Content Analysis

Analyzing research data is hard work, no matter what type of data we’re using. Let’s imagine a group of researchers analyzing people’s choices around plant-based food. You might envision experimental groups and a control group being assigned to different conditions: reading an article about factory farming, reading about the effects of meat production on greenhouse gases, or no article for the control group. Then, they take everyone to lunch and see what they order: the meat or plant-based option? The data from this study would be quantitative; Researchers would use statistical analysis to determine the percentage of people in each group who chose meat versus vegetarian options, and to identify any influencing factors in their results. While this might be what we typically picture when we think of data analysis, that's not what every type of study uses.

Content analysis focuses on qualitative data. In this case, rather than analyzing a binary lunch choice, researchers may bring participants in for a focus group. They would ask open-ended questions about how participants make food decisions, how they feel about what they read, and what other factors may be at play for them. Maybe there’s a third group of researchers, interested in understanding why people switch to vegan diets in the first place. They would turn to the internet (where there are endless youtube videos, reddit threads, and facebook posts from people explaining exactly what they eat and why). For both of these research groups, the data they are working with isn’t numerical. Instead, they’re analyzing an abundance of content. 

To make sense of focus group transcripts, social media posts, or any other large qualitative data, researchers use content analysis, which helps us uncover patterns in the data. There are a number of ways we can analyze this content, and even though the data is qualitative, we can quantify some of the results: how often is a certain word used? Are certain phrases more likely to be said together? Are certain groups more likely to reference the same thing? 

Alternatively, we can perform a more qualitative analysis, coding the words from the data, grouping the codes into themes, and reflecting on patterns within the themes to understand the meaning or bigger picture behind the data. Regardless of which method we choose, when working with qualitative data, we’ll be doing some sort of content analysis.