11 Qualitative Data Analysis

As discussed in Chapter 6, qualitative methods are often used for interpretive, theory-building research projects. Qualitative data consist of pictures, words, audio files, and similar non-numerical information that require analysis strategies such as thematic coding, narrative analysis, and content analysis. This chapter focuses on techniques for analyzing the various kinds of data that qualitative methods, such as focus groups and field research, tend to generate. Through this discussion, you’ll also learn more about the details of how researchers go about conducting qualitative research studies.

Overview of Qualitative Data Analysis

Qualitative methods such as focus groups, field research, and qualitative interviews (covered in Chapter 12), generate massive amounts of different data forms. In focus groups, the researcher might end up with audio or video recordings of group discussions, field notes about participants’ interactions, and demographic information about participants. In field research, the data might be field notes, audio recordings of interviews, and texts gathered for content analysis. Regardless of which forms of qualitative data the researcher ends up with, they all need to be prepared for systematic analysis that will help answer the research question.

Sometimes the analytic process of field researchers and others conducting inductive analysis is referred to as grounded theory (Charmaz, 2006; Glaser & Strauss, 1967). Grounded theory occurs, as you might imagine, from the “ground up,” with the researcher beginning with an open-ended and open-minded desire to understand a social situation or setting. Using a grounded theory approach to data analysis involves a systematic process, whereby the researcher lets the data guide the inquiry, rather than guiding the data using preset hypotheses. The goal of a grounded theory approach is, perhaps unsurprisingly, to generate theory. Its name implies that discoveries are made from the ground up and that theoretical developments are grounded in a researcher’s empirical observations and a group’s tangible experiences.

As exciting as it might sound to generate theory from the ground up, the experience can be quite intimidating and anxiety-producing, as the open nature of the process can sometimes feel out of control. Without hypotheses to guide their analysis, researchers engaged in grounded theory work may experience frustration or angst. At the same time, the process of developing a coherent theory grounded in empirical observations can be quite rewarding, not only to researchers, but also to their peers who can contribute to the further development of new theories through additional research, and to research participants who may appreciate getting a bird’s-eye view of their everyday experiences.

The overall goal of data analysis is to reach some inferences, lessons, or conclusions, by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. Each type of qualitative data requires somewhat different analytic techniques, and it would be impossible to cover every data type in this textbook. Instead, we’ll focus on the most common types of data: field notes, audio recordings of interviews or focus groups, and texts for content analysis. Analyzing these types of data requires preparing the data for analysis, coding the data, and evaluating common themes resulting from the coding.

Preparing Qualitative Data for Analysis

Field Notes

Analyzing field note data is a process that begins the moment a researcher enters the field, and continues throughout their time in the field, as they write up notes and consider what their interactions and notes mean. In the field, a researcher generally takes descriptive field notes, that describe a researcher’s observations as straightforwardly as possible. These notes typically do not contain explanations or comments about their observations. Instead, the observations are presented on their own, as clearly as possible. Analyzing field notes involves moving from descriptive field notes to analytic field notes. Analytic field notes include the researcher’s impressions about their observations.

Often field notes will develop from a more descriptive state to an analytic state when the field researcher exits a given observation period and sits at a computer to type their notes into a more readable format. We’ve already noted that carefully paying attention while in the field is important; so too is what goes on immediately upon exiting the field. Field researchers typically spend several hours typing up field notes after each observation. While creating and preparing the data for analysis, the researcher also begins analyzing the data. In this setting outside the field, researchers reflect on their experiences in the field, and what their observations might mean.

Audio Recordings

Analysis of audio data typically begins with transcribing the audio into written form. To transcribe an audio file means that you or someone you hired creates a complete, written copy of the recording, by playing the recording back, typing in each word spoken on the recording, and noting who spoke which words. Researchers generally aim for a verbatim transcription that reports everything said in the recording, exactly as the speakers said it. In addition to the words spoken, a verbatim transcription should include verbal cues such as laughing and filler words (e.g., uh’s um’s, etc.), notes on nonverbal cues such as tone of voice, and when and how respondents emphasized specific spoken words.

Transcribing audio files can be extremely time-consuming. Some researchers pay for transcription services while others transcribe audio themselves. When I transcribed interviews from my community responses project, I averaged about five minutes of transcription time for every minute of audio recording. That means a one-hour interview would take five hours to transcribe! And, those files were interviews rather than focus groups or other events, where a researcher might have to distinguish between multiple voices and narrative threads in the transcription. Despite the time it takes to transcribe audio files, I think it’s worth it. When researchers transcribe their own files, they become immersed in the data. Patterns begin to emerge in what people are saying. Listening to conversations you participated in or observed can spark recall of nonverbal cues or other interactions you’d forgotten to include in your field notes. These can contribute to richer data and put a researcher on the path to data analysis.

Texts

Preparing texts for content analysis depends on the type of texts the researcher has collected. Audio files need to be transcribed, as explained in the previous section. Written texts should be compiled and organized in a way that makes sense for the aims of the research (e.g., by source, theme, or type of text). In other words, preparing texts for content analysis requires organizing the texts into forms that allow for systematic review in the next analytic stages.

Coding Qualitative Data

Once the researcher has prepared their qualitative data for analysis, they begin looking for patterns across the data, by reading through their data files and trying to identify codes. A code is a shorthand representation of related, complex issues or ideas. The process of identifying codes in one’s qualitative data is often referred to as coding. Coding involves identifying themes across data by reading and rereading (and rereading) the data until the researcher has a clear idea about themes across the data points.

As you might imagine, wading through all this data can be quite a process. Luckily, some computer programs can help qualitative researchers sort, code, and analyze their data. Programs such as NVivo and Atlasti are designed to assist qualitative researchers with organizing, managing, sorting, and analyzing large amounts of qualitative data. The program allows researchers to import electronic documents, label passages with codes, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes.

A researcher might engage in two types of coding during this process. First, open coding is a process by which the researcher reads through each data file, line by line, and notes whatever categories or themes seem to jump out as important. During open coding, researchers try not to let their original research question or expectations influence the categories or themes they see. In other words, researchers must keep an open mind during open coding. Open coding usually requires multiple go-rounds so that researchers can be sure they’ve identified all possible codes.

Sometimes researchers find themselves struggling to identify themes at the open coding stage. When this happens, they can ask themselves questions about their data; the answers then give clues about what themes or categories might emerge. Some questions might include: Of what topic, unit, or aspect is this an instance? What question about a topic does this item of data suggest? What sort of answer to a question about a topic does this item of data suggest (i.e., what proposition)? (Lofland and Lofland, 1995). Asking these questions about passages of data can help identify and name potential themes and categories.

As researchers pore over their data, they start to see patterns or commonalities across the categories or themes they’ve identified. Once this happens, they might begin focused coding. Focused coding involves collapsing or narrowing themes and categories identified in open coding, by reading through the notes made while conducting open coding. This process can involve identifying themes or categories that seem related, and perhaps even merging some that seem similar, to warrant their own unique codes. The researcher then gives each collapsed/merged theme or category a name (or code) and identifies passages of data that fit each named category or theme. To identify these passages, the researcher reads through their data again, marking each with the applicable code or codes. During the coding process, the researcher might also create a codebook, a document including brief definitions or descriptions of each code. The codebook can help researchers review their data to ensure they have marked passages with the relevant codes.

Analyzing Qualitative Data

Recall from the beginning of this chapter, that the overall goal of data analysis is to reach some inferences, lessons, or conclusions, by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. The analysis process for qualitative data is not distinct from the preparation and coding stages. For example, while transcribing audio files, the researcher is also beginning to identify themes (coding) and make sense of those themes (analysis). Even creating a codebook is a way of making sense of data and developing a way to discuss the findings. Thus, analyzing qualitative data occurs throughout the entire process of an inductive, qualitative research process. Researchers conducting these studies begin analyzing when they start a focus group, enter the field, or gather texts. By the time the researcher has prepared their data, identified codes, marked passages with those codes, and developed definitions of each code, the data have been condensed into a manageable form that allows the researcher to report on their findings in ways that make sense to a larger audience.

Summary

• Grounded theory is a bottom-up method of analyzing qualitative data that starts with empirical observations, and works to build a theory based on those observations.
• Preparing field notes for analysis requires typing up all the notes, and moving from descriptive to analytic field notes. For audio recordings, the preparation process involves transcribing the recordings word-for-word. How a researcher prepares texts for content analysis depends on the type of text.
• Coding is the process of looking for patterns and identifying themes in a researcher’s data. The process involves many close readings of the data during which the researcher labels passages with relevant codes. Researchers generally start with open coding and narrow their attention to focused coding.
• The main goal of data analysis is to condense large amounts of data into more manageable information the researcher can use to reach conclusions about their data.

Discussion Questions

1. Read more about grounded theory at the Grounded Theory Institute’s website. What do you think about grounded theory? Is this way of conducting research something interesting to you? Why or why not?
2. Conduct the practice field research explained in Chapter 10, discussion question 1. Then, prepare your field notes for analysis by typing up all of your notes, and adding your own insights to create some analytic field notes. How long did this process take you for notes from a 15-minute observation period? What did this experience tell you about preparing field notes for analysis?
3. Choose a podcast episode from Give Methods a Chance.  Transcribe the first 2 minutes of the podcast. Be sure to type exactly what the speakers say; indicate who said what, the tone of voice, and any other cues you hear. How long did this process take you for 2 minutes of audio? What did this experience tell you about preparing audio data for analysis?
4. Use the field notes or audio transcript you typed up for questions 2 or 3 above, to practice your coding skills. Start with open coding, and then move to focused coding. Create a codebook with at least two codes and their definitions. How long did this coding process take you? What did you learn about coding from this experience?

Works Cited in Chapter 11

Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Thousand Oaks, CA: Sage.

Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, IL: Aldine.

Lofland and Lofland (1995). Lofland, J., & Lofland, L. H. (1995). Analyzing social settings: A guide to qualitative observation and analysis (3rd ed.) Belmont, CA: Wadsworth.