Whether your data is qualitative or quantitative, you need to analyze it before you can write about it. When you analyze your data, pay particular attention to the following items.
What types of patterns can you find in your data? Where are there places where people seem to agree or disagree with each other?
If you have quantitative data, this is a good time to do at least some basic mathematical analysis: what is the mean (average value), median (middle value), and/or mode (most often represented value)? If you can, running statistical analyses makes good sense here, too.
If you have qualitative data, patterns can still be observed/noted by examining where similar answers were given or where similar behavior patterns were exhibited.
If you conducted a survey or completed observations, it is probably a good idea to do some deeper analysis here, too, in order to compare how certain subsets of your sample group responded or behaved. For example:
- Did the first-year college students report feeling differently about a variable than the seniors did?
- Did the men and women behave in any discernibly different manners in your observations?
These types of comparisons can often lead to moments of insightful analysis in the discussion of primary research results.
As you find patterns in your data, you will also find data points that don’t follow those patterns. This is normal; not all respondents or interactions will answer or function in the exact same way. Your task as a researcher is to analyze these data points.
- Are they random outliers?
- Are there patterns to the outliers as well?
- Are there other possible explanations for their existence in your data set?
You especially want to explain any variations when they can lead to further insights into your results or possible future research projects.
(Dis)Connections to Prior Research
One final step you want to take with your data is to see how well it matches up with prior research. This requires that you go back to the articles that informed your literature review and compare and contrast your results with those that prior studies collected. Take note of where and how your results converge and diverge with prior data sets, and identify reasons for those similarities and differences.
Please note that differences are not necessarily negative: you likely introduced one or more new variables or gathered a different sample set in one or more new ways. If your results vary from prior studies’ results, then, yours may indicate the possibility of having gained some new knowledge about the topic.
Likewise, confirmation of prior results can also be helpful: replicating past studies’ results is sometimes, in itself, a worthwhile endeavor, as various scientific branches have begun confronting what has been termed the Replication Crisis. In short, many published studies have not had their results replicated by other researchers, causing their findings to become doubted.
Lastly, if your results’ connections to prior research are not clear, that may indicate any number of issues, including that your secondary research may not have been focused well enough or that your primary study may have design and/or methodological flaws. The limitations applicable to your study—remember that all studies have limitations—should therefore be included in your discussion of your results.