Data Source:
My data source was the ESPN Body issue that features male and female athletes over a 10 year span. I looked at two males and two females photo collections from each year spanning from 2009-2019.
Sampling Strategy:
For my sampling procedure I looked at the ESPN body issue data base from 2009-2019, the 10 years they did the body issue. The sampling method I used was probability sampling as I wanted to be able to make generalizations about my study and findings. More specifically, I used simple random sampling where I randomly selected 2 males and 2 females for each year of the body issue. In order to do this I counted up the total number of athletes photographed per year and used randomizer.org in order to randomly select my athletes. After finding 20 male and 20 female athletes I looked at each male and female athletes collection of photos. I looked at a total of 98 male photos and 103 female photos.
Method:
For my study I used the method of basic and interpretive content analysis. Basic content analysis can be defined as, “basic content analysis focuses on “content features that could be categorized with little or no interpretation by the coder (Drisko & Maschi 2015).”” Interpretive analysis can be defined as, “a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the contexts of their use (Krippendorff 2013).” Firstly I developed my own code categories and many based on Goffman’s gender display theory.
Male Code Categories
- Standing upright
- Flexing
- Grounded
- Balanced -Grounded knee bend
- Gripping/grasping object
- Stare
- Active/Alert
Indifferent Code Category
- Knee bend seated
- Movement knee bend
- Graceful Movement
- Powerful Movement
Female Code Categories
- Lying down/Seated
- Off balanced
- Vulnerable
- Submissive
- Holding oneself in protective matter
- Breathless posture
- Gentle touch
- Self Touching
- Drift/Dreamy
- Smiling/Laughing-giggling
- Biting/Finger to mouth
Using qualitative analysis I looked at the photos to try to categorize how athletes were displayed and whether or not those displays aligned with Goffman’s gender display categories. I then picked the best representing photo of each code category and best photo representing the recurring themes I found. I then looked deeper into the photos for recurring themes and new themes that arose. Once i had created my codes, I used quantitative analysis to count the relative frequencies of each gender display code category throughout the athletes photo collections.