Data Analysis

As with any of the previous topics I could probably run several 12 days of SoTL just for this topic. There is a variety to data analysis processes or frameworks you can refer to. Some of which @UofGLEADS offers CPD for. Today we have a brief look at the easiest one for qualitative data.

Thematic Analysis:

is probably the easiest approach if you are undertaking qualitative research for the first time. And I highly recommend the following article which does not only provide a clear explanation but also offers a bit of background to the approach. A brief summary is that you read and reread your data and begin to code it according the topics that are emerging, you can later cluster these topics into a theme. (However, you will see there is more to it, and different ways of going about it.) Coding tends to be an iterative process and we often ask a colleague to code a section of our data to see if they concur with our codes, and explore where similarities and difference might come from.

Braun, V., Clarke, V., Hayfield, N., & Terry, G. (n.d.). Thematic Analysis 48. https://doi.org/10.1007/978-981-10-5251-4_103

Statistical analysis:

again have a look at the resource our colleagues have created: http://www.stats.gla.ac.uk/steps/glossary/paired_data.html I would suggest if you are not used to using statistics be careful what conclusion you can draw from your analysis. Also one aspect I have seen too many times, if you have small numbers of questionnaires avoid using percentages to talk about your data. In general be cautious with your language!

Reflexive Practice

I briefly mentioned reflexive practice before, which basically means you explore your self within the context of your practice, e.i. teaching. The blog I am writing, could be considered part of my reflexive practice. However, to make this into SoTL, as a next step I would have to either use Thematic Analysis, or code the way Nicole explains, and analyse my own perspective, influencing factors, motivators, values, norms, and link all of this solidly with literature.

Questions of reliability and validity

also need to be answered. Although these are approached different between qualitative and quantitative research approaches. Here the debates vary widely, and you have to form your own opinion and decide what stance you take. In principle, generalisation from smaller projects, is not advisable. However, you might find a strong body of research or other educational inquiries that have all come to the same conclusion. At which point, we can see a pattern. Is your research repeatable and result in the same outcome? The answer to that will be: this depends. And you will have to think what arguments in your case would speak before and against this.

When speaking to colleagues who begin to dip their toes into SoTL projects, the most challenging aspect seems to be: to sit with insecurities. With the fact that things are open to debate and interpretation, that there is no clearly defined right way to do something.

Over time things will make more sense!

• https://www.statisticshowto.datasciencecentral.com/reliability-validity-definitions-examples/
• Emden, C., Sandelowski, M. (1998) The good, the bad and the relative, part one: Conceptions of goodness in qualitative research. International Journal of Nursing Practice  vol: 4  issue: 4  
  doi: 10.1046/j.1440-172X.1998.00105.x
• Brink, H. (1993). Validity and reliability in qualitative research. Curationis, 16(2), 35-38. doi:https://doi.org/10.4102/curationis.v16i2.1396

One of the most problematic things seems to be to get your head around analysing qualitative data. Have a look at Nicole’s fab explanation of how to go about this.