When I began my thesis, I approached my research question with a strong focus that my hypothesis would be clearly correct or incorrect. I didn’t anticipate the potential for the outcome to be somewhere in the middle. This made interpreting my results all the more difficult. Of course you will have an idea in mind of what you expect to see in your results. However, my advice to any thesis writer is to approach your results with an open mind when you are analyzing your data.
My research question addressed the gender equity impacts of Name Image and Likeness (NIL) in college athletics and I hoped to be able to take a stand of whether Title IX should be applied to NIL benefits. I wanted to find out (1) if NIL disproportionately benefits male or female athletes, (2) what factors in university spending contribute to an athlete’s NIL value/potential, (3) how differences in fan and spectator engagement with male and female sports teams impacts an athletes ability to be compensated through NIL, (4) the role of potentially disproportionate television and sports broadcasting benefits impacts NIL compensation, and (5) if universities have the tools in place to support equitable NIL benefits for student-athletes.
I entered my research assuming I would find disproportionate opportunities for male and female athletes, which I did: almost 75% of the NIL deals coming from Division I NIL Collectives are made with male athletes. What confused me, however, was the results I found regarding the factors that contribute to this difference. I expected to see that increased spending for and fan engagement with male athletics would both have a strong, positive correlation with the number of NIL opportunities for male athletes.
My results were not as clear as my hypothesis. My advice to anyone doing research is to be okay with your results not being as clear as you anticipated. Of course you will attempt to make your research question and methods as comprehensive as possible, in order to understand intervening variables. But still, you may not be able to explain everything you hoped to in this study.
Since my results were not what I expected for each variable I tested, I spent a lot of time trying to figure out what was wrong with my data and my analysis. Your time in this process is precious and important to manage. If you become stumped because a few of variables you are studying did not yield the results you expected, you may loose critical time you have to understand the results of your thesis.
In hindsight, what I wish I had done was pretend that I did not have a hypothesis when I was analyzing my results the first few times. This would have allowed me to spend more time understanding the relationships I saw from my data, rather then spending too much time frustrated that my results weren’t what I thought I would see.
After deciding to drop the view of my hypothesis during my initial analysis, I was finally able to make progress in interpreting the results and relationships I found in my data. There are so many things I could give advice on as you take on this awesome task of writing a thesis, but what I wish I realized earlier is that it is okay if your hypothesis is not correct and you will find other important relationships in your data that you didn’t think to look for by approaching your analysis with an open mind.
Margo Rogers is a senior in the O’Neill School of Public and Environmental Affairs.
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