Data analysis involves inspecting, cleaning, transforming, and (often) computationally modeling data to find information and trends within a dataset. Data analysis helps turn data into meaningful information about the objects represented through the data.
You will need to organize and interpret the data you collected to give meaning to it, and to answer your research question. Your choice of methodology points the way to the most suitable method of analyzing your data.
If the data is numeric you could use SPSS, Excel, Google Sheets, or “R” to do statistical analysis. You can identify things like mean, median, and average, or identify a causal or correlational relationship between variables.
The University of Connecticut has useful information on statistical analysis.
If your research sets out to test a hypothesis your research will either support or refute it, and you will need to explain why this is the case. You should also highlight and discuss any issues or actions that may have impacted on your results, either positively or negatively. To fully contribute to the body of knowledge in your area be sure to discuss and interpret your results within the context of your research and the existing literature on the topic.
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 phenomena 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 analyzing each artifact 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 organized 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 or areas for further research should also be discussed.
For further information on analyzing and presenting qualitative data, read this article in Nature.
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.