Hello data enthusiasts! Let’s return to our exploration of qualitative analysis. Last time we uncovered a few ways qualitative analysis can expand research findings by looking beyond number data for better insight on human experiences. Now I want to explore strategies for putting qualitative analysis into practice.
Concentrating on Content Analysis
As we discussed last time, qualitative analysis is flexible and adaptive to different types of data. As you may have already guessed, this means there are multiple methods for qualitative analysis depending on the kind of research you are conducting, the form of your data and the questions you are asking. Content analysis is one of many methods of qualitative analysis. It carefully filters, categorizes and condenses qualitative data sets (often text based) to discover hidden (or not-so-hidden) meanings! It is one of the most common methods of conducting qualitative analysis, and so it is a great place for us to start this chapter.
The key question that content analysis helps answer is, how do you categorize this textual data to best identify important patterns, anomalies, and relationships that answer your research question?
Planning for Success
You can visualize the content analysis process as following a treasure map where the treasure (buried in the data) is the insights your analysis will eventually reveal!
First, there’s quite a bit of preparation that needs to take place to ensure your analysis goes as smoothly as possible. For content analysis you should clearly identify the main question you want your data to answer. In other words, what is the treasure that you want to find? Content analysis is a long, strenuous process and having a specific goal will help direct you along the way. To build off our example from the last post, a survey that asks the open ended question, “Do you feel that the library is an essential community resource, why or why not?” may have a driving analysis question of, “How can libraries increase a population’s sense of community?”
However, as the analyst, you may now be staring at fifty lengthy responses, all of which have a person behind them with their own unique perceptions and experiences they want to share with you. You know there is useful information within the responses, and you want to make sure you are considering everyone’s responses by using correct research methods.
This means it’s time to read your data, then read it again! While it takes both patience and time, this step can also streamline the rest of the process. You don’t need to read it with any specific goal in mind except to be open minded, consciously consider any biases you have, and take notes of your general impressions. Instead of fixating on specific responses try to take a step back and look at the data as a whole. You want to know your data thoroughly as you embark on the next step, just as you would want to know the map before setting off on an adventure!
Once you know your data backward and forward it is FINALLY time to start your content analysis with a method called coding. Coding is essentially categorizing the text with descriptive labels, or codes.
Coding has multiple steps, but the process is also repetitive and cyclical. Remember, you can always return to previous steps and adjust something so your analysis better encompasses the data. It’s unlikely you will find the treasure immediately, so always be willing to backtrack if necessary!
Before you create codes for your data, it may help to condense text into sections that hold meaning, called meaning units. Don’t let this step intimidate you. You still want these meaning units to be close to, if not literally, the text of the data. For example, perhaps someone’s response to our example question includes the sentence, “I’ve always enjoyed the library, but it was a particularly great resource for me while raising my children.” A condensed meaning unit you may take from this is “a great resource while raising my children.”
It is important that your meaning units relate as directly to the text as possible, and you are careful not to over-interpret or otherwise misrepresent the responses in the data set .
You begin to interpret the data and take it to a slightly more abstract form with the next step, which is applying codes. An example of coding is taking the meaning unit “a great resource while raising my children” and assigning the label “family support.” If these are not the specific words you would use to describe this meaning unit that is OK. Codes will vary person to person and also change depending on the focus of your driving question.
As long as you work diligently to keep the coding faithful to the text, while acknowledging and limiting your bias from previous experiences on the subject, codes will not be right or wrong. Many codes will be used repetitively throughout your data analysis. You may assign the code “family support” to other meaning units from other survey participants if appropriate. There will likely be certain codes that are used often and other outlying codes that are not. There is also no right or wrong answer for the number of codes that you use. It depends on the size of your data set and the variations within it.
It can be helpful to use your intuition while creating codes as long as you are still basing these labels in the text and staying aware of how your biases will affect their selection. Similarly, there may be aspects of a map you intuitively understand, but it wouldn’t be very smart to throw the map away entirely and assume you know the way yourself.
While coding you may need to change meaning units that suddenly don’t make sense moving forward. Remember this backtracking is a normal part of the process to make sure the codes you are using reflect the whole of the data the best that they can.
After creating codes and applying codes to your data your analysis is off to a good start! Stay tuned next week to learn where to go from here. We will explore categorizing the codes you create to find themes and finish your analysis!
Here is a quick reflection on what this post covered:
- Content analysis is a common method for qualitative analysis that categorizes data to reveal key research findings.
- There is a lot of preparation involved in this long process. Take notes to track your work and know your research question and data thoroughly!
- Meaning units help you identify the meaningful parts of the text that you will code.
- Codes are descriptive labels you apply to meaningful parts of your data to make sense of it all.
- Your intuition can be helpful, but only if you are aware of how your biases may affect your analysis. Find a balance!
LRS’s Between a Graph and a Hard Place blog series provides instruction on how to evaluate in a library context. To receive posts via email, please complete this form.