Qual vs. Quant

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// Two different research approaches and world views -> what you value you different, what you're looking for is different, how to look for it is different, what you believe to be rigorous is different //

Qualitative

Underpinning Assumptions

  • “there are many right answers or truths”  
  • “knowledge and truth are created not discovered; what we take to be objective reality is actually the result of our perspective”
  • “understanding people’s perceptions requires getting close to them, spending time with them, being able to empathize with their concerns, perhaps even be one of them, if you hope to truly understand”
  • “better to ask people what they think is important, and incorporate their answers in our efforts to make sense of their behaviour”
  • “humans are thinking, motivated beings; we extract meaning from our experiences and are affected by our perception of these meanings”
  • “our perception of the world is open to change; hence, the world is open to change”

Small sample size (*extreme users)

Interested in the extremes on the bell curve because these people have amplified needs and thus more noteable workarounds (necessity is the mother of invention). If we design supports that work for the extremes, they usually work for the middle. However, if we design for the middle, these supports almost never work for the extremes.

Interpretive / cognitive (psychology)

Describes the thoughts, feelings, larger context that drove the action. Emphasis on the why more than the what.

Sequence / starting point

open question -> in context research -> theory emerges from stories and observational research -> test data against hypothesis -> more research…. etc.  I.e. Theory emerges from the research process.

Other Related Concepts

Grounded Theory. Inductive thinking. Bottom up strategy. Constructivism. Phenomenology. 

Quantitative

Underpinning Assumptions

  • “there is a right and wrong answer, there is one truth”  
  • “there is a reality out there that is waiting to be discovered (one truth)”
  • “good data is dispassionate data, far removed from it’s source. This avoids bias and maintains neutrality and objectivity. Overidentification is a problem.”
  • “we may talk to research subjects but we can’t trust what they say; their job is to provide the raw data for analysis, and ours is to figure out what their words and actions means”
  • “humans are but just another organism; it is egotistical to think of ourselves as special”
  • “prediction demonstrates understanding”

Large sample size (aggregated averages)

Interested in the middle of the bell curve, rather than outliers. By using aggregate data, the extremes -- both positive and negative -- will cancel each other out; the “average” becomes the most typical or normal behaviour.

Behavioural (action) describes what people generally do, their actions. Emphasis on the what and how more than the why.

Sequence / Starting point

theory -> hypothesis -> set up an experiment to obtain the data that proves either [yes or no] -> adjust the theory accordingly. I.e. Theory guides the research process.

Other Related Concepts

Big data. Systems & Complexity Theory. Top down strategy. Positivism. Realism. 

Useful comparison of the two approaches from an excellent textbook I found in Value Village called "Research Decisions: Quantitative and Qualitative Perspectives" .

The below Wheel is also from that book, and aims to show that, while the starting point of Qual & Quant are very differnt, they follow the say steps on the wheel. They just have a different starting point.