Data Analysis Qualitative Research Dissertation

The most important task that you will undertake in your PhD research project is the analysis of data. The integrity and rigour of your research is utterly dependent on getting the analysis right and it will be intensely scrutinised by the external examiners.

Think of it this way, you started out with a set of research questions or phenomena that you wanted to explore. You decided on the methodology and the research methods that would be most effective for the purpose.

You have gathered the data accordingly and now you have to undertake analysis to produce the findings that will contribute to new knowledge within the discipline. Get it right and your methodology would have been proven fit for the purpose. Get it wrong and your competence at planning and implementing a high-level research project will be severely questioned.

If you do not undertake the analysis correctly then you will make it difficult for yourself to move to the next stage, the crucial stage of writing up your findings. If your findings are vague and inconclusive then your whole thesis will be worthless.

Tips for conducting good qualitative data analysis

  • Gather all your interview transcripts and field notes together in preparation for coding.
  • Use a coding technique that is appropriate for your research project. APRIORI codes are linked to the research questions you started out with so these will help you to link the data back to those research questions.
  • EMPIRICAL codes are created from the emerging data so are essential in identifying new phenomena that you had not necessarily predicted.
  • Go through each piece of data slowly and carefully adding codes, making additional notes and highlighting any quotes you may wish to use.
  • Once this has been completed and you have surveyed the coded data, examine it again for similarities and differences (constant comparative method). A great deal of interpretation is required at this stage and you might want to make research notes on your choices as an additional validity tool.
  • After you have completed the comparative analysis the data must be re-examined to identify broader conceptual themes. These should be fairly easy to identity as they should link back to your theoretical framework and so you see theories jumping out at you.

The conceptual themes will help you plan the chapter headings for the remainder of your thesis. If you have done a good job of the coding and analysis then in writing up and discussing your findings you should be able to demonstrate how your findings answer your research questions, or what phenomena you have identified in your research exploration, highlighting any new theories (we’re talking micro here not macro) that have emerged.

Questions you should be asking yourself once you have completed the data analysis and before writing up relate back to your literature review. The purpose of the literature review is to critically analyse the literature related to the topic and identify any gaps.

So you ought to be thinking about whether your research that you have now analysed has helped to fill any of those gaps – if not then why? Is there a flaw in the design of your research or in your sample? In the case of interviews, did you interview the right people? Did you ask the right questions? Have you interpreted the responses correctly?

The more you examine and re-examine the data then the easier it becomes for you to identify the conceptual themes and emerging theories, enabling you to write with conviction and authority for the remaining chapters of your thesis. So make sure you nail the analysis!

Has this post helped you? If so then please leave a comment!

SEE ALSO: Inductive and deductive approaches to research & Using Conceptual Frameworks in Qualitative Research

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To some qualitative data analysis may seem like a daunting task. Some quantitative researchers openly admit they would not know where to begin if given the job, and that the unfamiliar process scares them a bit. Unlike most quantitative methodologies, qualitative analysis does not follow a formula-like procedure that can be systematically and analytically applied. When we embark on a qualitative journey, we need to be prepared to work in a slightly more intuitive and not always tangible way. But that does not imply qualitative methodology lacks rigor. On the contrary — it just achieves results in a different way to a quantitative study.

Do not let this post’s title fool you, qualitative analysis is not an easy task. Often time-consuming and at times slightly chaotic, the researcher generally never knows where the study will take them. But, hey, that’s also the beauty of the qualitative method and its hidden potential.

Reading interviews multiple times to get familiar with your data is where most qualitative researchers start. In qualitative research, we immerse ourselves into the study; we do not first start to seek objectivity, but rather closeness. Remember, as a qualitative researcher you are the research tool.

Which method of analyzing to choose?

As a novel researcher, it might be best to stir away from some types of qualitative research methodology and analysis. Grounded theory, for example, might be a bit too complex and ambitious to undertake as your first assignment (if you really want to implement it properly). It might be safer to initially choose a more relaxed way of dealing with your material.

If you have conducted qualitative interviews, here are three methods that can be used to analyze your data:

  1. Thematic content analysis

This is probably the most common method used in qualitative research. It aims to find common patterns across a data set. It usually follows these steps:

  • Getting familiar with the data (reading and re-reading).
  • Coding (labeling) the whole text.
  • Searching for themes with broader patterns of meaning.
  • Reviewing themes to make sure they fit the data.
  • Defining and naming themes.
  • The write-up (creating a coherent narrative that includes quotes from the interviewees).
  1. Narrative analysis

This approach is becoming increasingly popular, especially in social sciences. As the name suggests, it is about making sense of stories. It can follow these steps:

  • Gather the stories.
  • Analyze each story and look for insights and meanings.
  • Compare and contrast different stories; look for interpretations.
  • Create a new story that connects the previous ones in a novel and insightful way.
  1. A deductive approach

In some cases, it is possible to use a somewhat non-qualitative approach. Deductive approach means that you already have a predetermined framework for the of analysis. The researcher (you) then uses this framework to analyze the data (i.e. news clippings, transcripts, interviews, etc.) In this approach, the researcher tests his or her pre-existing theories. Themes and concepts are decided before the analysis starts and are imposed on the material. This approach is relatively easy and quick, however, it generally can only be used when you are not seeking depth and new understanding.

Using computer software for data analysis

The good old days when qualitative researchers could be found endlessly rearranging Post-it Notes are probably coming to an end in the near future. Some still prefer the nostalgic pen and paper method of organizing their research material; however, increasing number of researchers now make use of computer programs such as ATLAS.ti or NVivo to help manage their data. This does not mean the computer simply performs the analysis — that is still the job of the researcher. These software programs can nevertheless help us organize, retrieve and present our data in an effective and more coherent way.

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