5.8 Analysis

In our financial exploitation example, as is the case with many research projects, most of the analysis will be statistical. While discussing all the different statistical methods is beyond the scope of this chapter, it is important to understand that a poor research design will lead to poor data. You can run an analysis on these data and the program will deliver a calculation, but any conclusions drawn based on that calculation will be faulty. If we have set up our research project correctly, we can have faith in the conclusions drawn from the analysis.

One reason why reliability and validity, which we discussed earlier, are so important is because research is often used not just to produce immediate findings, as these conclusions are used to generalise to a broader group or even predict an outcome or future event. If the current study is not done well, then any attempt to draw broader conclusions or anticipate what the future holds will be incorrect as well.

Since we took care in setting up our research projects, we can proceed with the analysis. In both our statistical analysis of survey responses and our qualitative analysis of interview responses, it is important to be aware of the translation-like process through which this non-academic knowledge that was shared with us is converted into conclusions by those of us in the academy. There may be important differences between “community or elder” and “academic” knowledge that needs to be identified so Indigenous viewpoints and knowledge can be acknowledged rather than silenced, even if they wrote something or answered questions in ways other than what we expected. At this stage, it is helpful to reflect on how our findings may overlap or diverge from the findings of past researchers and think about why that may be the case.