,Chapter 1
A research question is a question that you plan to answer, or at least try to answer, by researching. A
good research question is well-defined, answerable, and understandable - those can be hard to
figure out!
Empirical research
Empirical research uses structured observations from the real world to attempt to answer questions
(instead of reasoning the answers).
Quantitative empirical research is just empirical research that uses quantitative measurements
(usually numbers). There are more data sets and fewer interviews. Measurements are hard to take
precisely or interpret accurately. Statistics is a difficult field.
One particularly sticky problem with quantitative empirical research is that the numbers we observe
often don’t tell us exactly what we want to know.
If our numbers don’t actually answer our research question, what can we do?
If you do it right, you often can figure out how to collect the right numbers or do the right things
with those numbers to get an actual answer to our question. We have to carefully design the right
kind of analysis that will answer our question.
Why research needs a design
Proper research design is essential to uncover the true relationship and avoid superficial
interpretations.
Research design is challenging, acknowledging these difficulties is better than producing unreliable
results. High-quality research design is critical to genuinely understanding the questions at hand.
Chapter 2
2.1 What is a research question?
A quantitative research question is a question that can be answered, and for which having that
answer will improve your understanding of how the world works. It should inform theory in some
way.
A good research question takes us from theory to hypothesis, where a hypothesis is a specific
statement about what we will observe in the world.
A good test for whether a research question informs theory is to imagine that you find an
unexpected result, and then wonder whether it would make you change your understanding of the
world.
2.2 Why start with a question?
Data mining: Skip the hard part of deriving a research question from a theory and instead just see
what sorts of patterns are in the data?
The kinds of things that data mining is good at are in finding patterns and in making predictions
under stability (data staying the same). The kinds of things that data mining is less good at are
in improving our understanding, or in other words helping improve theory. It also tends to find false
positives if you aren’t careful.
, Why does data mining have difficulty helping theory?
• Data mining focuses on what’s in the data, not why it’s in the data. In other words, it’s
fantastic at revealing correlations - patterns in the data of how variables we’ve observed
have varied together in the past - but the correlations it uncovers may have little to do with
causality, or an understanding of why those variables move together.
• Data mining is so focused on the data, data mining doesn’t really deal in abstraction, it
misses the applications and relation to real life.
• False positives are another reason why data mining can be dangerous, it finds relationships
that might not apply to real life, but surely there’s something there. If you check, say, a
hundred variables and see if they’re related to aggression, something is going to pop up as
looking related, just by random chance. That random relationship is unlikely to pop up again
if you tried another sample. It’s only in the sample you have by random chance, which is
what makes it a “false positive.”
Without a disciplined research question, there’s no reason not to just check everything!
2.3 Where do research questions come from?
Mostly, curiosity. We want to know how the world works, and that naturally leads to questions!
There are two steps in this process: thinking about theory and coming up with a research question.
Either one can come first.
1. Perhaps it begins with theory: “I think this is how the world works” or “I wonder if this is
how the world works” - that’s your theory.
2. With the theory in place, the process continues with our hypothesis: “if this is how the world
works, what would I expect to see in the world?”
2.4 How do you know if you have got a good one?
Just a few things to check before you get too far into the process:
• Consider Potential Results. A good way to double-check the relationship between your
research question and your theory is to consider the potential answers you might get. Then,
imagine what kind of sense you’d make of that result, or what conclusion you would draw.
• Consider Feasibility. A research question should be a question that can be answered using
the right data if the right data is available. But is the right data available?
• Consider Scale. What kind of resources and time can you dedicate to answering the research
question?
• Consider Design. A research question can be great on its own, but it can only be so
interesting without an answer. So, an important part of evaluating whether you have a
workable research question is figuring out if there’s a reasonable research design you can
use to answer it.
• Keep It Simple! Answering any research question can be difficult. Don’t make it even harder
on yourself by biting off more than you can chew! A common mistake is to bundle a bunch
of research questions into one.
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