Presentation of quantitative data can be equally as challenging as the presentation of qualitative data, but for very different reasons. For example, with the qualitative data you might be concerned about length. Quantitative data poses the risk of overwhelming the reader with numbers, statistics, and percentages that can make heads spin with confusion.
Something to consider first with numeric data is that presentation style depends what department you are submitting to. In the hard sciences, there is likely an expectation of heavy numeric input and corresponding statistics to accompany the findings. In the arts and humanities, however, such a detailed analysis might not be as common. Therefore as you write out your quantitative findings, take your audience into consideration.
Just like with the qualitative data, you must ensure that your data is appropriately organised. Again, you’ve likely used a software program to run your statistical analysis, and you have an outline and subheadings where you can focus your findings. There are many software programs available and it is important that you have used one that is most relevant to your field of study.
For some, Microsoft Excel may be sufficient for basic analysis. Others may rely on SPSS, Stata, R, or any of the other programs available through your institution or online. Whatever program you have used, make sure that you document what you have done and the variables that have affected your analysis.
One common mistake found in student writing is the presentation of the statistical analysis. During your analysis of the data, you are likely to have run multiple different analyses from regressions to correlations. Often, we see students presenting multiple different statistical analyses without any real understanding of what the tests mean.
Presentation of quantitative data is more than just about numbers and tables. You must explain your results and justify why you have run/presented the tests that you have. You could also explain how they relate to the research question. However, depending on how you have organised your work, this might end up in the discussion section.
Students who are not confident with statistical analysis often have a tendency to revert back to their secondary school mathematics skills. They commonly document the mean, median, and mode for all of their results. Now, these three outcomes can be important. But having a good understanding of why you are proceeding with this strategy of analysis is going to be essential in a primarily quantitative study.
That noted, there are different expectations for an undergraduate dissertation and a PhD thesis, so knowing what these expectations are can be really helpful before you begin.

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