The Relationship Between Gender And GPA


The impact of students’ socio-demographic characteristics on their overall academic performance has been the subject of much scholarly literature, and it is a debated issue in contemporary academic discourse. Understanding how a student’s ethnicity affects their overall educational performance helps identify the key predictors that determine systemic learning outcomes, taking preventive steps based on evidence and the desire to improve the student’s personal experience. One of the pillars for in-depth study of this issue is the broad assumption of historical and social inequalities, leading to White students being systematically more likely than their peers of color to perform better academically. The psychological concept behind this hypothesis is that students stratified by ethnicity experience greater emotional and cognitive pressure during the educational process, failing to expend as many resources and energy on learning as their White classmates (Gougis, 2020; Maajida Aafreen et al., 2018). This literature review section seeks to explore this relationship through relevant research sources published over the past five years.

One of the most noticeable predictors of lower academic achievement for communities of color is the phenomenon of discrimination that qualitatively sets White students apart from their peers from alternative ethnic cohorts. One critical study in this area is an article by Stevens et al. (2018), who examined the relationship between discrimination against U.S. college students and their academic performance. The authors examined data from more than 69,000 U.S. students and concluded that members of communities of color, which include Hispanic, Black, Asian, Native American, and multiracial, were significantly more likely to experience academic discrimination than their White peers. Notably, Black students reported feeling discriminated against about four times as often as White students. In turn, students also stated that the discrimination they perceived hurt their academic performance, whether through unequal treatment by teachers or being forced to put more effort into their studies. One possible factor influencing the lower academic performance of communities of color in the context of discrimination is an emotional predictor, as “Blacks are burdened with the added stress of race prejudice throughout their academic careers” compared to White peers (Gougis, 2020, p. 1). Likely, the development of this stress may also be due to the formation of majority pressure, as it has been found that students from an ethnic group that appears to be more represented in the course are more likely than their less represented peers to perform better in chemistry, physics, and psychology (Blatt et al., 2020). This latter finding suggests that students of color may perform better if they are more represented in the course.

However, there is ambiguity about the overall achievement gap between White communities and communities of color. For example, official government data reports that the achievement gap between Black and Hispanic students compared to White students has been narrowing significantly over the past fifteen years (Carnoy & García, 2017). This allows the achievement gap to be seen as a dynamic indicator that, despite optimistic changes, persists. However, government data cannot be extrapolated to younger students, for whom an increase in achievement inequality was found in terms of the ethnic communities mentioned over twenty years (Paschall et al., 2018). In other words, it is correct to postulate that either the gap is narrowing only for adult students or there is an inconsistency in the published data.

However, discrimination against communities of color has been explored not only through student and faculty communication but also through the institutional tools of educational institutions. For example, an intriguing look at student achievement practices was contributed by Camelo & Elliott (2019), who examined the relationship between student food security and academic performance. The authors showed that Black and Hispanic students were more likely than others to suffer from low food security due to social inequality, resulting in significantly lower academic performance. Notably, low food security was associated with academic performance through mental health constructs (Martinez et al., 2020). Forced to worry more often than others about covering the fundamental needs of healthy and affordable food, these students are unable to invest in quality learning, causing their overall academic performance to fall. Another mediating factor between ethnicity and student achievement is financial ability. Black and Asian students have been shown to have less financial privilege to pay tuition, resulting in financially mediated stress that is destructive to student performance (Baker & Montalto, 2019). Thus, financial security has been identified as another predictor of lower academic achievement for communities of color.

Based on the literature review results, it became known that the issue of academic achievement has been extensively researched by independent authors. However, in virtually all studies examining the relationship between ethnicity and Final Grade, authors use mediating variables to describe the effects of food security, stress due to discriminatory practices, or financial security. The discussion of the study showed high academic value because it shed light on the problem of the relationship between a student’s ethnicity and their academic performance; however, it did show some contradictory conclusions. Moreover, no material has been found that directly measures the effects of students’ ethnicity on their overall academic performance, creating a dearth of reliable knowledge on this topic. Thus, in forthcoming research, it is appropriate to look at the direct relationship between the two variables in conjunction with the need to examine the pressure factor as a psychological concept that affects the academic performance of students of color.



For this study, I will use the archival data method, in which preliminary information collected by other researchers is reused to identify previously new patterns. Because it is not known precisely how the archival data were collected and whether they accounted for the equal opportunity of each participant in the general population to enter the group of subjects, it is not possible to verify precisely what type of sample was used to create the primary data. To create the sample, all available records from the original 105 student performance file will be used, so in this case, it is appropriate to assume a probabilistic sampling mechanism in which each participant has an equal chance of being in the sample of the current project (McCombes, 2019). It can be said that a systematic sampling method will be used in which every first entry in the original primary data file was used for statistical analysis.


I will use a quantitative research paradigm to measure the potential relationship between a participant’s gender and their GPA score. In this case, the data is treated as numerical information, and the analysis will result in quantitative patterns between the variables. The gender variable was measured on a nominal scale and included two values, male and female. At the same time, the GPA variable was defined at the scale level and contained non-discrete information about the student’s actual GPA. Given the nature of the two variables, the parametric Independent T-test will be used as part of inferential statistics to compare statistical differences between averages within groups (Laerd Statistics, 2020). More specifically, the T-test allows for a statistical comparison between the mean GPA scores for females and males to determine if there are differences. For both, reliability can be measured through the use of Cronbach’s alpha, which will determine a measure of internal consistency between the data. In addition, the very nature of the variables used is of high face validity, as both gender and GPA do measure exactly the desired values. For this reason, the measurements are also expected to have high internal validity and thus can be used to examine observable effects.


I will use a quantitative design method based on archival data. It is a secondary data analysis or archival study in which primary information has already been collected and used to discover new relationships within the current study (VT, 2018). The implication is that all records in the original file were obtained voluntarily, which means that each participant has consented to participate in the study. For this reason, the use of primary data for secondary analysis is also based on informed consent that was signed by participants when the original information was collected. Specifically, respondents would fill out a questionnaire that included a socio-demographic block, namely information on name, gender, ethnicity, and year of study. The rest of the questions on the questionnaire related to the main block, where reliable information about each student’s academic performance was obtained. Gender would be the independent variable, whereas GPA information was determined to be the dependent factor. For this reason, all participants belonged to a single study group, and no control group will be implied in the absence of a research intervention. Since it is expected that different institutions may differentially evaluate students and have differences in student preparedness, all participants would belong to the same educational center to control for this effect. In addition, the variables of the year of study and ethnicity may also affect student achievement, so a mediator analysis for influence will be conducted to control for these effects.


Baker, A. R., & Montalto, C. P. (2019). Student loan debt and financial stress: Implications for academic performance. Journal of College Student Development, 60(1), 115-120.

Blatt, L., Schunn, C. D., Votruba-Drzal, E., & Rottman, B. M. (2020). Variation in which key motivational and academic resources relate to academic performance disparities across introductory college courses. International Journal of STEM Education, 7(1), 1-25.

Camelo, K., & Elliott, M. (2019). Food insecurity and academic achievement among college students at a public university in the United States. Journal of College Student Development, 60(3), 307-318.

Carnoy, M., & García, E. (2017). Five key trends in us student performance: progress by blacks and hispanics, the takeoff of asians, the stall of non-english speakers, the persistence of socioeconomic gaps, and the damaging effect of highly segregated schools [PDF document].

Gougis, R. A. (2020). The effects of prejudice and stress on the academic performance of Black-Americans. In U. Neisser (Ed.), The school achievement of minority children (pp. 145-158). Routledge.

Laerd Statistics. (2020). Independent t-test using SPSS Statistics. LS.

Maajida Aafreen, M., Vishnu Priya, V., & Gayathri, R. (2018). Effect of stress on academic performance of students in different streams. Drug Invention Today, 10(9), 1776-1780.

Martinez, S. M., Frongillo, E. A., Leung, C., & Ritchie, L. (2020). No food for thought: Food insecurity is related to poor mental health and lower academic performance among students in California’s public university system. Journal of Health Psychology, 25(12), 1930-1939.

McCombes, S. (2019). Sampling methods | types, techniques & examples. Scribbr.

Paschall, K. W., Gershoff, E. T., & Kuhfeld, M. (2018). A two decade examination of historical race/ethnicity disparities in academic achievement by poverty status. Journal of Youth and Adolescence, 47(6), 1164-1177.

Stevens, C., Liu, C. H., & Chen, J. A. (2018). Racial/ethnic disparities in US college students’ experience: Discrimination as an impediment to academic performance. Journal of American college health, 66(7), 665-673.

VT. (2018). Research methods guide: Research design & method. Virginia Tech.