Sampling Methodology
My sampling method for this study consisted of a combination of randomized and quota sampling. My sampling frame includes all United States colleges or universities with an LGBT Resource Center with a public Instagram page active during the Fall 2023 semester. To narrow down this frame, I first divided the country into five regions, the Northeast, Southeast, Midwest, Southwest, and West. I then randomly selected four schools from each region using the Consortium of Higher Education LGBT Resource Professionals’ Consortium Map.
This random selection was repeated for each region until I had sampled two public schools and two private schools from each region. I was also sure that the pairs of public/private schools differed in their school size. Due to the lack of Centers in the Southwest region and the overrepresentation of Centers in the Northeast, only three centers were selected from the Southwest with the fourth private school being selected from the Northeast instead. This resulted in a total sample of 20 schools. By including both large and small colleges in the sample, I aimed to increase the diversity of types of schools included in the sample. Religiously affiliated schools and specialty schools like HBCUs and Historically Women’s Colleges were excluded from the sample. Explore the schools and centers included in the sample on the map below:
To sample the posts, I counted the number of posts made on the Resource Center’s Instagram during that school’s Fall 2023 semester. I then randomly generated three numbers from within that range and counted down from the last post of the semester and screenshotted that post. This resulted in three posts per center for a total of 60 posts. If the sampled post was not an advertisement for an event or resource, a new number was generated, and a new selection was made.
Content Analysis Methodology
I analyzed the Instagram posts in my sample using a mix of basic and interpretive content analysis. Basic content analysis refers to analyzing the manifest, or obvious, content of a text. This includes things like counting the frequencies of certain words or describing the colors used in a picture. Basic content analysis was used here to count the uses of relevant words or symbols. For example, uses of the word “queer” or images with LGBT symbology. Interpretive content analysis refers to analyzing the latent, or inferred, meanings of a text. Interpretive content analysis was utilized here to classify the subtype categories that each program falls into. Each program was categorized by its audience type, focus category, format, location, intention, and tone. Within these categories, I analyzed the latent and manifest content of the post’s texts and graphics to determine their specific purposes and characteristics.
An example of the codesheet used to analyze the posts is provided below: