Welcome back to scienceIQ! Qualitative research explores the quality of the situation/phenomenon. It has nothing to do with p-values or mean differences. The purpose of qualitative research is to understand the phenomenon and NOT to generalize the findings. Qualitative research is not so popular in the health sciences. It originated and developed from social sciences. In this post, I will be discussing a few simple steps to analyze qualitative data.
We tend to think that conducting qualitative research is easier compared to quantitative research, but, in my experience, it is the opposite. A qualitative study requires a much deeper understanding of the data. It has to be supported by theoretical background. The analysis of qualitative data does not happen with a click of a button. Instead, it requires a lot of brainstorming and visiting and re-visiting the data. You need to be intellectually involved and accountable for your findings. It is more like art, where we attempt to draw a somewhat realistic picture of the phenomenon. I have talked more about it in my last post on qualitative research (https://scienceiq.blog/2019/08/14/qualities-of-a-qualitative-researcher/)
So, here I would be discussing a few simple steps to kick start your data analysis:
STEP 1: Transcribe your data – The most common methods of qualitative data collection are focus group and interviews, which is audio taped. So, as a very first step, we need to transcribe the interviews/focus group discussions into our laptops. The transcription has to be word-to-word, as told by the participants. You can’t change the sentences or meaning of the data while transcribing. It is called as ‘verbatim’ – exact words of the participants. I must tell you that transcription is a verrryyy lengthy and tedious task. You have to listen to each sentence carefully from the audio recording and type it as it is. It may take days to complete the transcription of one interview. So, you have to type, type, and type.
STEP 2: Read and re-read your transcripts – What I learned during a data analysis workshop was to read each transcript (the transcribed interviews) thrice. Once you should read superficially to have an idea about the content, then read twice with more details and understanding before starting to code your data. It is known as ‘deep hanging out’ with your data. Read and think and then think and read. It involves a lot of cognitive processing.
STEP 3: Start coding – Codes are the summary of the phrases and sentences of your interviews. Through coding, we are trying to condense the large chunk of information that we have collected in our research. It means labeling the text. E.g., If I have a verbatim such as “I was satisfied with the treatment, it made me recover better. I keep telling my friends about it,” so I would condense this piece of information by labelling it as ‘patient satisfaction.’
GOLDEN RULES OF CODING:
- Be as close to the text as possible.
- Don’t be too broad or too specific while developing codes. Codes have to be repeated in other transcripts as well, so keeping it very specific will not make it reusable in other transcripts.
- The context of the text is equally important.
Thus, a better code would be ‘patient satisfaction with treatment.’
STEP 4: Data analytical cycle – Once you have completed coding of all the transcripts, then start the analysis. It is a cyclic process that involves going back and forth to the data. Data analytic cycle consists of the following components:
a) Description b) Comparison c) Categorization d) Conceptualization and e) Theory development.
Description is often called ‘thick description’ in qualitative research because it has to be as explicit as possible. When describing, we give a detailed account of the phenomena. It includes describing a) breadth (range of experiences & different dimensions), b) depth (what & why) and, c) context of the phenomenon (where, when who.)
Comparison means comparing the data between cases. Here you compare the information provided by different categories of participants, e.g., young versus old, rich versus poor, acute versus chronic, urban versus rural, so on, and so forth. You have to compare and contrast the perceptions/experiences of the participants and develop meaning out of it. You should know the reason for the difference (if any) in their opinions. You should also know if there is something which is left unexplored in your study.
Categorization is a relatively simpler task in the data analytic cycle. It involves grouping similar codes into broader categories or themes. E.g., if I have codes such as anger, anxiety, frustration, etc., then I would group it as ‘negative attitude.’ Remember, the code groups have to be based on your research objective and the primary question that you aimed to answer. Once you have categorized the codes, you can start writing the details of each category/theme.
Conceptualization is a very crucial aspect of qualitative data analysis, and new learners often miss it. It involves making links between categories and finding a bigger picture from your data. You have to summarize the core concepts that were derived from your data. It includes telescoping – that means zooming in and zooming out of your data to understand the concepts. I know it sounds a little daunting, and it is… but you will be able to do it with sound knowledge in your research area.
Theory development is the last and eureka moment in qualitative studies. We usually do not develop theories from every research we conduct. But with years of experience and research in a particular field, you may develop a new theory, or verify an existing one or contradict a current theory or even add to an existing method.
So, in summary, transcribe your data, code it well, group codes into categories, compare, and conceptualize. Some of the software programs that can help in qualitative analysis are:
- Open Code 4.2
Many people have asked me about the software for qualitative analysis. They think that using the software will analyze the data, just like SPSS. But you should know that software doesn’t analyze qualitative data. Your brain, your experience, and reflexivity during data collection help in data analysis. It is you who can explain your qualitative findings and not the software. The software just manages the data and helps in organizing it.
That was all on qualitative data analysis. There are several other ways and methods to approach qualitative data, but I felt these were a few simple steps to begin with. I hope you found it useful.