Welcome back! I am excited to dive back into content analysis with you. It is no secret that content analysis can be far from a walk in the park and is possibly more comparable to following a treasure map across a remote island. Therefore, I will fill this post with a review of what we have already discussed, the final steps for analysis, obstacles to be aware of along the way, and a few helpful hints. 

Returning to Coding

We left off last week on coding, where short labels are applied to qualitative data which represent meaning within this data. Returning to this process throughout your analysis allows you to condense repetitive codes and make sure you are heading in a direction that is consistent with your data as a whole to avoid bias and better answer your research questions (or find the treasure, so to speak). 

Coding is actually the culmination of two steps. First, decoding data to find meaning and second, encoding it by applying a word or phrase that represents this meaning. Keeping these two steps in mind helps demystify the process of coding as a whole. The codes you create and how you apply them will depend solely on your data and the questions you are trying to answer. When creating codes it is helpful to think of them as not only labels but also links that piece your data together. 

Coding is a key step that organizes your data for further analysis. Once codes are applied to your data, you are ready to begin the more abstract part of analysis by categorizing the codes and searching for themes. 

Categorization of Codes

Categorizing codes is essentially synthesizing them into your analysis by identifying patterns in the coded data. Categories for codes are phrases that encompass an idea which multiple codes fall within. While searching for patterns of underlying meaning in your data, remember that patterns often develop by grouping similarities but can also develop through grouping data by outliers, frequency, order, causes, or other relationships. These possible pattern configurations are all helpful tools for categorizing your coded data. 

To continue with our previous example, in a response to the question “Do you feel that the library is an essential community resource, why or why not?” the code “family support” may fall into the category “highly valued early learning programs.” Other codes such as children’s programs, reading development, and storytime may also fall within this category.

Data outliers should not be viewed as problems but as points of interest and discovery. In this case, evidence that some patrons raising children do not use the library’s early learning resources does not necessarily make the previously mentioned category (“highly valued early learning programs”) wrong, but may lead to a new category entirely or reveal how these programs are more accessible to certain patrons than others. 

Developing Themes

Themes are a more abstract level of insight content analysis might reveal, meaning they are general and applicable beyond a single study. Themes may not always evolve from your coding and that is OK. Codes and categories can still be informative and point to paths for further research to answer your key questions accurately if themes do not develop.

Themes develop when you identify consistent patterns that stretch across the coded and categorized data and bring insight to your main research question. Once you have charted these patterns it is time to start digging for the treasure! Themes are not found by leaping to conclusions and away from your data, they develop through careful analysis of codes and categories to triangulate meaning based on evidence.

If multiple categories point to it, a theme developed from our example study could be “early learning programs bring people together from across the community” This is a concrete answer to the overarching research question “How can libraries increase a population’s sense of community?” and it could be used to inform decision making on future programming in your library.

Obstacles

Content analysis is a time intensive and sometimes frustrating process. You must be willing to dedicate time and effort to it for your conclusions to be accurate and limit bias. Also, it focuses solely on the content of your data without taking into consideration outside factors such as societal context. This limited focus, and the reduction of data to codes, categories and themes, may allow nuances of meaning to be lost. Condensing the data can be problematic if important aspects of it are ignored, or it can be exactly what you need to do to find the buried answers you are looking for. 

A Helpful Hint!

Coding does not have to be a lonely process. In fact, collaborating with a team can help you navigate this work and make the whole journey more enjoyable. As we discussed in the last post, each person will not apply the same codes to each excerpt and that is OK. Being open to a range of perspectives will bring insights to the data that you may never see alone. The possibilities for coding are enormous and narrowly focusing on one route can obscure key information and get you stuck.

Finally, make sure to take careful notes of your process and the codes you use. This will be helpful for you to refer back to throughout your analysis, and it will be helpful for those you share your study with to understand the work you put into it! 

Conclusion

Initially, qualitative data may feel overwhelming or ambiguous, but coding provides a map for condensing the data until you can categorize it and find meaning based within the text. It is rewarding when themes appear that were initially buried in the data. As you are putting time and effort into it, make sure to keep reminding yourself of the research question, the importance of your work, and your end goal. You may uncover key information when you least expect it! Coding reappears in other methods for qualitative analysis, so be sure to keep this information in mind as we continue this chapter.