Research data remain meaningless until you analyze them. Through data analysis, you give meaning to the data: What can the data tell us about X? While the importance of Collecting data is often emphasized for PhD students, a lot of data may remain under-analyzed, especially in qualitative research. To many academics, data analysis can feel like tedious or even unnecessary work, even though a thorough data analysis can be critical to truly insightful research.
Different data analysis methods may be appropriate to different research aims. For instance, quantitative data analysis is typically more suitable for testing theory (hypotheses), whereas qualitative data analysis is typically more suitable for developing theory.
As types and sources of (research) data are almost endless, it seems impossible to provide a brief yet comprehensive guide on how to analyze data. A common distinction is made between quantitative and qualitative data analysis methods. Quantitative data analysis methods often involve statistics, whereas qualitative methods often involve coding.
The “how” of data analysis, then, is highly dependent on your research aim and the Methods you choose. To facilitate your research and your eventual Writing process, consider the following generic tips: