Paulcg87 Posted December 21, 2020 Share Posted December 21, 2020 (edited) I just finished the first term/semester of the first year of my political science PhD. Just a few things for next year's incoming cohort: Learn how to code in R. Don't fight the inevitable, just learn it. - This is the most important advice I can give to anyone entering a PhD program next year who doesn't have a strong computer programming/coding background. In the past, users on here have emphasized how important quant methods are ad nauseam, and this is true and I don't want to take away from it. You do need to understand undergraduate algebra based statistics. You do need to know basic concepts like hypothesis testing, linear regression and p values, ordinary least squares, t-tests and average treatment effects. What you also need to know that is as important as a basic knowledge of stats is a basic knowledge of coding in the 'R' language. R is a computer coding language similar to Python but a bit more customizable and complex. It has become the gold standard for a lot of social science PhD programs. Python is also important and used more than R outside of polisci specifically so try to learn that too if you can. Polisci PhD students (in North America at least) are no longer doing stats on paper or in Stata or in SPSS; almost everything is being done in R. My entire first year PhD quant methods framework uses the R language, as did my master's degree quant courses. It's no longer enough to have a basic intro to stats; you need to know how to do those stats in R. If you aren't familiar yet with R or with coding in general, take an online class and download R Studio and learn how to code in R markdown and then practice applying quant analysis to sample datasets/data frames. Learn how to code functions, plots and tables. It will make the first year of your PhD so much easier. You can learn R during your first year and some in my cohort are doing that right now, but they are struggling because of the extra workload. It's enough to be dealing with all of the other pressures of the first year and the required coursework; also learning how to code from scratch simultaneously is just one extra thing that you don't need. And for those who are thinking "It's ok, I plan on doing mostly qualitative/ethnographic etc research and I don't need to know R", trust me, unless you are a theory student, you will be using R. It isn't possible anymore to avoid R or computer coding in the majority of North American polisci PhD programs if you are a non-theory student. So much of the field has moved from observational to experimental and from qualitative to quantitative that even if it isn't what you plan to do professionally, ever, you still have to learn how to do it. I think the logic is that if you're going to be competitive in applying for academic/TT jobs some day, you at least need to know enough about quant methods and coding in particular to be able to explain it to your students even if you avoid doing it yourself. Don't stress if you don't like your field(s)/subfield(s) What most North American polisci PhD programs have in common is that you have to choose one or two fields/subfields (comparative, IR, theory, development, policy, American, etc). Some people, including myself, go into a polisci PhD sure of the field we are interested in studying and then change our minds a few weeks or months in. Sometimes it even happens after the first year. Fields are NOT set in stone when you are starting out and it's ok if you want to change. The tradeoff is that if you change fields after you start your PhD, depending on how long you wait, you could be adding an extra term or an entire extra year to your PhD that might not be funded if you received a fixed amount of funding. I, for example, received 5 years of guaranteed funding, so if I stay past that for whatever reason, I'm on my own when it comes to money. It is what it is, but don't stress about being locked into a field/subfield. Also note that changing fields/subfields within a political science PhD program is different from changing your PI/advisor/supervisor. The size, culture, funding and other attributes of your PhD program will determine how much flexibility, or lack thereof, you'll have, but nothing is usually impossible if you have a change of heart after starting. Don't stress about online education My department/school started off this past fall semester in a hybrid with in-person and online courses and then switched to entirely online for everything once the second wave started a few months ago. Yes, online classes are not as good as in-person classes in just about every way, including networking with your cohort and in-person learning. I found it so much harder to do the weekly labs for my stats/coding course when everything went online because we were not together in the computer lab and I couldn't just ask the TA what a line of code meant in person. Personally, I'm not a fan of online education and I don't like Zoom. When we switched over to online only, one of my classes was in Zoom, one was in an Adobe program, one was in Microsoft Teams and a lab was in Blackboard Collaborate. Literally, every single one of my classes/labs used a different online learning program/method and it was very frustrating. It was a lot harder to do do these things this way, but it was not the end of the world. We got through it as a cohort, we commiserated over Zoom study sessions and on our cohort facebook page/group, and life went on. I'm happy to say that we didn't have any of the first term curriculum delayed because of COVID, and you won't either, whether everyone is vaccinated and everything is in-person in the fall of 2021 or it is still online. Hopefully it's the former, but if it's the latter, your department will make it work. If anyone who is still on here from last year is also just finishing the first term and has anything to add, please do. The single biggest piece of advice I have is to learn R as soon as possible. Even if you can't take a class on it, try some of the free online learning modules, download R Studio, use the sample datasets and start practicing with the mean function in R markdown. Also, I highly recommend one of the interactive texts we are using this year for our 3-course stats/R coding sequence, which is available in paper, PDF and Kindle formats: Kosuke Imai. Quantitative Social Science: An Introduction. Princeton: Princeton University Press, 2017. Edited December 21, 2020 by Paulcg87 Added QSS ref ovejal, needanoffersobad, irinmn and 9 others 7 5 Link to comment Share on other sites More sharing options...
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