Book summary quantitative research methods
Chapter 2:
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:
- finding patterns and making predictions under stability.
The kinds of things that data mining is less good at :
- improving our understanding, or in other words helping
improve theory.
- It also has a tendency to find false positives if you aren’t careful.
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.
False positive: is when a scientist determines something is true when
it is actually false
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, like “people who wash their hands will get sick less often.”
That is, a research question should be something that, if you answer it,
helps improve your why explanation.
Great research questions often come from the theory themselves - the
line of thinking being “if this is my explanation of how the world
works, then what should I observe in the world? Do I observe it?”
,How Do You Know if You’ve Got a Good research question?
Consider Potential Results: imagine what kind of sense you’d make
of that result, or what conclusion you would draw. If you can’t say
something interesting about your potential results, that probably
means your research question and your theory aren’t as closely linked
as you think!
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!
, Chapter 3:
Types of variables:
Continuous Variables. Continuous variables are variables that could
take any value (perhaps within some range).
- The monthly income of a South African would be a continuous
variable.
Count Variables. Count variables are those that, well, count
something. Perhaps how many times something happened or how
many of something there are.
- The number of business mergers in France in a given year is an
example of a count variable.
Ordinal Variables. Ordinal variables are variables where some
values are “more” and others are “less,” but there’s not necessarily a
rule as to how much more “more” is.
- A “neuroticism” score with the options “low levels of
neuroticism,” “medium levels of neuroticism,” and “high levels
of neuroticism” would be an example of an ordinal variable.
Categorical Variables. Categorical variables are variables recording
which category an observation is in - simple enough!
- The color of a flower is an example of a categorical variable. Is
the flower white, orange, or red? None of those options is
“more” than the others; they’re just different.
- A special version of categorical variables are binary variables,
which are categorical variables that only take two values. Often,
these values are “yes” and “no.”
Qualitative Variables Qualitative variables are a sort of catch-all
category for everything else. They aren’t numeric in nature, but also
they’re not categorical.
Chapter 2:
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:
- finding patterns and making predictions under stability.
The kinds of things that data mining is less good at :
- improving our understanding, or in other words helping
improve theory.
- It also has a tendency to find false positives if you aren’t careful.
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.
False positive: is when a scientist determines something is true when
it is actually false
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, like “people who wash their hands will get sick less often.”
That is, a research question should be something that, if you answer it,
helps improve your why explanation.
Great research questions often come from the theory themselves - the
line of thinking being “if this is my explanation of how the world
works, then what should I observe in the world? Do I observe it?”
,How Do You Know if You’ve Got a Good research question?
Consider Potential Results: imagine what kind of sense you’d make
of that result, or what conclusion you would draw. If you can’t say
something interesting about your potential results, that probably
means your research question and your theory aren’t as closely linked
as you think!
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!
, Chapter 3:
Types of variables:
Continuous Variables. Continuous variables are variables that could
take any value (perhaps within some range).
- The monthly income of a South African would be a continuous
variable.
Count Variables. Count variables are those that, well, count
something. Perhaps how many times something happened or how
many of something there are.
- The number of business mergers in France in a given year is an
example of a count variable.
Ordinal Variables. Ordinal variables are variables where some
values are “more” and others are “less,” but there’s not necessarily a
rule as to how much more “more” is.
- A “neuroticism” score with the options “low levels of
neuroticism,” “medium levels of neuroticism,” and “high levels
of neuroticism” would be an example of an ordinal variable.
Categorical Variables. Categorical variables are variables recording
which category an observation is in - simple enough!
- The color of a flower is an example of a categorical variable. Is
the flower white, orange, or red? None of those options is
“more” than the others; they’re just different.
- A special version of categorical variables are binary variables,
which are categorical variables that only take two values. Often,
these values are “yes” and “no.”
Qualitative Variables Qualitative variables are a sort of catch-all
category for everything else. They aren’t numeric in nature, but also
they’re not categorical.
