57 Analysis of Qualitative Interview Data

Analysis of qualitative interview data typically begins with a set of transcripts of the interviews conducted. Obtaining said transcripts requires having either taken exceptionally good notes during an interview or, preferably, recorded the interview and then transcribed it. Transcribing interviews is usually the first step toward analyzing qualitative interview data. To transcribe an interview means that you create, or someone whom you have hired creates, a complete, written copy of the recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. In general, it is best to aim for a verbatim transcription, one that reports word for word exactly what was said in the recorded interview. If possible, it is also best to include nonverbal responses in the written transcription of an interview (if the interview is completed face-to-face, or some other form of visual contact is maintained, i.e. Skype). Gestures made by respondents should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by respondents.

If you have the time (or if you lack the resources to hire others), it is best to transcribe your interviews yourself. If the researcher who conducted the interviews transcribes them herself, that person will also be able to record associated nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. Interviewees may roll their eyes, wipe tears from their face, and even make obscene gestures that speak volumes about their feelings; however, such non-verbal gestures cannot be recorded and being able to remember and record in writing these details as it relates to the transcribing of interviews is invaluable.

Overall, the goal of 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 interview data often works inductively (Glaser & Strauss, 1967; Patton, 2001).  To move from the specific observations an interviewer collects to identifying patterns across those observations, qualitative interviewers will often begin by reading through transcripts of their interviews 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 interview data by reading and rereading (and rereading again) interview transcripts until the researcher has a clear idea about what sorts of themes come up across the interviews. It helps achieve the goal of data management and data reduction (Palys & Atchison, 2014, p. 304).

Coding can be inductive or deductive. Deductive coding is the approach used by research analysts who have a well-specified or predefined set of interests (Palys & Atchison, 2014, P. 304). The process of deductive coding begins with the analyst utilizing those specific or predefined interests to identify “relevant” passages, quotes, images, scenes, et cetera to develop a set of preliminary codes (often referred to as descriptive coding). From there, the analyst elaborates on these preliminary codes, making finer distinctions within each coding category (known as interpretative coding). Pattern coding is another step an analyst might take as different associations become apparent.  For example, if one was studying at-risk-behaviours in youth and discovered that the various behaviours had different characteristics and meanings depending upon the social context (i.e. school, family, work) in which the various behaviours occurred, one has identified a pattern (Palys & Atchison, 2014, p. 304).

In contrast, inductive coding begins with the identification of general themes and ideas that emerge as the researcher reads through the data. This process is also referred to as open coding (Palys & Atchison, 2014, p. 305). It is called open coding for a reason—keep an open mind. Open coding will probably require multiple go-rounds. As you read through your transcripts, it is likely that you will begin to see some commonalities across the categories or themes that you’ve jotted down (Schmitz, 2012). The open coding process can go one of two ways: either the researcher elaborates on a category by making finer, and then even finer distinctions, or the researcher starts with a very specific descriptive category that is subsequently collapsed into another category (Palys & Atchison, 2014, p. 305).  In other words, the development and elaboration of codes arise out of the material that is being examined.

The next step for the research analyst is to begin more specific coding, which is known as focused or axial coding. Focused coding involves collapsing or narrowing 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. Then give each collapsed/merged theme or category a name (or code) and identify passages of data that fit each named category or theme. To identify passages of data that represent your emerging codes, you will need to read through your transcripts yet again (and probably again). You might also write up brief definitions or descriptions of each code. Defining codes is a way of making meaning of your data and of developing a way to talk about your findings and what your data means (Schmitz, 2012).

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 (Schmitz, 2012). Lofland and Lofland (1995, p. 2001) identify a set of questions that are useful when coding qualitative data. They suggest asking the following:

1. Of what topic, unit, or aspect is this an instance?
2. What question about a topic does this item of data suggest?
3. 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 are reading can help you begin to identify and name potential themes and categories.

Table 10.2 “Interview coding example” is drawn from research undertaken by Schmitz (see Schmitz, 2012) where she presents two codes that emerged from her inductive analysis of transcripts from her interviews with child-free adults. Table 10.2 also includes 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 and Atlasti. 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 interview 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.

Text Attributions

• This chapter is an adaptation of Chapter 9.2 in Principles of Sociological Inquiry, which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.