After having collected the data, the next stage is data analysis. But before you actually set out to collect the data, your research strategy should be ready. This is because you must know the data analysis technique to be used before your proceed for data collection.
What statistical tests would be appropriate for your study would depend on the following factors:
- Your Research Hypotheses
- The Research Design in use
- The type of Data
You must be very clear about your research question or what you call as research hypotheses before creating the design for your research. Your hypotheses and design, in principle, would let you know the which statistical tests would be the most appropriate to run on your data so that your research questions are answered in the most appropriate manner.
Few important things to take care of when taking up your data analysis are:
- Relevance: You must never blindly follow the collected data. All the presented data must be relevant and appropriate to the aims of the research and you must be able to give the applicable logic for data selection and analysis. You should justify your statistical techniques with the same strength as the data collection tools. It must convince that your method was not chosen haphazardly and rather was decided after prolonged research and reasoning.
- Thoroughness of work: You should thoroughly analyse all data and use it to demonstrate the critical perspective, keeping in mind the scope of errors. It is important to accept your limitation and strengths to add credibility to your research.
- Presentational Device: When the data is large in number, it becomes a problem to represent this data. Consider all available means to present what is collected by means of Charts, Graphs, Diagrams, Quotes, formulae, tables etc. You must keep your reader in mind while presenting your data, which means that your analysis should also be clear to someone who is not familiar with your research.
- Discussion: In the discussion aspect of your data analysis you will have to exhibit the different trends, patterns and themes that can be figured out from the data. Assess the significance of each theoretical interpretation on the other.
Findings: This should include the essential conclusive outcomes that emerge from your data analysis. Findings should be clearly stated and the assertions should be backed by clearly stated and argued reasoning and empirical support.