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Pidgin Psychobabble Chronicles #3: Qualitative vs. Quantitative

Pidgin Psychobabble Chronicles #3: Qualitative vs. Quantitative

So a major question I’m encountering trying to prepare for my ever more imminent entry into grad school is what form my research should take – laboratory experimental, extra-laboratory experimental, observation, survey, interview, etc.

Laboratory experiments are the toughest to design properly – and often difficult to derive based on the research question, requiring some creativity on how to “get to” the core premise being tested while reducing confounding variables. However, if properly designed, they are quite objective and the data is easily converted into numerical form (i.e. quantitative data) and analyzed.

Qualitative data, conversely, is easily gathered from the other experiment types (especially observation and interview) but requires inductive reasoning to distill down to numerical form and are accordingly subjective and biased by my own analysis, but the designs – or the way of studying the questions I have, the things I want to test – are much easier to produce.

This is just another in a long line of choices that I’m currently being driven mad by.

For the time being, I’m preparing myself to perform both (as I will probably need both skill sets over the course of my career) and trying not to worry too much.

Still, I can’t help but feel that these days I’m learning everything all the time on the off chance that I will need it. I’m sure it’s good for me, but it’s difficult and exhausting nonetheless.


For shits and giggles, here is the way I wrote to myself earlier about what I just wrote for the blog:

Recruit my innate heuristics to extrapolate case studies in the service of hermeneutics.

Making the profound individual meaning into more than a single point.

Heidigger’s phenomenology?

Interpretation – there’s an idea… do I set up designs that are free of confounding variables and easily distill to quantitative data – or do I embrace the challenge of sorting and applying qualitative data?

Yet another major decision. Case by case? Depending on what the design/question require?

Do I want to specialize in correlation rather than causation? Forming proof of causation is a formidable order.

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