RWBG

This has been an interesting opportunity for me to learn about the intricacies of the US tax system (I'm Canadian). I agree that one should consider both income and sales tax when making precise comparative calculations. I also agree that the minor differences should not be pivotal in terms of decision-making; approaching it this way just had the advantage of showing me that the differences are, in fact, minor. Does anyone actually finish in four years?

Something I've found to be a useful exercise recently is to consider the total amount of money offered you by a school over five years. Given that for many schools, stipends vary between years (especially when dependent on TAships), and given that summer funding is often offered separately and for a limited number of years, I've found this to be a useful (if perhaps obvious) exercise for comparing schools. One can also divide the total amount by five to get an effective annual rate for easier comparisons between schools. See below: Wisconsin: 10.5k/year (after fees) going up to 12.2k/year after comps. => $55938 total => $11187/year. Michigan: 17k fellowship first year, TA ship years 2-5 that goes up 3% each year. 3k summer funding first two summers. => $100976 => $20195/year UCLA: 24k fellowship first two years, TA ship starting at approx 17k going up to approx 20k after comps => $104761 => $20952/year Rochester: 22k/year (two years of teaching commitments) => $110000 total => 22000/year Also, I've been using this to compare living costs: http://www.bestplaces.net/col/

Definitely sorry to hear that, but I hope you'll take what many people here have said about being more successful on their second try to heart.

He's the man with the name you'd love to touch ...but you musn't touch! I suspect changing your name (presumably you have done so in real life as well as on gradcafe) will be the single greatest positive factor behind your future success in your Ph.D program.

A couple of added notes I forgot about: the article above also talks about using tools that are "easily understood." I don't really think that's a fair critique; if social scientists can explain their evidence and how the methods work, then I think you can use methods that are, themselves, not broadly understood. Economists have been doing it for a long time, and probably have more influence on policy than political scientists. Also, @McMuffin, stats work certainly does not assume rationality. There's been a fair work done that is actually meant to test hypotheses that contradict rational choice; see, for instance, work done to test theories of bounded rationality. Moreover, rationality as it is described by formal theorists is far less narrow than most people assume; it basically just means that people will try to do what they view as in their interest. It also is not a requirement that all formal work be based on rationality assumptions; see, for instance, some of the work being done by Arthur Lupia that engages with work in neuroscience, or a lot of work done in complex systems modelling. Finally, on normative work, I don't have that much of an opinion, except to say that I think you can divide positive work from its normative implications. Also, from what little I know of normative theory, sometimes concepts of welfare analysis derived from economics can be useful in structuring one's thought (e.g. I think Rawls has utility curves in one of his books?). I think John Roemer does some stuff on normative formal theory? In any event, I certainly don't think normative work should be dominated by statistics

Ugh, I just wrote something and then accidentally pressed a button that went back to the previous page and deleted everything I wrote. This version has been written more haphazardly. First, I think it's useful to divide formal theory and stats, because they have different advantages. Formal theory helps to ensure logical consistency (though is not necessary to do so), and allows you to play around with the model to find contingencies. It also allows one to have a rigorously defined justification for the specification of a statistical model, helping to avoid the "Garbage Can regressions" that were discussed in another thread of this forum, in which one includes a laundry-list of variables in the hope of increasing statistical significance. However, the advantages of formal theory are distinct from stats, and sometimes game theory work accompanies case studies. Stats are different, and are more about getting the most out of limited data (which we all face as political scientists), and ensuring a clear and well-defined framework for causal inference. Case studies can be useful, but it can be hard to pick cases that control for the right variables. People sometimes talk about "special cases", e.g. the case that falsifies a deterministic theory (which I don't really think exist in the social sciences, most are probabilistic), or cases that are particularly persuasive because they are the case which you wouldn't expect a hypothesis to hold up in. The second kind of case I think is fine, but we should be careful about making too many strong inferences from that, given that we're still dealing with only one instance of something occurring in a particular way when trying to uncover probabilistic hypotheses. There's also process-tracing, which I know much less about, but I imagine it would be very difficult to "observe" causal processes in a complex social system; difficulty in observing biological mechanisms is part of what led to a move towards evidence-based medicine from more mechanistic approaches, and I think social systems are just as complex and biological systems, if not more. So finally, the article posted above writes of a trade-off between empirical rigor and substantive importance, favouring the latter. I don't think that such a trade-off is particularly fair, and I think much of the formal theory and stats work has been very important substantively. However, if we do accept this trade-off, then I think there's a real question about the value of work that is based on broad, important questions, but produces very little in terms of empirically evaluating competing hypotheses. In these instances, I think there's a danger of work just becoming a way for people to justify previously held viewpoints. That being said, I tend to think attempts to answer big questions are valuable, and it's good for people to put forth the effort to answer those questions as rigorously as possible. Still, there would be no reason not to use statistical analysis as part of a toolkit to answering big questions; maybe you get a broad-based regression that's suggestive of a particular relationship, and support that work with case-studies and process tracing. Sure, quant work sometimes encourages work that focuses on smaller questions that can be proved more conclusively (e.g. look in economics at a lot of the natural experiment and instrumental variable stuff), but it's not a necessary distinction, and I think the article above is criticizing a "normal science" approach more than a "science" approach. Ultimately, the question is less about cosmetic similarity to "science," and more about making the most of the data we have to make the best inferences we can. That may involve quant work, may involve qual work, and may involve some combination of the two. One thing I definitely think is true though is that both quant and qual people should spend more time thinking about the epistemology behind their research designs, and possibly spend more time reading philosophy of science.

Thanks again for your advice. I totally understand that reputation matters, I just was under the impression that what matters most is reputation within political science, as opposed to externally. It sounds like you're saying that fellowships are a good signal of the reputation of that department to other political science departments, in which case I would completely understand the value.

How about schools for doing IPE using applied formal theory and statistical methods? I'll take a look at the Review of International Political Economy papers, sounds interesting. The choices I have are listed in my signature, so any advice you have on those schools would be especially appreciated. So what's the value of being at a department which has a strong reputation to outsiders? I assume you mean outside of political science? Thanks for helping out.

Just curious, what kind of signal does this send? Is it about placement? One of the departments I've been admitted to appears to do particularly well on this dimension, and I'm not sure exactly what signal this send. Also, I might as well ask self-serving advice about what programs seem strongest to you for IPE I feel like people get a better sense of things as a grad student than they get beforehand.

When you say theoretical stats, do you mean stats using a lot of calculus/preferably proofs? If so, it could be good, but only if you have a way to signal the content of the course (i.e. if the course title just says "Statistics I" it may not help, unless you have a letter writer talk about it). Linear algebra can be a bit more consistently theoretical (although yours is combined with multivariate calculus?) but the payoffs in terms of research applications are probably more long-term. I'd say linear algebra is likely to be best if you plan on doing quant once you start grad school, as most the stats stuff can be duplicated in your grad courses, while you may not get an easy opportunity to take a dedicated course in linear algebra once your start your Ph.D. Also, when you say methods, are you including formal theory or just stats?

Well, as I said, I don't think fit is unimportant, and I suspect in many instances it can play a deciding factor. That being said, you also see some people who were rejected by many top schools but accepted by one, which I think makes a case for soft factors not having clear and consistent evaluative standards.

I think a huge difference is the weighting of things like the SOP. In econ, a lot is dependent on math courses/grades; if you have a bunch of courses in real analysis with A+ grades, chances are you'll do well. Letters I think are weighted a bit less as well, especially given that a lot of econ people end up with letter writers who observed their performance in test-based coursework, so the letters may not be as informative. So basically, all the soft factors are weighted much higher in polisci (writing samples aren't required for most econ programs as well) so you end up with a huge amount of variance. Edit: So I think that jsclar's point about fit being weighted higher in polisci is true too, but my point is more than that. I think in polisci, adcoms tend to think that soft factors are more informative of potential performance than factors like GRE/coursework, in part because there's a great deal of heterogeneity in the courses people take, and because what courses tell you about potential performance is heavily dependent on the kind of work you're interested in doing. Contrastingly, in economics, I think one of the biggest issues they have is students coming in who can't hack micro theory, and facility with mathematics is essential to being able to do work that economists think is good.

So no-one else to UCLA? I have two people on the list so far, and the campus visit is next weekend!

I don't think anyone can tell you whether an MSc is worth the cost, but it's definitely true that LSE has placed a lot of people in Ph.D programs successfully, including many who were not successful with Ph.D applications the first time. I also think it is harder to get letters to transfer from a Ph.D program without something "unexpected" happening (the people there generally will want their good students to stay), and admissions committees tend to be a little more reticent about admitting people from other Ph.D programs (at least that's what I hear). Doing well at LSE should help to deal with any inconsistencies up until now (including the law school thing), and generally people find their applications improve a lot the second go around. So the LSE MSc would help, it's just a matter of whether or not the time and financial costs are worth it to you, especially given that even a Ph.D from the best of programs doesn't ensure success on the job market.

Well, I was kind of being facetious That being said, I think these discussions can be productive, and I think being conscious of the reasons why and why not you are choosing a school can make you conscious of when you are making decisions for the wrong reasons (e.g. external reputation can be a bad reason to choose a school), and can give you a sense of reasons that you were underweighting (close supervision, etc.). I think it's a bit different than having an overspecified model...

I feel the opposite; not modelling would drive me crazy. You can't include everything, but you can minimize the error term.

Good point. Obviously you know better.

Certainly, as a prospective student interested in applied formal theory and methods, I agree with this. I also hope my comments weren't taken as "anti-Rochester" given that they're easily one of my favourite schools, and I think they've produced a ridiculous number of fantastic scholars. I'll add that in terms of rigorous training, people should also remember to consider the broad strengths of the school, as well as how easy it is to do interdepartmental work at a particular school.

Oh one other thing; when considering placement, think about how much of the success (or lack thereof) is based on the quality of the students coming in, and how much is based on the department's training. Generally, you should be concerned with how much a department will increase your job marketability, not the success of the average student. However, this kind of thing is difficult to measure, so use with caution!

This is a good summary of many of the considerations I suspect people should take into account (and that I am considering myself). I also think you should be able to imagine a couple of different dissertation committees, so if one or two faculty members don't work out, you aren't out of luck. An issue that I'll add (partly because it's one that's important to my decision) is to consider the advantages and disadvantages of diversity versus specialization. For instance, a department like Rochester is very specialized, and as a consequence it has a group of students and faculty that can discuss research on common terms (i.e. using math), and has research seminars, training, etc. that are directed towards those kinds of students (even with departments that aren't specialized in the same way, you may be weighing strength in your particular area versus strength in a broader subfield/political science in general). On the other hand, being exposed to a diversity of opinions, approaches, and interests can be really valuable (which can also make interdisciplinary opportunities important). See, for instance, this article: http://www.nytimes.c...nce/08conv.html You should also consider where you'll be left at a department if your interests change at all. If you're risk averse, even if you think your interests won't change, you should acknowledge that many people before you have thought the same thing but radically changed their interests during their Ph.D. I suspect that being in a Ph.D program will be a unique intellectual experience, and being exposed to a broad range of ideas may mean that the stuff that seemed most interesting as an undergrad may be trumped by new stuff you just discovered (or just became more familiar with). So that's another reason why it's worth considering the broad strength of a department outside your particular area of research.

Just bumping this to make sure it doesn't get lost; so far my lists are pretty sparse, with the exception of Madison's!

Well, for what it's worth, I think you've made a number of positive contributions to the forum and should be commended on them. I'll compensate for the downvoting when I can, but I also wouldn't concern yourself too much with the reputation system of an internet forum

I'm definitely not relishing turning down three of the schools that admitted me, given that they all have serious strengths that I would miss out by not going there. I'm feeling good about the schools I might go to, but I'm also kind of intimidated by the cohorts I'd be working with!

Congrats! Very exciting.