Social Class and Cultural Capital: The Jackpot Effect in Test Score Performance
One can easily overhear in daily conversations discussions on some of the main topics of inequality of education that often filter into popular culture; why do some students get to go to the elite colleges?, if only had I gone to a boarding school I could have attended too, among some of the phrases we may hear. In this nation, we, as a community, do have a general understanding of inequality in education as far as access, affordability, and outcomes go. Yet rarely do we question specific causes that may cause, and often perpetuate, inequality in academic performance and results. In this paper, I aim to give insight to some of the social forces that affect test score results and outline patterns we can recognize when we combine statistical educational data in relation to the literature of such scholarship. Much of the narrative of social policy experts and politicians is centered around improving our schools. While this is an important factor for student success and performance it is not the factor affecting test scores the most. Poor quality of schools and track placement certainly affect student’s performance but not as much as we thought it did. It is apparent that the chance for upward mobility in social class for students is indicative of a positive effect in test scores, hence in affecting the outcomes of said scores.
After prompting us with our claim to be proven, we now will be recognizing quantitative values from the findings of the Educational Longitudinal Study, Base Year 2002 which depict statistical values of test scores. This study specifically provides a broad breadth of information among which we can notice how much higher a student may score if we keep in consideration factors such as school affluent, different tracks, and social class. I will use the following graph based on data from this very study:
This graph can be exemplary of to what degree does poverty of schools and educational tracking account for outcomes of student’s test scores. The columns of this graph are the different factors considered to affect test score outcomes in this study. So, the x-axis has the appropriate title of the “Effect of School and Individual Characteristics on Reading Test Scores, 10th grade.” Their sample of students studied is 10th graders. The categories of these columns are described in a data legend just below the graph. This legend has colored bullet points matching the color of the columns between the axis. The legend shows us the four parameters of effect as the following: “school affluence: if students went from the highest poverty school to the wealthiest school,” “being in a college prep track,” “being in a vocational track,” and “student’s social class: effect of a student moving from poverty to upper class.” On the other hand, the y-axis has an increasing numerical scale ranging from -5, to 20 vertically. This axis is labeled as the “Effect size in reading test score points” (“Educational Longitudinal Study,” 2002).
It may be appropriate to disclaim the graph has its limitations. This graph portrays four parameters that were considered as a selection that may be worth comparison. Not only are there more parameters that affect reading test scores, but we must remember about the importance of the student’s own background. This is a point that will be addressed later in the literature of the scholarship surrounding student background and achievement. Additionally, we must consider that this study based some of their data on their own self-administered standardized reading test. Without going into further detail, it becomes impairing to make an argument for a larger validity of such test results in a larger realm. Regardless, this test resulted in the highest score being 78.7 points and a minimum result was 22.6. Why is this of importance? When examining the level of effect whether higher or lower, one must take into the consideration that nobody made the equivalent of a B letter grade or above, and some failed badly. Also, it is relevant to keep in mind that the range of results was 56.1 points between the best and the worst score skewing how much effect these four parameters truly have on results.
The graph has four numerical values; one for each category of study. Right away one can notice the superlative value of social class affecting reading test scores positively by 17.64 points if a student was to move from poverty to the upper class. Then we can notice two categories — ”school affluence” and “being in a college prep track” (“Educational Longitudinal Study”, 2002) that have a more moderate influence yet still in a positive direction. The fourth value, featured in grey is a different kind of educational tracking, the vocational track. This is the only factor (shown in the graph) that does not have a positive association with affecting reading test scores. Actually, it seems to affect it negatively by -1.28 points. So, the range of these social factors — schools, tracking, and class — accounts for 18.92 points. If we consider that the range of student’s results in reading tests is of 56.1 points between the worst and best, and these social factors can create a difference of up to 18.92 between students, that is a significant comparison to evaluate. If we perform this intuitive calculation we can come up with an indicative number of effect on test scores: or in numbers;
Numerically, these social factors approximately account for 33.73 percent of the variance in test results; about one-third of the range of score outcomes that students have. Now that we have established that the parameters represented in this graph have a strong relationship with test outcome, we need to go a step further. An overall argument of social factors affecting performance is not enough. Now one must evaluate which factors, in fact, play a larger or smaller role and which trends lie under these numbers.
Contrary to popular belief, the quality of schools themselves cannot be held accountable for inequality of student academic performance. There is, in fact, a positive association shown in the graph, but can not and does not unbalance test score outcome significantly enough. It does so a mere 4.79 points which on a 100 point scale cannot be significant enough to be responsible for such a large social inequality in reading test scores specifically. An argument could be made to argue that it mildly affects the outcome and one would be in the right to make such a point because they would be missing the bigger picture. One to make such an argument would have been the late Dr. Jean Anyon, an education expert. In one of her studies, she attempts to rank schools by social class and labels them as “working-class schools” (Anyon, 1981, p. 6). While she was in the right when investigating social class in schools, she attempts to qualify schools by class and maybe not allowing for social classes to be mixed at different schools. In other words, Anyon used the lens of social class on a macro level thinking that the “substantial differences in knowledge [are] among the schools” and not the students. By doing so, she did not leave space to concretely look at the social class of the individual and how will the student perform academically handicapped or enhanced by their social class. As far as the quality of schools, Anyon found that children at affluent professional schools (and also executive elite schools) will benefit from a privilege linked to the kind of school they attended to and gained knowledge from (Anyon, 1981, p. 35). On the other hand, we have similar literature of inequality of school affluence like The Coleman Report that studies racial school inequality in the 1960s. This study showed racial complications in representation, and lack thereof, of African American students in schools and in turn showing how unequal life-after-school success was between the white majority and African Americans. While long ago, this study is still pertinent because it serves as one of the first pieces of scholarship denouncing the lack of true relevance of differences in school affluence in regard to affecting academic performance and success. In other words, The Coleman Report shows that a mere “20 percent of the achievement of Negroes  is associated with the particular schools they go to, whereas only 10 percent of the achievement of whites” is. Further research was pitched by this report as these researchers wondered “what factors in the school seem to be most important in affecting achievement?” (Coleman, et.al, 1966, p. 26–27). It is now clear that the quality of schools is not as important as we previously had considered. It is essential that we begin to consider how a student’s background affect their academic performance. But first, we will follow another concern The Coleman Report featured in their concluding statements and that is, which factors within schools have an effect on achievement? Well, factors within schools account for half of the data of the graph at hand — tracking categories.
Tracking in schools can be defined as “the practice of assigning students to instructional groups on the basis of ability” (Hallinan, 1994, p. 79). There are tracks in schools such as the ones featured in the above-mentioned graph, such as college preparatory tracks, and some of the least ability tracks such as vocational or skill learning tracks. There have recently been complaints that tracks tend to be assigned to students in biased ways and often end up negatively interfering with the development of students potential. There is somewhat negative rhetoric surrounding the ethics of keeping tracks in schools. In the graph of the ELS:2002 it is shown that the tracking categories coincide in being of least positive influence towards attaining better reading test scores. Nevertheless, both tracking categories — college prep and vocational — are different because they have different graphical directions and opposite associations. The college prep track is set to aid performance in said tests by a mere 3.05 points while the vocational track does not positively affect test outcomes. The vocational track is the only category that worsens the outcome of test scores. In this study only by -1.28 points. Both categories do amount for the perpetuation and “increases in inequality” (Gamoran, 1992, p. 13), but the vocational track is an impediment, not an aid when hoping to score better. Regardless of the disparity of outcome between tracks, it is apparent that their effects are even less than school affluence. Sociologist Dr. Adam Gamoran also finds that tracking rarely improves achievement in school, but indeed contributes to the inequality (Gamoran, 1992, p. 15). Both our graph and the literature show the small betterment of test scores and some increase in inequality, yet the two factors are the smallest in test score variance.
Reaching the conclusion that tracking only has a small influence on academic achievement inequality in comparison to other factors one needs to look no further than our biggest factor at hand — social class. This is the one factor that is most transformative in performance. If a student was to transition from the lowest rank in social class, poverty, to the highest, upper class, they would gain a large advantage in school testing. Several experts like Anyon as previously mentioned, argue in favor of this position; that social class is the driving factor of achievement inequality in the schooling system. How much so, one may ask. Well, according to our ELS:2002 graph of findings, we see that the most superlative value is, in fact, social class. This parameter accounts for 17.64 points of effect on reading test scores. Considering the total effect of our social factors from our graph, social class represents about 93 percent of such effect on scores; Ultimately, social class is the overwhelming favorite of all affecting factors measured in this study. And almost exclusively so by leaving less than 7 percent of effect for other factors. Most importantly, if scores only vary in 56.1 points and social class is proven in this study to vary scores by 17.62 points, this means that social class can change the outcome of scores by more than 30 percent; If these results do not seem striking enough, this would mean that social class inequality could affect reading test scores by more than three letter grades — the difference between an A and a D. Such school performance altering change should be worrisome to us, but when one claims that social class can impact your grades, what do we really mean? Social class is often linked to cultural capital, defined by leading education scholars Lamont and Lareau as “the institutionalized… high-status cultural signals… used for social and cultural exclusion” (Lamont & Lareau, 1988, p. 156). Such drastic change in grades is exactly that, excluding of other people due to inequality in cultural capital. The higher one’s social class is the higher one’s own cultural capital is. Acquiring this cultural capital depends on having social class making it for lower classes such as the working class unable to perform as well as other students due to no fault of their own but of their background. So, it is believed that “Pupil’s achievement is strongly related to the educational backgrounds” (Coleman, et.al., 1966, p. 30). These educational backgrounds if they are not built in schools they must come from home, from the family. This is how one can recognize that how social class and cultural capital depend on being raised by a socially and intellectually privileged family because “almost everything [children] learn comes from their families” (Hart & Risley, 2003, p. 6–7). These families are setting up their children for an academic success that their fellow students outside of their social class do not have access to regardless of merit.
We have examined the effects of school affluence, educational tracking, and social class on the outcomes of test scores. It is clear that social class and cultural capital are responsible for a large part of the variance in score outcomes. Additionally, we recognize that the higher social classes are benefiting from great privilege. When we think about privilege in our day to day, don’t we also think about white privilege? In all of this analysis that is the one factor that research, studies, graphs, and so forth have failed to demonstrate. Lastly, one would be foolish to understand that a student’s background is not solely based on social class and cultural capital. Other factors also pertain to student success in the classroom. Plenty of research has been done on the above mentioned and analyzed factors but we need nuanced studies and findings that research a possible connection between educational inequality, such as test score performance, with race, color, and ethnicity. Class is not the only factor of student’s background and therefore we are left with no other choice but to racial bias in academic achievement in schools.
Anyon, J. (1981). Social Class and School Knowledge. Curriculum Inquiry, 11(1), 3–42. doi:10.2307/1179806
Coleman, J., Campbell, E., Hobson, C., McPartland, J., Mood, A., Weinfeld, F., & York, R (1966). Equality of Educational Opportunity: The Coleman Report. U.S. Government Printing Office,1–32. Educational Longitudinal Study of 2002 (ELS:2002). (n.d.). Retrieved March 13, 2019, from https://nces.ed.gov/surveys/els2002/avail_data.asp.
Gamoran, A. (1992). Is ability grouping equitable? Educational Leadership, 50(2), 13–15. Retrieved from http://lynx.lib.usm.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=9301031798&site=ehost-live
Hallinan, M. (1994). Tracking: From Theory to Practice. Sociology of Education, 67(2), 79. doi:10.2307/2112697
Hart, B., & Risley, T. R. (2003). The Early Catastrophe: The 30 Million Word Gap by Age 3. American Educator,27(1), 4–9. Retrieved March 13, 2019.
Lamont, M., & Lareau, A. (1988). Cultural Capital: Allusions, Gaps, and Glissandos in Recent Theoretical Developments. Sociological Theory,6(2), 153–168. doi:10.2307/202113