Research Project
Assignment #1
Goal: Review the available data and identify an interesting question that could be answered
using these data. Ultimately, you will need to propose an answer to your question in the form of
a hypothesis and identify and explain the causal mechanism at play. You will need to propose a
couple of alternative explanations and identify relevant variables in the data to test your
hypothesis against the alternatives.
Note that nothing you turn in today is set in stone. If what you propose today isn’t workable, you
will not be locked into the project.
Research Question. A good research question is narrow, empirical, and answerable. This means
the data need to exist to answer the question, it should be clear how the question would be
answered, and the question needs to be about things we can observe and measure. It should not
be about things that are subjective (a matter of opinion). It should be clear what the dependent
variable (the thing that is effected) and independent variables (the thing that effects something
else) are.
A basic format for a research question is: How does [independent variable] affect [dependent
You can refine this by suggesting the direction of the effect (e.g. Does an increase/decrease in
[IV] lead to an increase/decrease in [DV]?), adding scope conditions that specify the time period
or countries/regions of interest or by indicating a variable you will control for (this isn’t really
necessary in the research question, but it doesn’t hurt).
NOTE: your question also needs to be relevant to course concepts. In the following examples, I
may use questions that are not relevant to ensure that I leave the field open for you.
1. Inappropriate: Are tariffs good or bad for women.
What’s wrong with it:
It’s subjective: what one person considers a “good” effect, another might consider a bad
It’s vague: good/bad in what way? What variables could be used to test this? Is it about
the effect of tariffs imposed by the woman’s country or against the woman’s country?
Appropriate: Does an increase in the tariffs imposed against a country decrease the
share of girls enrolled in primary school in that country.
Why it works:
It’s specific: I know what the dependent (share of girls in primary school) and
independent (tariffs levied against a country) variables are.
It’s objective: regardless of whether I think tariffs or school enrollment are good or bad
themselves, these are measurable things that can be observed
Note that this fits the format “Does an increase in [IV] lead to a decrease in [DV]?”
2. Inappropriate: Should women vote for female veterans?
What’s wrong with it:
It’s vague: vote for them for what?
It’s subjective: this isn’t an empirical question and there are no data that could adequately
answer this. It is ultimately a matter of opinion. We could look at how effective female
veteran legislators are in terms of bills introduced or some other measure and compare
this to other types of legislators. We could look at the types of bills that veteran women
introduce. We could look at a whole host of different variables, but ultimately, it’s not
clear what we would measure here or on what grounds we could conclude that women
should behave in one way or the other.
Appropriate: Do women voters prefer veteran women candidates for the U.S. House
over other types of candidates?
Why it works:
It’s objective: we can get public opinion polling data on this or run survey experiments to
answer this question. Even though voter preferences are themselves subjective, the data
on this are objective. In other words, your decision about whether you prefer a veteran
woman or a non-veteran man is subjective. But if I have polling data showing that 56%
of women voters prefer the veteran woman, that is objective – it’s not about my opinion
of whether women voters should prefer veteran women, it’s about whether they do prefer
It’s specific: It’s easy to tell what the IV (whether candidate is veteran woman, nonveteran woman, veteran man, or non-veteran man) and DV (whether women voters prefer
the candidate) are. I’ve even specified scope conditions (candidates for U.S. House and,
by implication, U.S. voters)
Note that this fits the format “Does [IV =veteran woman candidate] lead to an increase
in [DV = support from women voters]?”
Appropriate: Do veterans serving in state legislatures sponsor and co-sponsor more
legislation dealing with healthcare policy as compared to non-veterans, controlling for
Why it works:
It’s specific: IV is veteran status and gender of legislator; DV is number of bills dealing
with healthcare policy sponsored or co-sponsored
It’s objective: Doesn’t make a value judgement about whether the policy is morally god
or bad, it just asks about something measurable – number of bills.
Note that this fits the format “Does [IV=veteran status of state legislator] lead to an
increase in [DV = more healthcare bills sponsored/co-sponsored]?”
Anchor 209
Research Project
Data Sources
The following is a list of publicly accessible data on topics relevant to the course. I have linked
to the codebook for each dataset. Start there are the codebook explains what the variables are,
how they are coded, measured, etc.
If you can’t find the data you want, let me know.
1. Women in Armed Rebellion Dataset (WARD). Reed Wood and Jakana Thomas. 2017.
Available at: (click on “codebook” and open the file).
2. WomanStats. 350 variables from 176 countries. Available: (click on “codebook”)
3. United Nations Data: . Try clicking on “Datamarts” and then browse
the available data.
4. UCDP/PRIO Armed Conflict Data. Available: (download the codebook).
There are no gender related variables, so you will need to pair with another source.
5. Pew Research Center. If you are looking for public opinion data, this is a good place to
check. You will need to create a free account to get much use out of it. There is a lot here,
so it’s best to have an idea of what you want before you start looking so you don’t get
6. Correlates of War Project. There are no gender
related variables, so you will need to pair with another source.

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