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Inquiry and Process:
Purpose of this Journey: In the past millions of students have been impacted by affirmative action as it is defined as a “positive step” taken to increase the representation of minorities in areas including employment, education, and culture (Fullinwider 2018). However, the Supreme Court ruling this past summer has created room for many questions to be raised in regard to affirmative action. Many arguments are created some of which support affirmative action and some who believe it is unjust and not beneficial for students across the nation. A study written by Sara Garcia provides a perspective on the positive impact affirmative action has had on the enrollment rates of American Indian/Alaska Native students. Garcia discusses a trend over a time period from 1976 to 2008 and states that American Indian/Alaska Native individuals saw their share of total college enrollment increase by 46 percent (Garcia 2023). However, in contrast to this Sacks argues a more critical perspective and states that affirmative action decreases the importance that qualifications have for students and future professionals. Specifically discussing doctors, he states that qualifications should remain a higher priority than diversity when looking at what a student will bring to the workforce (Sacks 1996). Arguments such as those of Garcia and Sacks demonstrate the complex role that affirmative action has in society. However, studies have only looked at the statistics in admissions and the impact affirmative action had on admissions in comparison to before it was enacted. Pattern recognition in admissions of minorities has been the main point that many research papers look at, and this is the gap that my research project takes on. There has not been a study looking at the impact that the abolishment of affirmative action had on college applications specifically, and this is where the value of my research comes from. Because of the recency of this Supreme Court ruling, my project will not overlap with other research and will create a path for a new understanding of the college admissions structure.
Garcia, Sara, and Connor Maxwell. “5 Reasons to Support Affirmative Action in College Admissions.” Center for American Progress, 13 Sept. 2023, www.americanprogress.org/article/5-reasons-support-affirmative-action-college-admissions/.
Sacks, David, and Peter Thiel. “The Case against Affirmative Action.” STANFORD Magazine, 1996, stanfordmag.org/contents/the-case-against-affirmative-action.
(1) Regional Divisions used in my research for data collection. List of states for the Northeastern region used to view universities by each state.
US Census Bureau. Census Regions and Divisions of the United States, 2023, www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf.
Beginning the Drive and Flat Tire: Initially, my methodology consisted of looking at 2018 and 2023 essay questions on college applications and comparing the differences in the questions over the years as a result of the abolishment of affirmative action. Specifically, I am observing differences in the presence of essay questions that ask about topics including diversity and race. I changed the scope of my research through methodology. The main change that I made in my methodology has been to no longer look at a comparative viewpoint of the 2018 and 2023 application forms. In other words, I limited the scope of data collection to only view 2023 applications. I reduced the scope of my project in order to compromise to the timeline that was available to me for data collection. My new research question is: What direct impact has the abolishment of affirmative action have on essay questions asked in the Fall 2024 college applications, and how does this vary from “large” private, public, and ivy league universities across the United States? However, the other aspects of my methodology remained the same from my first blog post. Regional divisions remained the same and are derived from the U.S. Census Bureau (1). To filter the colleges that I need to look at, I used Niche because it filters colleges by state, on whether they are private or public, and also on student population size which, for my research question, has to be a minimum of 15,000 to be a large school as defined by the College Board.
College Board, Big Future. “Understand College Campus and Student Body Size.” Understand College Campus and Student Body Size, 2023, bigfuture.collegeboard.org/plan-for-college/college-basics/types-of-colleges/understand-college-campus-student-body-size.
(2) The images show the filter options that Niche provides that were utilized for my research.
Niche. “2024 Best Value Colleges in America.” College Search, 2023, www.niche.com/colleges/search/value/?studentBodySize=large&type=public.
(3) Spreadsheet before random selection. Shows 1 of 4 regions, and created with the help of Niche's filters.
Repair Shop: Once I worked through finalizing the scope of my research, I started creating a spreadsheet in order to randomly select universities that I will be collecting data from. In order to assist me in naming all the universities I used Niche, a website that provides filters (about student body size, location by state, and college type) to look at colleges across the United States (2).
With Niche I was able to efficiently create a spreadsheet, divided public, private, and ivy, of every single school that met the requirements of possibly being in my sample. With this I could begin random selection (3). The image of the spreadsheet labeled as 3 shows the Northeast region before I began random selection. In other words, it shows all the schools that meet the student body size requirements under each college type.
(4) This 3 step image demonstrates an example of how the “randIntNoRep()” function works on the calculator.
McCalla, Jeff, and C. C. Edwards. “How to Generate Random Numbers on the TI-84 Plus.” How to Generate Random Numbers on the TI-84 Plus, 2016, www.dummies.com/article/technology/electronics/graphing-calculators/how-to-generate-random-numbers-on-the-ti-84-plus-160919/.
Repair Progress: Once I finalized the possible sample for all 4 regions, I began random selection to choose the finalized sample for data collection. In order to do this I used a statistics function on a graphing calculator. The function is “randIntNoRep()”, and this function generates randomly selected values within a certain range of numbers that don't repeat. Image (4) demonstrates the process in which the numbers are randomly generated. The lower to upper values are the numbers on the left side of the spreadsheet that show when the university list in each region begin and end. Following this, the "n" value would be the number of randomly generated integers that I need for the region. I choose 50% of the total number of schools listed.
For example, when looking at public universities in the Northeast region in image (3), the "lower" value would be 19 and "upper" value would be 34. Since there are a total of 15 schools in this region I would round up from what 50% is. So the "n" value would be 8 as I want to collect data from 8 public colleges of the 15. Image (5) shows an example of random selection of the Northeastern region.
(6) Highlighted slots represent my finalized sample for data collection following random selection.
Final Repairs: I repeated this process for all 4 regions and each college type within each region. Image (6) shows the Northeastern region once I finished random selection. The highlighted universities represent my finalized sample for data collection. I chose to collect data for all 8 ivy league universities because of their small number in comparison to other school types, and therefore they are highlighted in the spreadsheet.
(7) Shows the separation of sections on the spreadsheet I used for data collection
(8) Shows finalized data collection for 1 region (Northeast) with "X"s representing each data point for each university
New Tires: Once I finalized my sample, I finally began data collection. To collect data I used CommonApp which provided me access to view almost all college essay questions through one outlet. I simply looked up each university, and looked at if they had essay questions that asked questions that are asking or hinting at questions regarding diversity or race. To record my findings I created a separate spreadsheet thats structure is shown in Image (7). I put an X in the column that defined what the essay question was asking. The "Hinting at it" column represents schools that asked questions where students could respond with answers talking about their race or racial experiences but didn't need to. Image (8) shows completed data collection for the Northeastern region.
Minor Issues with Installation: When completing data collection some universities were not available to look at on CommonApp, and because of this I needed to create a separate account on each college website in order to access essay questions if they asked any. This process took more time and led to some delay in data collection, however it wasn't a big roadblock because my timeline accounted for some delays.
Back on the Road: Once I finished collecting data I decided to consider the "Hinting at it" column to come under "Yes" as it still asked a question that hinted about race and diversity and provides space for students to write about these topics. Therefore, I believed that this gives considerable reason to move the "X"s to the "Yes" column. This will also assist in data analysis as there are clear variables to base differences off of.
Future Rest Stop: Once this became finalized in my data, I am ready to begin data analysis. In order to start, I needed to find a significance level that will help me decipher if there is true significance or if the results are from chance alone. The significance level that I plan to use is 0.05. Once I run these tests for the 4 regions I can then finalize my results and specifically state patterns that I find in my paper.
Discoveries Along the Way: These results will accurately answer my research question and once I run the significance test I can see if there is convincing evidence of differences in questions asked by public, private, and ivy league universities. A limitation to this however is the fact that I cannot check the actual impact that these differences have on admissions with admissions offices due to the data not being produced in time. In other words, I don't have the source or time to fully solidify the impact that the differences I find will have on admissions, however I do have the resources to signify that there is a difference/correlation between the essay questions and college type. Signifying any correlation/possible differences will allow for students in the future to know what to expect in admissions, and choose what school will offer the best advantage to them in terms of diversity expectations. Per the constitution however, if differences are found it can demonstrate the true extent to what a Supreme Court ruling can do in contrast to small loopholes in college admissions.
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