Mixed Methods: A short guide to applied mixed methods research

by Sam Ladner

Scale and causation are appropriately quantitative in nature. Yet —as I learned listening to that presentation on Chinese innovation—it takes an extraordinary amount of work for a human to make sense of quant data.

As philosopher Martha Nussbaum tells us, understanding is not a luxury but a very practical concern: “Understanding is always practical, since without it action is bound to be unfocused and ad hoc”

Maybe they will fight you when you ask them to think. But do not give up. Your courage and confidence as a research leader will inspire them to slow down and spend just a few precious moments thinking about the implications of their actions.

This moment of challenge inspired my talk on Being Cassandra at the Radical Research Summit in Vancouver.http://2018.radicalresearchsummit.com/sam-ladner/

Yet, if you consider every successive research project you complete as just one phase in a long-term, sequential research design, it begins to seem more possible.

Induction goes beyond “this is what we empirically observed” and uses symbolic tools like metaphor to describe the deeper meaning of a given set of behaviors.

what Davis and Davidson (1991) dubbed “data exhaust,” or the data that are automatically generated through digital interactions.

But I often tell people to tamp down their excitement about data exhaust because none of these data are actually designed for falsifiability in mind—it’s simply the detritus of our digital lives.

Just because we have more data doesn’t mean we are doing better research.

It’s becoming rapidly apparent to analysts that “data science” is more about formatting troublesome data than it is about answering meaningful questions.

Imagine my shock to discover that some data scientists do not know basic statistics. What a lost opportunity!

It never ceases to amaze me how bottomless the practice of social research can be. I’ve practiced research for almost 20 years and I still find myself noticing tiny nuances in technique that I had never noticed before. What happens if you try asking a survey question with a yes/no binary choice online instead of in-person? What happens if you wear different clothes to the usability lab? How do participants react when you sit beside them instead of in front of them? What if you force participants to use physical objects to numerically rate their experience? What if you don’t write a single interview question at all, and just sit and listen? There are endless ways to push your craft, and endless ways to become a better researcher.

As F. Scott Fitzgerald once wrote, “the test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function. One should, for example, be able to see that things are hopeless and yet be determined to make them otherwise.”