Yesterday I wrote some conclusion and something of a summary on my thoughts. Today it was time to "kavla upp ärmarna" (roll up the sleeves) and get going - doing - *something*
Well lets call it research, as research in its truest sense, I suppose - literally searching and reading about essentially *everything* yet *nothing* and a sort of "throw something at the wall and see what sticks" type of method (not to be confused with throwing a strand of pasta at the wall and if it sticks it's ready to eat - not the single pasta strand you threw but the whole batch of pasta you presumably made (now that would be funny if you only cooked one strand of pasta) - you can view it as a SAMPLE representing the WHOLE (population)).
See, research is about statistics, that is my ultimate conclusion perhaps.
Okay, enough with the shenanigans. It was time to work, work on that search query I suppose - so I included all sorts of Booleans and grouping and whatnot; but ultimately needed to go back and just look at individual words and terms. Any long search query is bound to be somewhat cumbersome, if you want to really understand what is going on, I think. But for demarcation|delimitation, it really is what you need.
At one point - I realized that the only 20 hits was actually all there was about this particular topic.
However, lets get back to the topic; I suppose, if I have anything cogent to say about this topic, which has not been said before (of course not): now: qualitative methods are old school - analog and manual. But, there is a way out of the misery, and that is by involving computers, naturally!
However, you still need to know what to do with this godly power of computation: first you can go easy with word frequencies and such... then it's time for: latent semantic analysis and latent dirichlet allocation - you can also stumble into stuff like probabilistic latent semantic analysis and latent semantic indexing.
Then it's time for machine learning, or if it was perhaps already included in the previous (may be the case): Supervised Machine Learning (SML) and get into that sweet Bayesian statistics.
Let the computer do the job, and sit back and enjoy. I guess. Well, you need to prepare the datasets and do the training and install a bunch of software and well, learn some new math and statistics including but not limited to linear algebra *gulp*. But other than that, just sit back and relax; the transistors will do the work from now on.
Well that would have been the case unless I had a manual method lined up for a literary review; which will likely need to obey certain rules. However; this meta study now has its subject or topic, which is all of above. I think it's a massive study but lets hone in on the particulars; which I believe will be related to the method (quantitative) to some extent and the analysis (content|thematic analysis).
Well it's a mixed methodology I suppose; but treating the data, which is qualitative? With a qualitative method (thematic analysis, using content analysis methods?) I guess you can go wrong here. There are divides which needs to be clarified I can *clearly* see. But I think that is what is interesting. Perhaps.
We will see, it looks more philosophical than anything else, but might do for a meta analysis I guess.
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