13.5 Analyzing qualitative data

Analysis of qualitative data typically begins with a set of transcripts of the interviews or focus groups conducted. Obtaining these transcripts requires having either taken exceptionally good notes or, preferably, having recorded the interview or focus group and then transcribed it. Transcribing audio recordings is usually the first step toward analyzing qualitative data. Researchers create a complete, written copy, or transcript, of the recording by playing it back and typing in each word that is spoken, noting who spoke which words. In general, it is best to aim for a verbatim transcript, one that reports word for word exactly what was said in the recording. If possible, it is also best to include nonverbals in a transcript. Gestures made by participants should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by participants. Because these are difficult to capture via audio, it is important to have a note-taker in focus groups and to write useful field notes during interviews.

If you have the time (or if you lack the resources to hire others), I think it is best to transcribe your qualitative data yourself. I never cease to be amazed by the things I recall from an interview or focus group when I transcribe it myself. If the researcher who conducted the interview or focus group transcribes it herself, that person will also be able to make a note of nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. Participants might roll their eyes, wipe tears from their face, and even make obscene gestures. These nonverbals speak volumes about participants’ feelings. Unless you write them down in your field notes or include them in your transcript, those details cannot inform your analysis.

The goal of qualitative 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. Analysis of qualitative data often works inductively (Glaser & Strauss, 1967; Charmaz, 2006). ^[1] To move from the specific observations a researcher collects to identifying patterns across those observations, qualitative researchers will often begin by reading through transcripts and trying to identify codes. A code is a shorthand representation of some more complex set of issues or ideas. In this usage, the word code is a noun. But it can also be a verb. The process of identifying codes in one’s qualitative data is often referred to as coding. Coding involves identifying themes across qualitative data by reading and rereading (and rereading again) transcripts until the researcher has a clear idea about what themes emerge.

Qualitative researcher and textbook author Kristin Esterberg (2002) ^[2] describes coding as a multistage process. Esterberg suggests that there are two types of coding: open coding and focused coding. To analyze qualitative data, one can begin by open coding transcripts. This means that you read through each transcript, line by line, and make a note of whatever categories or themes jump out to you. At this stage, it is important that you not let your original research question or tentative hypotheses cloud your ability to see categories or themes. It’s called open coding for a reason—keep an open mind. You may have even noted some ideas for coding in your field notes or journal entries.

Open coding will probably require multiple rounds. That is, you will read through all of your transcripts multiple times. As you do, it is likely that you’ll begin to see some commonalities across the categories or themes that you’ve jotted down. Once you have completed a few passes and started noticing commonalities, you might begin focused coding. Focused coding is a multistage process. First, collapse or narrow down themes and categories identified in open coding by reading through the notes you made while conducting open coding. Identify themes or categories that seem to be related, perhaps merging some. Once you come up with a final list of codes, make sure each one has a definition that clearly spells out what the code means. Finally, you recode the dataset using the final list of codes, making sure to apply the definition of the code consistently throughout each transcript.

Defining codes is a way of making meaning of your data and of developing a way to talk about your findings. Researchers must ensure that codes are applied in a uniform way in the entire data set during focused coding. In open coding, new codes and shifts in definitions for codes are common. The researcher should keep an open mind and allow the definitions of codes to emerge from reading (and re-reading) the data. However, once focused coding begins, the definitions should not change for any reason. Any deviation will make the data analysis less trustworthy. If there are pieces of data that do not fit with your definition, then it is important to note those deviant cases in your final report.

Using multiple researchers to code the same dataset can be quite helpful. You may miss something a participant said that another coder catches. Similarly, you may shift your understanding of what a code means and not realize it until another coder asks you about it. If multiple researchers are coding the dataset simultaneously, researchers must come to a consensus about the meaning of each code and ensure that codes are applied consistently by each researcher. We discussed this previously in Chapter 9 as inter-rater reliability. Even if only one person will code the dataset, it is important to work with other researchers. If other researchers have the time, you may be able to have them check your work for trustworthiness and authenticity. We discussed these standards for methodological rigor in Chapter 9. Remember, in qualitative data analysis, the researcher is the measurement instrument, determining what is true, what is connected to what, and what it all means.

As tedious and laborious as it might seem to read through hundreds of pages of transcripts multiple times, sometimes getting started with the coding process is actually the hardest part. If you find yourself struggling to identify themes at the open coding stage, ask yourself some questions about your data. The answers should give you a clue about what sorts of themes or categories you are reading. In their text on analyzing qualitative data, Lofland and Lofland (1995) ^[3] identify a set of questions I find very useful when coding qualitative data. They suggest asking the following:

• 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 is suggested)?

Asking yourself these questions about the passages of data that you’re reading can help you begin to identify and name potential themes and categories.

Still feeling uncertain about how this process works? Sometimes it helps to see how qualitative data translate into codes. In Table 13.2, I present two codes that emerged from an inductive analysis of transcripts from interviews with child-free adults. I also include a brief description of each code and a few (of many) interview excerpts from which each code was developed.

As you might imagine, wading through all these data is quite a process. Just as quantitative researchers rely on the assistance of special computer programs designed to help with sorting through and analyzing their data, so too do qualitative researchers. Where quantitative researchers have SPSS and MicroCase (and many others), qualitative researchers have programs such as NVivo (http://www.qsrinternational.com) and Atlas.ti (http://www.atlasti.com). These are programs specifically designed to assist qualitative researchers with organizing, managing, sorting, and analyzing large amounts of qualitative data. The programs work by allowing researchers to import transcripts contained in an electronic file and then label or code passages, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes. They even include advanced features that allow researchers to code multimedia files, visualize relationships between a network of codes, and count the number of times a code was applied. Having completed a handwritten coding process as part of a class project with a rather old-school professor, I’m happy I can use qualitative data analysis software to save myself time and hassle.

To summarize, the following excerpt, from my paper analyzing the implementation of self-directed supports for individuals with intellectual and developmental disabilities summarizes how the process of analyzing qualitative data can work:

Transcribed interviews were analyzed using Atlas.ti 7.5 (2014) qualitative data analysis software, a commonly used program in qualitative social science. The researchers approached data analysis from an inductive perspective, allowing themes to emerge from the data. As described by Braun and Clarke (2006), the thematic analysis proceeded along six sequential phases: (a) familiarizing with the data set, (b) generating initial codes, (c) searching for themes, (d) reviewing themes, (e) defining and naming themes, (f) and reporting data. One member of the research team conducted the coding and thematic analysis, consulting with a peer reviewer at the end of each of the three passes of coding and the entire research team after the coding process was complete. The peer reviewer reviewed each phase of coding for consistency, and worked with the primary coder to identify, review, and name themes. At the end of coding, the entire research team reviewed the themes and established a shared meaning that best reflected the narratives of participants, based on a series of dialogues. The themes were organized into a thematic map which was refined through consultation with the research team to ensure homogeneity within each theme and heterogeneity between themes. The analysis contained within this paper used co-occurrence counts as a guideline for the prevalence of themes within the data set. Thus, the analysis is limited to the most prevalent themes that answer each research question, while attending to exceptional or divergent cases. Methodological journaling related to coding and peer review helped to ensure the dependability, confirmability, and trustworthiness of the final research product (DeCarlo, Bogenschutz, Hall-Lande, & Hewitt, in press). ^[4]