On Friday, November 12 your five-page (maximum) outline of your research proposal is due.
In brief: This is designed to be a shorter version of your final assignment research proposal, providing a more summary version of the project will propose in fuller form at the end of the term. It will include a research question, an indication of the key causal explanation, a discussion of the key observable implications of that causal explanation, and a justification for the project. To make things even more clear, we have suggested explicit guidelines below.
- YOUR NAME needs to be on all of your written work please!
- All short proposals must be submitted through turnitin.com.
- This outline will be graded and counts for 20% of the final course grade.
Suggested contents for your outline:
- What is the main problem you wish to address? Summarize the project and its motivation in no more than a page. Focus on what general theme the project addresses (e.g. “do campaigns make a difference”) and describe the specific and concrete way that your research will investigate this theme (e.g. to study the association between spending recorded in Irish election campaigns and relative vote share received in the election, at the constituency level by candidates).
- What contribution will your study make, even if very small, to our understanding of the problem? this section can be short.
- Theory and/or Hypotheses. Every project will be different with regard to this section, with some being more aimed at testing “causal relationships” than others. But in this part, probably between 0.5-1.5 pages depending on what your paper is about, you will describe what you hope to test or establish or discover. For instance, you might be interested in examining how the choice of executive institutions influences post-conflict political systems (a version of the presidential versus parliamentary system debate). Or you might be interested in explaining why in a specific case it appeared that two democracies went to war with another, despite the widespread belief that this never happens.
- Data. This section needs to clearly identify what the main unit of analysis is and how you will observe it. This section needs therefore to be both conceptual and practical. Sometimes, these two levels point to different units, for instance: say you are interested in why voters in two-ballot (or “mixed member”) electoral systems split their ticket by voting for different parties on different ballots. Your conceptual unit of analysis, following from your theory about why and how voters split tickets, is at the individual level. But your practical unit of observation may be at the level of aggregated election returns, say party totals for each ballot, in an electoral district. This is because no record is published by the election authorities linking individual votes on the two ballots. Alternatively, if you had access to an election study (a detailed survey of individual respondents), then your unit of observation would be an individual survey respondent.
- Methods. Here you need to be very clear as to what methodology you plan to use to analyze your data, linking it to theory. Don’t be overly vague, for instance by stating “I will use qualitative and quantitative methods to analyze my dataset. Instead, state: “I will use macro-statistical methods to determine the association between the level of democracy and the rate of foreign war involvement, at the country-year level. I will also analyze the three cases of the X-Y 1970 conflict, the Y-Z 1973 conflict, and the ABCD 1980 conflict in detail using historical accounts.”
- Expected and/or potential findings. Here you should also point out what sort of results from your data analysis would lead you to specific conclusions. Conversely you should also point out what sort of evidence would lead to to reject those conclusions. This acts as a good test that your theory and/or hypotheses are potentially falsifiable. Note that if your paper is more of an investigative report on something that has received little systematic study, then your findings will be more of a descriptive nature and hence the falsifiability criterion will apply less to your project than to others. But it should still be easily possible to describe what you might or might not find following a full investigation of your data.