyzes that within a period of time, USA will become diverse. Despite having demographic changes, white men, in particular, remain to be overrepresented in engineering and other science and technology courses. The readers will probably find the introduction of the article wordy and difficult to understand, and the main problem was never clearly stated. For the purpose of making the problem understandable, the authors should have inserted contexts regarding to the diversity of US and how it affects the passing rate of STEM courses in schools and should emphasize the racial micro aggressions itself. A study from Mccullagh (2016) cited examples of racial micro aggressions in higher education and would have been great in introducing the purpose of this study.

Review of the Literature

The authors combined both the literature and conceptual framing in order to denote contexts of racial climate framework in external and internal to the campus environment. External factors include the government and policy context, such as state and federal mechanisms for higher education funding as well as desegregation, diversity, and affirmative action policies that may significantly alter the postsecondary landscape at local and national levels (Lee, Collins, Hardwood, Mendenhall, Huntt, 2020, pp. 2). Furthermore, the literature helped define the factors and variables by citing articles from previous studies. This helped me better understand what the authors are trying to imply in the article. However, though the authors have advantages in combining the literature and framework, some readers might find this section confusing as it is difficult to decipher the difference between them. I also noticed that the authors introduced and explained the variables they wish to examine by citing research that studied racial micro aggressions and how different organizations and institutions deal with the campus racial climate. Racial representation of students in STEM is often attributed to pervasive stereotypes about intelligence and academic preparation based on race (McGee & Martin, 2011; McGee, Thakore, & LaBlance, 2017; Trytten et al., 2013). On the other hand, I also wonder how the authors come up with variables since a lot of explanation was given yet it was still somewhat unclear. Do the variables depend on stereotypes, misconceptions and the like or is it the environment? I somehow suggest that the authors should shorten some of the information and cited the variables straight to the point.

Further to the question, I also looked for answers since I might have overlooked some and I did find a few explanations but somehow, it is still confusing to me. Contradicting research may also have served as a basis for undertaking this research. The present study advances the ever-important work of understanding and addressing disparities in STEM education, paying particular attention to the impacts of racial campus climate on students of color in STEM majors. (Lee, Collins, Hardwood, Mendenhall, Huntt, 2020, pp. 6). With regard to the statement of the problem, it seems to be vague. Does the study focus only on racial micro aggressions on engineering course or STEM courses as a whole? For me, the definition of racial micro aggressions have a lot of factors to tackle and allowed some variables to be overlooked or disregarded. That could have created inaccurate outcomes in return. Upon reviewing the evidences presented, I noticed that the authors cited credible sources which substantiates their claims and findings.

Methodology

At the end of the review of the literature, the researchers created hypotheses and research questions. The hypotheses were as follows: Sue et al. (2007)

1. Examine whether one’s race can be used to predict the likelihood of the regular occurrence of microaggressions on the campus, academic, and peer levels.

2. In examining racial climate on three levels for STEM students of color, we expect that those who identify as Black and Latinx will have higher rates of incidents of microaggressions (1) on the campus level, (2) in their academic interactions, and (3) from peers.

The research questions were as follows: Sue et al. (2007)

1. Do non-White STEM students face RMAs at the campus, academic, and/or peer levels?

2. How do these experiences vary by race, gender, and class year? What are the specific types of RMAs experienced?

3. How do RMAs contribute to the low numbers of underrepresented minorities in STEM majors?

Following the hypotheses and research question, the researchers clearly stated the variables. It was specific and straightforward. The dependent variables were the campus level, academic level, and peer level and the independent variable were the race, gender, and class year. The authors explained the response of each dependent and independent variable. After describing the dependent and independent variables, the researcher did not identify any intervening or confounding variables. Albeit, we suggest one confounding variable regarding this research to improve it as a whole, and that is by stating the educational attainment of their participants since having a better educational attainment equates to a broader background knowledge to the topic they are asked to. They employed a deductive analytical approach to identify instances of RMAs experienced by non-White STEM students on campus, in formal academic settings, and between peers. This deductive approach, also known as a template approach (Crabtree & Miller, 1999), included a series of codes developed before the analysis of the open-ended responses. This project followed the ethical and legal standards outlined by the Institutional Review Board for research involving human subjects. Informed consent was obtained from all individual participants in the study. As no names were collected in the survey, pseudonyms are used throughout the paper.

For this study, the population of interest was the student of color in STEM major who have faced RMAs at the campus, academic, and peer levels. The questions were adapted from the Schedule of Racist Events (Landrine & Klonoff, 1996), the Index of Race-related Stress (Utsey, 1999), and the Racial Life Experiences Scale (Harrell, 1997). The study’s sample included all enrolled students at the university. The study estimated a series of Poisson regressions to examine whether one’s race can be used to predict the likelihood of the regular occurrence of microaggressions on the campus, academic, and peer levels. Poisson regressions are used when the dependent variables are count variables that can appear to be continuous but have a range of under 100 (Liao, 1994). The dependent variables in this study were count variables. While Poisson regression measures the probability that an event will take place, in this study, it was used to predict the probability of higher incident rates of RMAs.

Discussion, Conclusions, and Recommendations

All variables used in the models are included along with their mean, standard deviation, and range. For the dependent variables, the mean references the total score of the scaled variable. The majority of students were in the later years of their undergraduate studies or were in graduate school when surveyed. The latter category was so small because the survey was taken early in the school year and first years were just starting to get acclimated to their new surroundings.

By examining both the quantitative and qualitative data, we found that even when the occurrences of RMAs were fewer, they nonetheless made an impact on the emotions, confidence, and retention of students of color in STEM fields. This research also reveals how much more intense that experience is for Black students. The findings are consistent with what others have found about Black students' experiences of RMAs in higher education settings. Studies show that White individuals perceive Black students to be intellectually inferior, second-class citizens, criminals, and of inferior status (Sue et al., 2008). Black students report little interaction with faculty (Allen, 2010), and also report that constant RMAs from their instructors and peers lower their academic motivation (Solórzano et al., 2000).

Through the examination of students’ experiences with RMAs at the campus, academic, and peer levels, the present study considered the role of campus racial climate in contributing to representational disparities in the STEM professions. Future research can continue to build upon their findings in several ways. It is worth investigating the influence of external factors, such as government funding initiatives and the national sociopolitical context, as well as additional internal factors, such as admissions and coursework policies, on students’ racialized experiences in STEM programs. Finally, future studies should consider disaggregating the STEM discipline into specific majors or occupational clusters to more fully understand the nature and scope of racial disparities and racial discrimination to provide tailored and targeted educational interventions.

References

Mccullagh, J. (2016, January). Racial Microaggressions, College Self-Efficacy, And The Persistence Of Students Of Color In Predominantly White, 4 Year Institutions Of Higher Education. https://commons.und.edu/cgi/viewcontent.cgi?article=2931&context=theses

Lee, M.J., Collins, J.D., Harwood, S.A. et al. (2020). “If you aren’t White, Asian or Indian, you aren’t an engineer”: racial microaggressions in STEM education. IJ STEM Ed 7, 48 https://doi.org/10.1186/s40594-020-00241-4

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