Research Synthesis Example
Here is an example to sum it all up in a case below. Inspired by true stories.
You are a social scientist tasked to observe Danny’s morning routine and design solutions around a problem he faces consistently.
Every morning Danny wakes up and goes to work and observes the following: the dishes are left in a mess in the kitchen sink, there are bread crumbs all over the dining table and fruits are missing from the fruit bowl.
Given the problem statement to solve: How might we help Danny wake up to a clean kitchen/dining area and a fully stocked fruit bowl without daily external intervention?
Do you have enough data to brainstorm a few solutions?
- Yes, if you are comfortable with making one key assumption.
- No, if you would like to be precise.
Most of you will take the data and move forward with designing solutions assuming that Danny lives with a partner or flatmate. A crucial missing piece of data that you could have collected by asking Danny (the user).
Now if Danny tells you he lives alone and suffers from a condition known as sleep walking. Wouldn’t that change your design solutions quite drastically?
At this point, do you have sufficient data to design solutions? No, at least I wouldn’t be comfortable to do so if I were you. What else might be missing?
The answer: The root cause of why the state of the kitchen and dining are is what it is. What I might have done in your position is to ask if Danny for permission to place a night vision camera in the bedroom, kitchen and dining area. Given the recordings from the night and an understanding of the root cause of why the kitchen/dining are is in the state of what it is every morning, we can then move forward to design the appropriate solutions.
As you may have noticed, the design process is filled with moments where inductive and/or deductive reasoning are used to help you design the appropriate solutions. Given certain constraints (e.g. Danny might not be comfortable with you recording his apartment in the evening) you may have to make do with the data you have or you may choose to interview and record similar patients to develop a better understanding of sleepwalking; where you can then draw inferences to the root cause.
I hope this helps you with more successful research outcomes that will drive better design solutions in this world. Leave a comment below if this article has been helpful for you.