Alexis Campbell and her coding excel document
My name is Alexis Campbell, I am a senior at the University of Arkansas pursuing an Honors Bachelor of Arts in journalism from the Fulbright College of Arts and Sciences. My degree includes an emphasis in advertising and public relations and a minor in marketing from the Sam M. Walton College of Business.
Over the course of the 2020 Fall semester, I worked alongside my honors research mentor, Dr. Jee Young Chung, to perform a content analysis of alcohol advertisements on social media. Content analysis is a quantitative research technique used to investigate content by interpreting and coding material. My goal was to quantify and describe the alcohol industry’s advertisements on Facebook, Instagram, and Twitter.
Drinking among college students is not uncommon; the 2018 National Survey on Drug Use and Health found that 54.9% of college students drank within the past month. In fact, studies demonstrate that some students begin drinking as early as middle school. As an advertising major, I couldn’t help but wonder how these numbers might correlate with advertisements. After some research, I found several studies that demonstrated a correlation between underage drinking and traditional media such as commercials and magazine advertisements, but the number of existing studies surrounding online alcohol advertisements was next to none. I wanted to help change that. I hypothesized that these online advertisements behave in the same way as traditional advertisements, showing a correlation between drinking and exposure. However, a minor’s exposure to online advertisements could likely be far greater, due to the amount of time Generation Z spends online. From there, I formulated several research questions, but my main question was: What messages are within these online advertisements? By finding what content is within online alcohol advertisements, perhaps it could help determine what restrictions should be put into place on alcohol advertisements that minors are frequently exposed to.
In the beginning steps of my content analysis, I had to select my sample and construct content categories. Placing a unit of analysis, one individual advertisement, into a content category is called coding. Creating these categories was a trial-and-error process. Each category had to be mutually exclusive and exhaustive, meaning that every unit of analysis would be able to be placed in only one category.
Dr. Chung is very experienced in research and has published work in Public Relations, Communication and Media, and Quantitative Social Research. She served as my guide throughout this analysis. When creating the coding categories, I would send her a list of categories I developed, she would send her revisions and suggestions, we would code a sample of advertisements, compare our results, and restart this cycle. Over and over. Unanticipated items frequently appeared and then the original scheme required changes to be made before the primary analysis could begin. Finally after several revisions, we reached a final product and I began a pilot study with a research coder.
A person who does the coding is called a coder, and the number of coders involved in content analysis is typically small; for my study, it was me and a fellow student at the University of Arkansas. I trained the other coder carefully with lengthy sessions to ensure the coder thoroughly understood all of the operational definitions, category schemes, mechanics, peculiarities, and total ins and outs of the study. The coder was paid through my Honors College Research Grant to ensure that the research was reliable and unbiased. A pilot study of 72 advertisements was conducted separately between me and the other coder. Dr. Chung and I used a formula to calculate intercoder reliability between the coding results and found that the other coder and I had 98% similarity within our work, which is an acceptable level of reliability to continue to the full content analysis.
For the full content analysis, the other coder and I evaluated the messages within 432 more advertisements. We evaluated advertisements on Facebook, Instagram, and Twitter, for 12 alcohol brands, including White Claw, Bud Light, Tito’s, and Jack Daniels to name a few. For each advertisement, the caption and picture were evaluated separately for 54 different categories. The categories examined the context of the advertisement: if there was a joke or humor, if there was a recipe, if it was a seasonal advertisement, if the underlying message was to relax with a beer, to socialize with a drink, etc.
At the moment I am analyzing the data and interpreting the results. I will have a full report of the results complete around March 2021. The Honors College Research Grant allowed me to create a study with reliable and respected results about a topic that I am thoroughly interested in. This research project serves as a pioneer in its field and I am proud to create work that can make a difference in our society.