For my quantitative analysis, I will be counting the number of times words like vaccine and covid are mentioned throughout reading. I will also be counting the amount of times tweets were used to either promote or discredit the athlete of discussion. I will be using 50 total news articles from both right and left oriented news outlets, to properly gauge bias in each. To figure out if these news articles are predominantly left or right leaning, I will be using the media bias chart from allsides.com, which labels each news source in categories based on political agenda. For my qualitative content analysis, I will be looking at the athletes’ reasoning within these news articles for being against or hesitant about the Covid-19 vaccine, and what other factors there may be to drive them to a decision. I would also like to look at the tone these articles use to describe the athletes being talked about. I will search for things in common between articles written about Aaron Rodgers, Kyrie Irving, and Novak Djokovic. I will be using purposive sampling, a type of non-probability sampling, due to the types of athletes and news media sources I am trying to look at, and then looking at data that goes along with it.
My sampling frame will be looking at news articles and statements made by athletes since around the time the Covid-19 vaccine was introduced in 2021. I will look at the perception of vaccines over the time period, and see if they became more or less accepted from 2021 to now. My sampling method will be taking specific athletes who have made anti-vaccine statements, and finding 25 news articles that discuss them for both sides of politics.
This topic is one to shed light on due to the ways that news sources can display political bias, which may change viewers’ minds about certain topics. Bias is important to note due to how it can change readers’ minds on their opinions of athletes or any other topic just because of the stance of the media.