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Five years'. My belief is that you should always email the graduate coordinator to ensure that they've received your supplementary materials; all sorts of things can go wrong with anything that relies on third parties, including the post office or FedEx. Asking "when will I hear back?" is just a nuisance, but you might be able to include the question with a legitimate check-in.

It's what Dr. Falk said when he called me. But my memory might be playing tricks on me; he might have said "about 15" instead. Either way there are still slots left (although I have no idea how good the remaining chances of funding are, if any.)

They were the top 15 candidates, so there will be other admittants, yeah.

I heartily approve of DIY science. I can think of at least three ways to improve your methodology, though: 1) Along with each prediction, state your estimate of the chance that your interpretation of the cards was incorrect. (i.e., given that the Tarot deck has the real power to convey some message, the chance that you correctly guessed what that message is.) 2) Also state the estimated probability, given that Tarot does not work, of the event portrayed in the message occurring, given all the non-Tarot information that was given to you. (I posted a thread earlier providing a rough estimate of the chances that one would get into any schools, for instance, given only the information about what schools an applicant applied to.) If the interpretations you get are likely to be highly idiosyncratic - "if you have good news it will come this week" for one school, "you will not get in" for another - then we may have to throw out predictions whose anterior likelihood is not easily calculable. Your method is to ask for a dream school, whose probability of admission (knowing nothing else) is presumably somewhere around the admissions rate - the dream school is probably the most selective school to which the candidate applied, which should push the chance down, but also a relatively stronger fit for research interests, which should push chances up. What would be ideal would be if, instead of asking for dream school, you asked for a randomly chosen school (research participants could roll a die) and requested that neither the person's profile nor their emotional connection provided information that would be useful for determining whether they got in or not. In that case, if there are no selection effects (Gradcafe users participating in an empirical test of Tarot reading) then the anterior non-supernatural likelihood of admission is just the admissions rate. Of course how many data points you should throw out like this depends on how big your samples are and how many events you can provide with an accurate anterior likelihood. 3) Include a control group: a group of data points composed of false queries and emotional memories. Some quick googling can show you some sites online used to conduct research studies, which may help you raise your sample size.

How would you estimate the chances of a given candidate being admitted to a given school, given (whatever information about the applicant and school that you would like to be considered known?) Well yes, obviously this doesn't attempt to address funding (though you could incorporate it if numbers were available.)

NYU fully funds everyone, if that's any consolation.

So: I chose my POIs and programs based on what I was genuinely interested in (as one would hope.) However, those programs aren't in my undergraduate major (which is, at least, another social science.) I took very few courses in the relevant discipline, and none of my research experience is related (although the skills are transferable.) My SOP emphasized that I've done a lot of independent reading in the subject matter and hopefully conveyed my passion for it, although I'm afraid I didn't sufficiently state what research projects I'd like to undertake, or provide outside evidence of my fit (not that I have it). None of my LORs are from professors in the discipline, although two taught classes related to the subject matter and (I hope) can attest to my passion and independent reading in it. Most of the POIs subscribe to a research program that explicitly disdains disciplinary boundaries, though beyond one case I don't know how many of them are on the adcoms or how much sway they have. I don't want this to be another "will I get into grad school?" thread, which I guess it is, but mostly I'm unclear on how professors measure fit from their end - how they weigh paradigm, substantive interest, and research skill set. From my end I just applied to those who research got me excited, but perhaps I didn't put enough thought into how I'd look from the other side.

To my mind Bread Loaf has been unfairly panned. While it does have a reputation as a "party school" - the undergraduate population is constantly baked - the ranking of its graduate program continues to rise. Academic traditionalists are sure to like its fine marble architecture and (frankly) whitebread demographics, while radicals may appreciate the role various other cultures have played in helping it grow. Don't worry about your funding kneads - its upper-crust benefactors have ensured there's plenty of research dough.

Liberal. Best live music scene in the country. Just the right mix of northern California weird and Texas weird. Bats.

There's no reason to throw away perfectly good political science skills just because the topic is bread. Rational choice theory: this is, in fact, the best loaf of Italian bread you are willing to bake, given the marginal costs of producing a better one. If you valued the product of another baking method more, you would have used that. Classical realism: get used to fucking up your breadbaking, man; it's just part of human nature. Constructivist realism: insufficiently chewy bread is inevitable given the anarchy of the kitchen. Perhaps you should improve its feng shui? Idealism: first, make sure you and the other breadbakers are organized democratically. Then invade the bread and reorganize the yeast along the lines of a parliamentary republic. If you and the bread can't cooperate after that, one of you wasn't really democratic. Critical race theory: what else would you expect? Just like the bread, the reigning ideology adopts a mix of whites and browns on the outside, while remaining just as white - and as dense - as ever in substance. Instrumentalist Marxism: how nice of you to complain about insufficient chewiness when the real purpose of the bread is to poison your neighbor! Structuralist Marxism: wait, stop! I'm sure you don't know this, but the bread is poison! World-Systems theory: asking questions about "the bread" is meaningless. After all, the bread was produced from other things in the kitchen with other things in the kitchen for other things in the kitchen. The proper unit of analysis is the kitchen-system. Positivism: can we really say that your baking the bread in the way you did lead to insufficient chewiness? Keep baking bread just the way you did until we can get the p-value down to to at least .1. Joe Sixpackism: Unenchewment up again??? I say we throw the bâtards out!!

(Actually, there's one case in which GREs give us a very easy calculation: if there's an unofficial math cutoff which, if you make the cutoff, they don't care about.. Since the quant section has an incredibly uniform distribution - one point is very close a quarter-percentile throughout - means equal medians and, if a school's applicant pool acquires its average from being drawn only from the top whatever% of scorers (who are drawn from that group uniformly, at least with respect to GRE scores), an admittant average of x implies a cutoff of 2x-800, such that your chance of being admitted to the program is zero if you're below the cutoff and (admission rate)(800- applicant average)/(800 - admittant average) if you're above. But that's assumption-heavy.)

I think the following toy model might be an unbiased estimator of chances given GPA/GRE - or other factors, for that matter, like number of pubs - on the unlikely chance that you have both the averages for the applicant and admittant pool. Suppose that every applicant has either a low or a high score - say, a 3.5 or a 4.0. (Th average of both pools should be between these scores, and your own score should be equal to or between them.) Then you find what the ratios between applicants must be and the ratios between admittants must be. (If the applicant average is 3.7 and the admittant average is 3.8, then 40% of applicants have high scores, as do 60% of admittants.) Then consider the admissions rate - if it's 20%, say, then that's composed of 12% high scorers and 8% low scorers, meaning that 12/40 (30% of) high scorers and 8/60 (13.3% of) low scorers are admitted. Then give yourself (your score - low score)/(high score - low score) probability that you are in the high-scoring category. So if you have exactly the applicant average your chances remain equal to the admissions rate (as we would expect), and if you have a 3.8, your chances are 0.6*0.4+0.4*.133 = 29%. I'm certain that this model is an unbiased estimator if you really are only measuring a binary factor - if there's gender-based affirmative action (or simple bigotry), say, or measures that are effectively binary, like if you know the percentage of applicants and admittants who are polylingual and either (a) that you're monolingual, (b ) that you speak a typical number of languages for a polyglot (presumably not in this example, since it's probably one-point-something), or © if you speak some number of foreign language and don't have good reason to suspect it's better or worse than the number typical for polyglots (again unlikely, since it's probably one-point-something, but.) For gradients like GPA - and especially the GRE, which has a known and very wonky distribution - my intuition is that something like the toy model above is a good basis for a true unbiased model, but there's some other set of equations, based on what you know otherwise about their distributions, that will tell you 1) what to set as the high and low scores (possibly even ones which are impossible for real students to have) and 2) how to weight your score relative to each, based on something other than p(high)=(yours - low)/(high - low) (possibly weighing one of them more than one and the other negatively). Actually, for a quick proof that your choice of high and low scores matters: suppose we selected a 3.0 and 4.0 for the high and low scores above. In that case 70% of applicants and 80% of admittants have high scores, meaning their 20% admissions rate is composed of 4% low scorers and 16% high scorers, such that 16/70 (22.85% of) high scorers and 4/30 (13.3% of) low scorers are admitted. A cookie to whomever can tell me how to determine the proper high and low scores. (Probably easier cookies: given everything in the OP (or your own, better model) and that you've been accepted into institution Q, what is the conditional probability that you were admitted to institution R? Given that you were rejected from Q? What is your expected number of admissions, given that you will be notified of admissions in an unknown order and then rejections, and that the first notice you receive is admission to Q? Given that you expect to hear admissions in a known order, and then rejections, and that the first notice you receive is admission to Q?)

Out of curiosity, how common are pubs among MAless soc applicants?

Last year people started hearing 18 Februrary. What do you mean by this?

The weasely answer is that, if there aren't distinct trends, rates should be unbiased, and thus so should average admittances. This doesn't work for every function of admission rates, though, unless the distribution is really wonky. I feel like it should baaaaaasically work up until you get admitted somewhere, and then you'd need an idea of what the distribution is, at least if interannual variance is appreciable. If you do know that there's a trend, adjust admission rates by whatever you expect the trend to be (I can't think of any better basis than intuition for its value, but if you're familiar with your field your intuition might be pretty good.) Yeah, this is much more meaningful for those applying to schools that don't offer terminal master's programs, or who are shooting for a PhD but would settle for an MA if offered as consolation prize, &c. If your department offers something qualitatively different like an MFA or MPP it's no good (unless you want to estimate their proportion of the program.) Glad you liked it

As a proper Haraway fan, you should consider any available calculators to be part of your brain. Anyway, once you get past the notation, the concepts behind everything here are pretty simple. Here's what The Internet has to say about your schools: Duke: 394 applicants, 5% (20) accepted, 12 enrolled. Harvard: 405 applicants, 4% accepted. Columbia: 627 applicants, 12% accepted. UVA: 463 applicants, 15% (67) accepted, 33 enrolled. Georgetown: (no data, so let's just arbitrarily assume it's 20%) Wake Forest: 19 applicants, 79% (15) accepted, 7 enrolled. First, let's take a look at what our expectations would be before you were rejected from Duke. We're assuming, in our prior, that the admission rates reflect your actual chance of getting in. (As Bayesians, when we say "chance," we mean "justified expectation." Adcoms aren't rolling dice, they're looking at your GREs and SOP and so on and such forth. There may be no random elements to the process whatsoever. But since I don't know anything about you other than the schools you applied to, this seems like a good ground for at least my own expectations. If you believe you've got an edge that gives your application better odds than that, you might want to adjust your expectations above the base admission rates - assuming you have a good basis to believe that you have more of an edge than the randomly chosen student, who probably also believes she has an edge.) A nice fact that flows from this is that the average expected number of admissions - what we'll refer to as μa, μ just being the greek letter for m, which stands for "mean" - can be found by adding the admission rates together: 5+4+12+15+20+79=135, so you expect to get into 1.35 programs on average. You don't, of course, expect to literally get into 1.35 programs, but whatever else, it is the case that if you add together the chance you get into exactly one program, plus twice the chance you get into exactly two programs, and so on until six, you'll end up with 1.35 - at least as long as those independent probabilities (that is, for each individual school) hold. (I could prove this, if you like, or you could just accept it.) But this doesn't tell you whether you have a small chance of getting into lots of them or a very very good chance of getting into at least one. For this, we look at at the enrollment rates. We could of course look at further comparable schools, but since I have no idea what the rankings of lit departments are and am far too lazy to look it up, let's go with what we have, which is that it seems that each department seems to send out twice as many acceptances as it actually enrolls. Since the only people that enroll are those who received at least one admission, and we're assuming no one would go through this horrid process unless they really wanted to head off to gradville, this means that those applicants in your field you received at least one admission offer received about two on average. (Maybe they all got two, maybe 10% get 11 and the rest get one, who knows?) Now, if you're typical in the number of applications sent out, this means that you should expect that, if you got in anywhere, you got in to two programs on average. Since the alternative to getting in anywhere is getting in nowhere, we say that μa = 0*p(a=0) + b*[1 - p(a=0)] 1.35 = 2*[1 - p(a=0)] p(a=0) = 1 - 1.35/2 = 32.5% ("p(X)" just means "the odds that X") This is actually a really terrible estimate because it's actually larger than your chance of not being admitted to Wake Forest alone. Let's relax the assumption that n=n̄ (that you're applying to a typical number of programs) and thus that b=B (that if you get in, you can expect to get into the same number of programs as other graduate students, on average.) Ex recto, applicants as competitive as you actually apply (on average) to eight programs, not six: n̄=12. We still, of course, assume that they get into two programs on average when they get into anything - that's what the data says, after all. Since the competitiveness of the schools they're applying to remains the same, we just multiply their expected total number of admissions expected by the increase in number: (number of admissions per applicant in your field) = (your expected number of admissions)(the number of times more the typical competitor applied than you) μā = μan̄/n μā = 1.35(8/6) = 1.8 So they expect to get into 1.8 schools on average, and to get into 2 schools on average when they get in anywhere, meaning that they get in nowhere 10% of the time. Now if this person had applied to less schools - say, six - then they would expect to get into none p(ā)^(n/n̄) = p(a=0) 0.16/8 = .1778 17.8% of the time, which is what you can expect, since this is your situation precisely. (I had to choose wonky numbers in order for everything to come out consistently- some combinations of selections imply that someone really is over- or under-reaching, all else being equal - but why we raise these chances to exponents of the number-of-applications ratio might be easier with a better example. Suppose that the applicant who applies to the sorts of schools that you do only applied to half of them on average, and had a 50% chance of not getting into any of them. If he applied to twice as many, he'd have a 50% chance of getting into somewhere in the first batch, and an independent 50% chance of getting into the second, meaning a 25% chance of getting rejected overall. ("Why are we treating them as independent when we know that they probably correlate?" you may be asking. The answer is that we're dealing with a toy person and keeping his level of competitiveness, whatever that might be, constant. Causally, if you applied to twice as many programs as you did (and it didn't cause you to skimp on your SOP or whatever), your chances of total rejection would go down in this simple geometric way, as none of the programs would affect whether you got into any of the other ones. However, when you learn that you have gotten into programs, that serves as evidence that you're one of the people that programs happen to like.) Since you get in somewhere 82.2% of the time, and expect to get into 1.35 programs on average, you should expect to get into 1.64 programs when you get in somewhere. However, we also know that you got rejected from Duke. How should that revise your expectations? Bayes' Theorem says that p(H|D) = p(D|H) * p(H) / p(D) ("p(A|B )" is just statistics for "the odds that A, given that B") H stands for hypothesis, or our prior expectation, D for the new data we've just encountered. (Not everyone thinks visually, but if you do, it may help to associate Bayes' Theorem with a sort of two-by-two graph, with one axis divided between H and non-H and one between D and not-D, each cell expressing a number, and the numbers in the four cells adding up to 100. Interpret p(H) as the sum of the H row, p(D) as the sum of the D column, p(H|D) as the portion of the D column's numbers lying in the H-and-D cell, and p(D|H) as the portion of the H row lying in the H-and-D cell. Play around with squares like these on some loose leaf paper, seeing what information you can extract from other information. You should eventually grok it.) In this case, since we want to know the chances that you got into nowhere, given that you didn't get into Duke, H: you didn't get in anywhere D: you didn't get into Duke Fortunately all of the relevant terms are pretty easy: p(D|H) is the chance that you didn't get into Duke, given that you didn't get in anywhere. This is equal to 1, obviously. p(H) we already calculated, it's 17.8%. Note that it's our prior estimation that matters for calculating this term, before you knew Duke rejected you. And p(D) is just the prior chance that you were going to be rejected from Duke, which we already knew was 95%. So the new chance that you're screwed is p(a=0|blue devils noooo) = 1 * .1778 / .95 = .1872 Meaning that your chances of making it to graduate school plummeted from a soaring 82.2% to a miserable 81.3% - literally not even a percentage point, which is the biggest increment that our base data measures anyway (leaving aside the fact that we just made a couple figures up.) In other words, you're still at very significant risk of getting an English degree. (I hope you don't think that I'm picking on you; if it bothers you that I used you as an example, I'll genericize everything, and hope you accept my apologies - I've just found that many students find it more concrete to substitute their own situations into a model, is all. I'm also never completely sure, on the internet, where to draw the line between overcomplicated and patronizing - if you (the generic you, not just cyborg) find I err too much in the former direction, I can try to explain from a different angle; if too much on the latter, just assume that I'm dumbing things down for all those other people.)

I'll count that as a success.

Warning: nerdy and completely useless except as game; if I didn't want to spend a totally unreasonable number of hours engaged in pointless intellectual pursuits, I wouldn't be applying to grad school My math or reasoning in general might be off; if it is; call me on it. Note also that I'm being a Bayesian about things, so read "there is x% of y" as "you should estimate the probability of y at x%." So, you've applied to some set of schools. For most of them, Peterson's lists their number of applicants, admissions rates, and actual number of attenders from each class. How would one produce an unbiased estimate of your chances of universal rejection (and, possibly, a few other things) from just this information? (Of course if you have more information than this, you would want to make the model more complex to incorporate that extra information, and I'd love to see models that incorporate GPA/GREs, &c.) a : the number of admissions you will actually receive n : the number of schools to which you have applied pi : the admissions rate of the ith school to which you have applied (the order isn't important) Prior assumption: for the schools for which you have applied, you have no particular reason to believe that you are especially more or less competitive than the typical applicant. This doesn't mean that you expect to be exactly in the middle - if that case you know you would be universally rejected, assuming admissions rates are all below 50% - but that you expect a 1% chance of being in the first percentile of competitive applicants, a 2% chance of being in 2nd percentile or better, a 3% chance of being in the third percentile or better, and so on. If you can accept this prior, your chances of being accepted into school i is, conveniently enough, px, and the average expected number of schools you will get into is μa = Σni=1pi = p1 + p2 +p3 + ... + pn or the additive sum of their admission rates. However, you don't know how well these are correlated with each other. If they're maximally correlated - they all admit students on precisely the same criteria - then your chances of a wipeout are equal to the complement of the most favorable admissions rate among your schools; if they are totally uncorrelated, your chances of a wipeout are equal to the multiplicative sum of their complements; if if maximally negatively correlated, then your chances of a wipeout are min(0,1 - μa). Common sense says that they should be positively but not maximally correlated, but how much? Fortunately you know b : the number of admittances schools in your field send out divided by their number of graduate students per year, where "field" is selected such that its competitiveness roughly reflects the competitiveness of the set of schools to which you have applied (Sneaky assumption: the number of those in your field you are admitted to grad school and choose not to go at all is zero, or at least small enough to be ignored.) Thus we know that a randomly chosen applicant in your field - someone, by the first prior, who is as competitive as you - should expect, given that she is accepted into any schools, to get into b schools on average. If, as would be convenient, her expected total number of admittances including the possibility of wipeout is the same as yours, μa, then your/her chances of a wipeout, p(a=0) are μa = 0*p(a=0) + b*(1-p(a=0)) μa/b = 1 - p(a=0) p(a=0) = 1 - μa/b If you haven't applied to the typical number of schools However, this randomly chosen applicant, who is as competitive as you, isn't necessarily applying to as many schools as you - she's applying to n̄ of them, which might be more or less - although by assumption we suppose the schools she applies to are as competitive as your own. So in fact her expected number of admissions, μā = μan̄/n and μan̄/n = 0*p(ā=0) + b*[1-p(ā=0)] = b*[1-p(ā=0)] μan̄/bn = 1 - p(ā=0) p(ā=0) = 1 - μan̄/bn n̄ = bn * [1 - p(ā=0)] / p(ā=0) If we knew n̄, we could know p(ā=0) as well - or visa versa - and thus p(a=0) = p(ā=0)^(n/n̄) p(a=0) = (1 - μan̄/bn)^(n/n̄) or p(a=0) = p(ā=0) ^ { p(ā=0) / b[1 - p(ā=0)] } Can we produce n̄ or p(ā=0) independently? Unfortunately I don't see a way to do so, limiting yourself to the Peterson's data. Choose a number that seems reasonable for one or the other based on anecdotal evidence, or find some publicly available data (and post it here, ideally.) But either way an estimate of one should get you to p(a=0). This should also give you B=μa|a>0, the expected number of schools you get into in the event that you get into any schools at all: μa = 0*p(a=0) + B*[1-p(a=0)] B = μa / [1-p(a=0)] p(a=0) = 1 - μa/B Revising in light of results All of the above assumes that you haven't heard back from any schools yet. If you get an acceptance or rejection, how should that affect your expectations of getting into other schools? Unfortunately the ratio of acceptances to grad students doesn't tell us what the distribution of acceptances among the admitted is. Suppose you hear back from your first institution, University Q - an acceptance. Will you get into another? According to Bayes' theorem, p(a>1)|into Q = pQ|a>1 * p(a>1) / pQ Only pQ is a known constant, so we need to guess pQ|a>1 * p(a>1). pQ|a>1 is the chance that, given that you got into more than one school, one of those schools was Q. This is equal to pQ|a>1 = (pQ - pQ|a=1) / p(a>1) so p(a>1)|into Q = [ (pQ - pQ|a=1) / p(a>1) ] * p(a>1) / pQ p(a>1)|into Q = (pQ - pQ|a=1) / pQ (One intuitive, but clearly wrong, estimate of the chance of admittance to Q given only one admission is pQ|a=1 = (pQ / μa) leading to p(a>1)|into Q = [pQ - (pQ / μa)] / pQ p(a>1)|into Q = 1 - (1 / μa) This implies that an acceptance from one school is nearly as good a signal as a decision from another, and in fact that getting into an easier school should revise your expectations up more than getting into a harder school - prior to learning anything, you have a higher expectation of getting into at least one school other than your reach than getting into at least one school other than your safety, but in fact getting into your reach and into your safety brings their chances to the same level. In fact if there's any overlap between expected admissions at all then the chance of being admitted to an easier program but not a harder program is not only more likely than the reverse, but in a way that exaggerates their independent probabilities.) One obvious method is to use recursion: imagine someone, as competitive as yourself, who applied to every program but the one you've just heard back from, i.e. μa2 = μa - pQ, n2=n-1,n̄ remains constant, and her field is your field, such that B2 = (μa - pQ) / [(1- { p(ā=0) ^ [ (n-1) / n̄ ] }] p(a2=0) = 1 - [ (μa - pQ)/B2 ] In that case, p(a=0) - p(a2=0) = pQ|a=1, and - if we want to write out a big ridiculous equation - p(a>1)|Q = {1+ pQ + {(1- [ p(ā=0)(n-1)/n̄ ](μa + pQ)}/(μa - pQ) - p(ā=0)n/n̄ } / pQ p(I made some sort of obvious arithmetic mistake or worse) > 0.5, so the above is most likely nonsense. If it's right then calculating how to update your chances in case of a rejection should be trivial.

"Well, this certainly did include an application fee."

It really depends on when you should expect to hear back from your schools, as indicated by the Departments (or, more usefully, from the results search.) I haven't heard back from any of my programs either, but with the exception of UCB I don't expect to for another two weeks. (This isn't true for all programs in my field, just the ones I applied to.) If your friends have applied to some of the same programs as you and heard positive results back, that is indeed a bad sign - just remember that unless you're some sort of golden god (of luck, if nothing else) most of your results will be rejections. Bad news, not catastrophic news (unless, like, people have heard back from all of your programs.)

Have you read Gerry Cohen's Karl Marx's Theory of History: A Defense? I don't agree with all of his conclusions or interpretations, but it's a great stimulus for thought and shows that analytics can be as socially engaged as continentals.

The social sciences seek to investigate phenomena that, by their very nature, are ideologically contested. One can, of course, refrain from activism - though I don't see why sociologists should refrain any more from it than butchers, bakers, or candlestick makers - but can't imagine any sociology worthy of the name that wouldn't inspire attacks on itself. Any conclusion to certain questions is going to be liable to denouncement from somebody.