For the quantitative method, I used basic content analysis to look at the first ten comments to see how many times someone says something positive, or negative, or uses emojis in the comment section of the photo being examined. This is helpful for understanding how the public is perceiving these posts and whether the responses are different based on the gender of the person in the photo. I also focused on key factors of the post itself including the type of post (single photo, swipe photos, or video), gender of the model, body positioning, clothing, whether the model is accessorized or not, facial expression, bodily expression, signs of sexualization, and the setting.
The qualitative method used is an interpretive content analysis that examines the deeper meaning of how athletic brands choose to show gender representation through their Instagram posts. I analyze the poses, faces, settings, attire, activity, gender norms, and if they are being sexualized in any way. The text that is analyzed comes from the captions and the comments for each post. The captions were either discussing a specific topic, an advertisement, highlighting a specific person, or telling a person’s story. The first ten comments were grouped into specific categories of strictly emojis, positive, or negative.
For my sampling frame, I focused on four athletic brands’ Instagram posts that portray male and female athletes in no specific time frame or like count. The brands analyzed in this research are Nike, Under Armour, Lululemon, and Athleta. The sampling method I use is the purposive non-probability sample method. This allowed me to personally select the Instagram posts based on my own opinion and alignment with my requirements of one person as the focus of the post and four posts of women and four posts of men per brand. The only exception to the requirements is Athleta as they only produce clothes for women therefore, I only used four posts from them rather than eight like the other three brands. This tallied a total of 28 Instagram posts for the data. The comments are chosen using a non-probability convenience sample method. Through this method, I chose the first 10 comments that appeared under each post for each brand. This included emojis, positive, and negative comments but excluded comments that were repeated, written in another language, or had nothing to do with the post.