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Guide to Applying to PhD Programs in Political Science


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Dear new PhD applicants in Political Science,

 

I am writing this post to provide you with a centralized source of information to help you make decisions about where to apply. I decided to provide you with this source because this information was not available to me in any sort of organized fashion, meaning that I had to find and organize it myself. I wish a resource such as this had been available to me when I began applying.  This does not mean that you will not need to do research on the programs to which you consider applying. There is some information that I simply cannot provide you with, such as up to date data on placement rates or how well your research interests match with the departments you are considering. These are among the most important factors you will consider. While I will walk you through how one can go about making these calculations, the main point of this post is to provide you with a starting point- useful data to help you begin to make decisions about where you will apply.

 

Useful Links

 

Rankings

 

The first thing I should say about rankings are that they are only a short cut. There is a lot more noise than one would like. I encourage everyone to ensure that the department in question is placing people rather than assume it blindly because of the rankings (more on that later). There are three main rankings political scientists look at:

 

  1. The NRC

https://www.chronicle.com/article/NRC-Rankings-Overview-/124714

 

Methodology: the NRC rankings use several different methodologies based on multiple objective criteria to determine their five different sets of rankings. The S-Rankings use some 20 different factors that scholars say are important such as faculty research productivity, student completion rates and funding. The Research Rankings are based on measures of the departments research productivity. The Student Rankings are based on measures of student outcomes and quality of life while in the department. The Diversity Rankings are based on measures of diversity. The R-Rankings are a regression model trying to determine the departments that look most like the departments the Scholars Model likes.

 

Pros:

  • A lot of objective data went into these rankings.

  • The multi-dimensionality of the rankings allow you to weigh the different dimensions as you see fit. EG if you care more about research productivity than student outcomes, you can look at the Research Rankings and weigh them in your decision of where to apply to.

  • The S-Rankings most closely resemble the 5-year placement rates I saw when deciding where to apply of any ranking is (including US News and Oprisko)

Cons

  • Equivocal: there is a lot of noise, and they show it to you. Programs don't have ranks, but rather rank ranges and there are five different sets of rankings.

  • Infrequent: this set of rankings came out in 2010, the last NRC rankings before that came out in 1998. While I do not think that these rankings are so excited old that they are not useful, a lot can change in eight years.

 

2) US News Rankings

https://www.usnews.com/best-graduate-schools/top-humanities-schools/political-science-rankings

 

Methodology: US News simply surveys scholars on the department reputation, asking them to rank them on a 1 to 5 scale, and ranks departments based on the results

 

Pros:

  • The most widely used rankings

  • The only rankings that take reputation into account

Cons:

  • Reputation is the only factor taken into account, so it could be said that the rankings are completely subjective

 

3) Oprisko

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2303567

 

Methodology: Oprisko looks at outcomes- R1 placements. He counts the number of PhDs from a given institution currently working at a PhD granting institution in Political Science. He comes up with two rankings from that- the raw total number of placements and the number of placement divided by faculty members (placement efficiency)

 

Pros

  • Makes student outcomes front and center

 

Cons:

  • Only uses R1 placements

  • The main rankings does not control for program size. The placement efficiency rankings, however, do.

 

Stipend Information

 

http://www.phdstipends.com

 

This website provides a searchable database of funding offers from various departments. Just search the name of a University and “Political Science” and something should come up.

 

Some of this data may be outdated, so pay attention to the year it was posted. Some schools may not have any data posted on this site. Funding offers may vary.  Better information may be available on the departments webpage. Nonetheless this is a very useful resource for information about funding.

 

When will I hear back?

 

https://forum.thegradcafe.com/topic/100449-decision-timelines-for-particular-universities-and-programs-derived-from-the-gradcafe-data-gregpa-distributions/?tab=comments#comment-1058542321

 

This link shows the timeline in which decisions have been made in the past. You could also search the gradcafe results forum to get a sense for when results will come through.

 

What should my Statement of Purpose look like?

 

http://grad.berkeley.edu/admissions/apply/statement-purpose/

 

Here is some useful advice for drafting a statement of purpose. Tailor it to your specific program. Mention Professors you'd like to work with and programs/institutes that might interest you. Also edit it as much as you possibly can. I made about ten drafts before finally sending it off.


 

GRE/GPA Info

 

Here is the GRE and GPA information on every school I could find. This data either is posted on the departments website or was about six months ago when I searched for it. Use this data to strategize where to apply. If you have a 155/155, it may not be wise to only apply to Stanford, Duke, Cal, UCSD and WashU, as a safety. However, do not let minor discrepancies discourage you from applying from your dream program. These stats are a small part of a number of factors that will determine your success in the application process. Use them to give you a rough idea of how you may fare, not as an absolute predictor of your success.

 

Stanford 163 q/166 v/3.8 GPA Recommended (Rec)

Duke 163 q/163 v/3.8 GPA Average (AVG)

UC Berkeley 161 q/158 v Rec

Northwestern 148 q/160 v Rec

Kansas 148 v/156 q/ 3.5 GPA Avg

UCSD 163 q/166 v/3.9 GPA Avg

Chicago 162 q/166 v/3.8 GPA Avg

Columbia 158 q/161 v/ 3.8 GPA Avg

Penn 161 q/165 v Avg

WashU-STL 161 q/159 v/3.9 GPA Avg

Colorado State 154 q/154 v/ 3.5 GPA Rec

UNLV 148 q/160 v/3.5 GPA Rec

Emory 160 q/160 v/ B+ (or Better) GPA Avg

Princeton 160 q/160 v/3.8 GPA Rec

Notre Dame 158 q/ 165 v Avg

Colorado 154 q/160 v Avg

Oregon 300 total GRE/3.0 GPA Rec

UC Riverside 307 total GRE/3.0 GPA Minimum

Washington 314 total GRE/3.4 GPA Rec

Oklahoma 154 q/153 v Avg.

Iowa 158 q/156 v/3.3 GPA Rec

Hawaii- No GRE required

Baylor 163 q/163 v Avg

Virginia 155 q/153 v Rec

USC 158 q/ 162 v Avg

NYU 165 q/162v/ 3.5 GPA Rec

Stony Brook 163 q/157 v Rec

 

Maximum Master's/Transfer Credit Accepted (in classes)

 

This is the maximum amount of Master's/Transfer credit programs will award. Please note that it is often up to the department's discretion to award or not award people credit for some or all of these courses. These decisions are also often not made until well after you have entered the program.

 

Princeton- Dept Discretion

Columbia- Dept Discretion

UCLA- 6 Classes

Cornell- 3 Classes

Northwestern- 6 Classes

Texas- None

Emory- Start at Advanced Standing

Penn- 4 Classes

Virginia- Advanced Standing

Vanderbilt- Advanced Standing

Washington- 2 Classes

Ohio State-10 Classes

UNC- 6 Classes

Wisconsin- None

Duke- Dept. Discretion

Pitt- 8 Classes

Missouri- 8 Classes

Notre Dame- 8 Classes

UChicago- Dept Discretion

NYU- 8 Classes

UC Irvine- 6 Classes

USC- 8 Classes

Colorado- 3 Classes

 

How to Figure Out Fit

 

This is where things get somewhat subjective. Professors often move around, retire, ect, so it is not wise to attend a university where you believe that you could only work with one professor. Whether you want to apply to a program with one person who really fits your interest and one other who is less of a good fit, but not as well, is a decision you have to make. Most suggest that there should be at least two who you can work with, I applied only to programs where there was no less than three who shared my interests.

 

Go to department websites. Look at the faculty in your subfield. Look at their CVs, search them on Google Scholar. I'd suggest keeping track of them in a notebook and giving points based on how you feel about their work in relation to your own. By the end of this process, you will have a sense of departments that are good for your interests and those that are not.

 

Placement

This is a tricky thing to measure, but you should absolutely take placement into account before you apply. Some departments have very good data on placements (Michigan, WashU, Notre Dame, UNC to name a few), but you have to dig for it. What will shock some is how little the percentage of graduates placed varies from school to school based on it's rank, particularly if you take attrition into account. Based on their own data, at Michigan (USN #4), a starting PhD student has about a 40 percent chance of finishing the program and finding a Tenure Track job within five years of degree completion. At WashU (#19), a starting PhD student has about a 40 percent chance of finishing their degree and finding a Tenure Track job within five years thereafter. What about Notre Dame (#37)? A starting PhD student has about a 40 percent chance of finishing their degree and finding a Tenure Track job within five years of completion. This is not to say that placement does not vary, just that rank is not as big of a factor in whether or not you will get a job as some say.

 

APSA’s studies of placement backs me up on this one:

http://www.apsanet.org/RESOURCES/Data-on-the-Profession

 

Some years the schools in the NRC’s 20-40 and 40-60 range actually have better initial placement rates than those in the 1-20 range.

 

Where rank makes a difference, this study as well as the Oprisko data shows, is the types of institutions one gets placed at. If you absolutely need to get a job at an R1 PhD granting institution or this whole endeavor is not worth it for you, you might be best sticking to top 20 programs (but still do your homework on their placement). Otherwise, if you are fine ending up at an R3, non selective liberal arts college or a directional school, you have a lot more options.

 

So how do you determine a schools placement if this data is not readily available to you? Look on the department’s placement page. You can divide the number of total placements (TT, TT+nonTT, R1 jobs, jobs you would want to take, however you want to break it down) over a set period of time (5-7 years is advisable) and either divide it by the total number of grad students currently in the program (data which you can also usually find on the departments website) or by the planned incoming cohort multiplied by the number of years you are counting placements for (again, 5-7 is advisable). Just make sure you keep your process consistent. There will be some inevitable noise, but this should do enough to let you know what programs look good and which you should stay away from.  You may find that some 'top’ programs do a bad job of placing people, whereas some 'midteirs’ do an excellent job. If you focus on R1 placements, you will likely find that the rankings are excellent predictors.

 

Conclusion

So that just about wraps it up. I hope this advice has been useful. Best of luck to all of you.


 

Edited by buckinghamubadger
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