Qualitative Data
Data analysis for a qualitative study can be complex because of the variety of types of data that can be collected. Qualitative researchers aren’t attempting to measure observable characteristics; they are often attempting to capture an individual’s interpretation of a phenomenon or situation in a particular context or setting. This data could be captured in text from an interview or focus group, a movie, images, or documents. Analysis of this type of data is usually done by analysing each artefact according to a predefined and outlined criteria for analysis and then by using a coding system. The code can be developed by the researcher before analysis, or the researcher may develop a code from the research data. This can be done by hand or by using thematic analysis software such as NVivo.
Interpretation of qualitative data can be presented as a narrative. The themes identified from the research can be organised and integrated with themes in the existing literature to give further weight and meaning to the research. The interpretation should also state if the aims and objectives of the research were met. Any shortcomings with research or areas for further research should also be discussed (Creswell,2009)*.
NVivo is the premier software that supports qualitative and mixed-methods analysis of textual and audiovisual data. Qualitative researchers use NVivo to help them organize, analyze, and visualize their data and find patterns. NVivo is available for Windows and Mac. It can be used to, but is not limited to:
You can access some information from these NVivo guides. All UNIZULU students and researchers can go to the ICT Department to have this software downloaded on their devices.
Learn how to use NVivo by exploring free articles, watching demo videos, and finding answers to any question you might have about the software.
If the data is numeric you can use a software package such as SPSS, Excel Spreadsheet or “R” to do statistical analysis. You can identify things like mean, median and average or identify a causal or correlational relationship between variables.
Data analysis for mixed methods involves aspects of both quantitative and qualitative methods. However, the sequencing of data collection and analysis is important in terms of the mixed method approach that you are taking. For example, you could be using a convergent, sequential or transformative model which directly impacts how you use different data to inform, support or direct the course of your study.
The intention in using mixed methods is to produce a synthesis of both quantitative and qualitative information to give a detailed picture of a phenomena in a particular context or setting. To fully understand how best to produce this synthesis it might be worth looking at why researchers choose this method. Bergin**(2018) states that researchers choose mixed methods because it allows them to triangulate, illuminate or discover a more diverse set of findings. Therefore, when it comes to interpretation you will need to return to the purpose of your research and discuss and interpret your data in that context. As with quantitative and qualitative methods, interpretation of data should be discussed within the context of the existing literature.