Difficulty of First Year Courses

[gilbertrollins]

So, I've heard mixed reports on here about the difficulty of first-year courses.  There is a syndrome (not permanent) called Law Student Blindness, where people literally go blind from reading too much and not sleeping.  Economics programs are famous for their first year courses being soul-crushing hard.  And I presumed this was the case for any graduate program -- thousands of pages of reading per week  -- maybe a couple few hundred pages of writing by the of the semester.  I was looking forward to that.  Can anyone elaborate on the difficulty of their first year courses, and whether they felt challenged.  I was really looking forward to the boot-camp aspect, breaking you down and building you back up again with knee-jerk training in how to pull the trigger on your ideas no matter how terrified you are.

[mbrown0315]

For those without a strong quantitative background and who are nervous about doctorate-level math, I strongly encourage reading this text: http://www.umass.edu/preferen/Mathematics%20For%20Humanists/mathlit.pdf

[gilbertrollins]

Don't you think most students would need a bridge to digest Gintis' little document?  He's very formal.  And very few soc students will write formal proofs, ever, no?  The section on probability is probably useful preparation for the metrics sequence, but again someone who's only had a few semesters of calculus, if that, is going to have quite a bit of trouble reading Gintis.  He's going for mathematical elegance as against hand-holding.  I thought his title was laughable (and that's coming from someone who loves most of what Gintis does).

[mbrown0315]

Most of us won't write formal proofs, but we may need to understand them. At the very least it's useful to become familiar with notation.

[Illusio80]

I once spent two years in a very highly ranked sociology program.  The reading load was very thick, but overall, the standards were not that high, surprisingly.  It wasn't that difficult to get A's.  (After that program didn't work out for me, I struggled a lot more in a professional masters program at a different university.)  The "intermediate" statistics sequence was a bit of a joke - one of the professors couldn't teach his way out of the proverbial bag - and the overall expectations were very low.  The thing that hit me upside the head was the second year masters paper process, as well as the problems of not having good funding.

 

(So here goes nothing, again...)

 

I can't say for sure how difficult your first year courses will be if you are doing hard core quantitative stuff.  If my former program is any indication, I would not expect wonders in terms of quality of teaching.

[SOCgrad987]

I just finished my first semester at a top 15 university. As for the course load, there was quite a bit of reading and paper writing, but it was not too stressful. As for statistics, I had a great professor who made it interesting and relatable, focusing more on interpreting the data than memorizing formulas, etc. Our program emphasizes that grades no longer matter (or are not the sole focus of the program)--it is now about research productivity and thinking critically and innovatively. The difficulty was not the material (as most of us at least found it interesting), but balancing TAing, RAing, coursework, and outside reading in preparation for our master's thesis. 

[gilbertrollins]

Are there qualifying exams at your programs that cover your core material?

[Illusio80]

At my former program there are comprehensive exams, but they generally come after the masters process.  I think it may have been possible to take an exam before getting the MA, but it isn't common.

[gilbertrollins]

 As for statistics, I had a great professor who made it interesting and relatable, focusing more on interpreting the data than memorizing formulas, etc. 

What about the derivations of estimators and corrections?  The theory behind everything?  I want to get under the hood, and am wondering if taking the first year econometrics sequence at my prospective school will be dramatically more rigorous than the sociometrics sequence or not.

[jacib]

The boot camp thing is famous in econ programs, and soc programs don't do it nearly the same way.  There's no math boot camp here, really.  There's a soc theory boot camp/"getting everyone up to speed", but that varies by program.  You'll find the intro stats a joke.  If you go to a big school, like Wisconsin or Michigan, their demography sequences and stuff will be useful and cool for you I'm sure (and you will get under the hood), but in most places, one is more likely to want to cry from the theory reading than the math.  At my school, all the advanced math courses are outside the department(which everyone I've talked to agree is good thing).  While our "intro stats" class may be a joke, the people who want to continue on get seriously rigorous training... just in the stats department (occasionally in the poli sci department).  Networks stuff is mostly taught to people through mentorship rather than a class, weirdly.  Like you're expected to do it... and when you hit a problem, then you go to the professor.  That's just how my department works, but I think it's known for independent graduate students rather than graduate students who are broken off a chunk of the professor's pet project.

 

Edit: First year will still be very difficult.  I was shocked when I figured out that all of my colleagues were smart.  Even at my undergrad school, I thought half the kids were jokes, but here (almost) everyone is really actually smart.  That's a change, and you'll find ourself not feeling smart enough (the kids I didn't feel as smart as told me that they didn't feel as smart as me--it's a common thing).  You'll be a lot more isolated, probably, than you've been in a while.  There are just fewer people around and fewer people you know.  You'll be spending way too much time alone with your thoughts, and even if you love being alone with your thoughts (which I do), it's a lot.  But it's not like the courses are difficult.  In PhD programs, everyone gets A's.  You're no longer working to earn a qualification, the grade you get is meaingless but you're learning to impress the professor and to produce things that you think are good enough.  It's a very different way of being evaluated and it takes a while to get used to.

Edited January 8, 2013 by jacib

[gilbertrollins]

I've done 20+ hours of independent study slash research assistance, and the As there are pretty much guaranteed, so I'm actually already struggling with the sort of "third year woes," like how to stay productive and manage myself independently, how to battle my own insecurity about formalizing my ideas without someone right there to cheer me on, etc.  

 

Sociological theory only makes me want to cry because these guys refuse to get their definitions straight, love neologisms, etc.  I love the theory itself.  I do not, alternatively, like writing deductive proofs.  Since leaving econ my first thought when I read any theory is "how do we test this," not "ooo -- that sounds intuitively correct."  Frankly I think verbiage (sociology) and mathematics (economics) end up serving the same masters: obfuscation and window dressing.  

I'm taking an applied stats course and probability one this next semester, so maybe I can test out of the soc stats course?  Will that be rude to ask?  I don't want to do sociometric theory itself, just get really under the hood.  So maybe I could take the econometrics sequence?  They're concerned about a lot of the same things; I'll just have to interpret it differently.  I don't think I'm going to be doing strat, or surveys, at least while my tenure clock is ticking (maybe later), so I could maybe skip the multilevel modeling stuff?  I don't want to give anyone the impression like, "So I hear your stats sequence is a real joke -- Ima go over to econ thanks."  I'm genuinely interested in learning how to test sociometric problems like cascades and other structural/interdependency issues (especially with language data).  

 

I was thinking it'd be a good idea to have my classics read before I start, like Durkheim, Parsons, Weber, etc etc.  I really like the symbolic interactionists.  Has their impact faded some?

[splitends]

"...so I'm actually already struggling with the sort of "third year woes," like how to stay productive and manage myself independently, how to battle my own insecurity about formalizing my ideas without someone right there to cheer me on, etc."

 

Oh, honey...those are not third year woes...those are from day 1 woes...

[health_quant]

What about the derivations of estimators and corrections?  The theory behind everything?  I want to get under the hood, and am wondering if taking the first year econometrics sequence at my prospective school will be dramatically more rigorous than the sociometrics sequence or not.

If you're serious about learning what's going on under the hood and you've had mathematics through multivariable calc, pick up Statistical Inference by Casella and Berger. This will take you through probability theory and mathematical statistics at the upper undergrad or lower grad level. However, if you're not planning on doing a lot of heavily quantitative research and/or teaching yourself a lot of advanced methods in the future, C&B (and what follows) would likely be overkill.

 

Alternatives to C&B:

• Mathematical Statistics (Wackerly, Mendenhall, Scheaffer) - similar topics to Casella & Berger, but at a lower mathematical level, in my opinion
• Mathematical Statistics (Rice) - a lower mathematical level than WMS, but some with weaker math backgrounds may find it as a good intro to the topics
• Mathematical Statistics (Bickel, Doksum) - slightly higher level than C&B; this one does a better job of emphasizing estimation of multiple parameters, while C&B sticks more to single-parameter estimation

 

You will need a good background in regression to make use of all the above statistical theory. For that, you might try the following:

• Introductory Econometrics (Wooldridge) - this is used for the first-year quant methods courses in many soc programs (e.g., Penn's and UNC's); his upper-level book, Econometrics, is commonly used as an alternative graduate text on econometrics to Greene's
• Introduction to Linear Regression Analysis (Mongomery, Peck, Vining) - a good alternative to the introductory text by Wooldridge, and written more from the statisticians' perspective; note that you should have a background in multivariable calculus and linear algebra to get the most out of this book
• Linear Models in Statistics (Rencher, Schaalje) - good for self-study, as all solutions are provided in the back; the first few chapters review the essential linear algebra, but again, you should have already be familiar with eigenvalues, eigenvectors, spectral decomposition, etc.

A first-year sequence in quant methods for soc will likely cover the basics of ANOVA, standard linear regression, logistic regression, and Poisson regression (all three of which are encompassed by generalized linear models) and possibly touch on multilevel and longitudinal structures, survival analysis, and causal inference. With stats, you can go much, much deeper than what I've listed above. That being said, I don't think it's necessary to know all of the above to be a good quantitative researcher in the social sciences, provided that you do have a solid understanding of the assumptions and limitations of whatever techniques you use.  

 

As for taking the first-year graduate econometrics sequence: I wouldn't recommend doing this unless you have an extremely strong background in mathematics. Many of the top programs assume knowledge of real analysis and some familiarity with mathematical statistics before entering the program. Some top-10 programs' econometrics sequences even begin with an overview of measure-theoretic probability, and dive right into asymptotic properties of estimators. These are not trivial topics. FWIW, my own background is in math (undergrad) and biostats (grad).

Edited January 9, 2013 by health_quant

[splitends]

Also, as to the difficulty of classes: in my limited experience, it's going to vary a ton based on who's teaching what, how much flexibility you're going to have in your schedule your first year, and what you do with that flexibility. If you set things up right, you could absolutely have been broken down by your schedule in my program this year. But you could also have made your life fairly easy. Just depends on which classes you choose to take.

 

If you ever feel like your classes are too easy, though, don't worry: there is ALWAYS more you can be doing. You won't necessarily have someone forcing you to do it, but you can and arguably should be doing work outside class.

 

And please don't be so quick to dismiss theory as verbiage. I think you'll be surprised how much you might learn your first year.

[gilbertrollins]

"...so I'm actually already struggling with the sort of "third year woes," like how to stay productive and manage myself independently, how to battle my own insecurity about formalizing my ideas without someone right there to cheer me on, etc."

 

Oh, honey...those are not third year woes...those are from day 1 woes...

 

Oh.  Yeah I'm getting the impression the sequence is structured really differently than in economics.  Your job in economics is to make it through first year courses (micro, macro, metrics, and more general math for economists) and pass comps.  If you don't pass - you either stay without funding and pass, or you leave.  Then you take field courses second year, and have to pass comps for those.  Then they give you the MA.  Then you start working with an adviser.  

 

Sounds like in soc you start from day one working really independently, and forming your project.  I like that a lot.

[SOCgrad987]

I'm not so sure most programs would let you take stats courses in economics. I am almost positive that would get a few jaws dropped from the faculty/staff at my department. I guess it would not hurt to ask--all they could tell you is no. 

[SOCgrad987]

Also, my program has comp exam after you complete coursework, which is usually 3 years. You also complete an MA thesis during that time as well.

[gilbertrollins]

As for taking the first-year graduate econometrics sequence: I wouldn't recommend doing this unless you have an extremely strong background in mathematics. Many of the top programs assume knowledge of real analysis and some familiarity with mathematical statistics before entering the program. Some top-10 programs' econometrics sequences even begin with an overview of measure-theoretic probability, and dive right into asymptotic properties of estimators. These are not trivial topics. FWIW, my own background is in math (undergrad) and biostats (grad).

 

Everything you listed is covered in most undergraduate intermediate econometrics sequences.  (Removed at Users Request)

 

@SOCgrad987

An econometrics course has very little to do with economic theory.  Well i shouldn't say that.  They are assuming basic stuff about production functions and supply and demand, etc.  But econometrics is basically a slightly-tweaked version of applied stats from the stat department itself.  From my little wikipedia skimming of multi-level models, these look like techniques to control for endogeneity among RHS variables - which is basically what instrument variables are for (popular in econ) and a number of other corrective estimators.  

 

@Splitends

I've been reading a ton of soc theory and learning a ton.  I didn't dismiss theory.  I dismissed overt verbiage and neologisms as unnecessarily obfuscatory and prestige-hunting.  This is a common complaint throughout academia.  And I related it to the common complaint about formalism in economics.  I don't understand why theorists get a prima facie pass to use hyper technical language and derive entire systems of thought that require enormous work of their reader, when theory can be made clear and accessible, and moreover testable.  This was C. Wright Mill's eviscerating complaint in Sociological Imagination, no?  

[gilbertrollins]

A good example would be Fligstien's use of the term "isomorphism."  Based on the Greek etymology, he's in the right ballpark.  Based on its most wide use elsewhere in academia -- mathematics (and it is quite a common concept here) -- he's being quite confusing.  An isomorphism in mathematics is a function that preserves the structure of an ordered field -- that is, all of the usual multiplication, addition, division, and ordering axioms continue to apply.  So for instance, a function that maps operations on matrices to many operations in calculus is often isomorphic.  One can think of these as the "mechanics" or the "joints" of a social structure, and claim that when one social structure is isomorphic to another, they have identical structure.  And one can metaphorically relate "linear algebra" and "calculus" to two different social structures.  But an isomorphism in mathematics is not merely "something that looks different than something else, but has the same basic underlying mechanics."  

 

It would be much clearer to say "organizations are operating more and more the same way, e.g. little leagues for XYZ reasons now mimic the modern business corporation" than it is to say "organizations are increasingly isomorphic to one another."  

 

On the other extreme, definitional constance via mathematical formalism as in economics can be and often is equally as detrimental.  But I do think greater definitional consensus is in order.  

 

(disclaimer: I read Fligstein's paper recently and have a small sample I'm working from -- not trying to pick on him -- he's asking invigorating and important questions)

[jacib]

In my program, which is on the smaller side, since we really only have intro stats (for people who don't know stats) and maybe two random upper level classes, everyone who does serious quant work took most of their stats in other departments and taught themselves a lot of stuff (and when they got stuck, they went to their adviser).  The requirement was just that you took a math class in your first two years.

 

People might look at you funny if you wanted to take stats in the econ department, but not if you wanted to take it in the math or stats deparmtents or the CS department (for data-mining related stuff).  At least that's how it is in my school--I don't think anyone has taken an econ class.  Other places, like especially big schools like Michigan and Wisconsin and Penn State, have really solid in-house quant people who teach solid sequences and you'd probably be encouraged to take some of those first at least (remember, half the point of classes is so you can get to know faculty).

 

And by theory, I meant classical theory not contemporary theory.  My theory course was mainly Marx, Weber, Durkheim, then just introducing Tocqueville, Simmel, the Chicago School (Park, Burgess, et al.), Goffman, Garfinkle (ethnomethodology), Merton (middle range theory), Bourdieu, and Foucault, and maybe one or two others.  Some might add someone like Benjamin or Braudel, but stuff like Fligstein is definitely not what we learn in a theory class.  Everyone is expected to learn Marx, Weber, Durkheim, the Chicago School, etc. and then a little bit of stuff that's happened since 1950, and even less stuff that's happened since Goffman and Garfinkle (and none of the stuff since Goffman or Garfinkle that you really have to know is American, I don't think).  Is that typical?  Probably roughly typical, though I think most schools will also have a contemporary theory class (some will probably even have a required contemporary theory class, we don't even have an optional one that's regularly offered).  FWIW, I believe Fligstein's "isomorphism" comes directly from DiMaggio and Powell's "The Iron Cage Revisited", which is both enitrely theoretical and probably one of the key articles in contemporary sociology, but not what that you'll likely to see in a theory class.

[health_quant]

Everything you listed is covered in most undergraduate intermediate econometrics sequences.  We didn't get into Instrument Variables, Poisson, or Logistic regressions in my econometrics course (econ undergrad - math minor), but I think we'll cover these in my applied stats II course this next semester (it's cross listed for stats grads and math undergrads -- uses the second half of a relatively rigorous "stats for engineers" type of book, a little bit of R, etc).  I'm taking probability I this next semester, but I seriously doubt we're going to derive anything from metric spaces.  My Analysis I course didn't even make metric spaces explicit.  

 

I'm not extremely strong quantitatively.  I didn't even have basic algebraic fluency a couple years ago (older student), but I've plowed through as best I could.  I refuse to be one of these regression monkeys, writing papers based on what a Stata manual tells me.  And I want to go out and measure a bunch of things in digital humanities / corpus linguistics that haven't been measured before -- so I have to know what I'm doing.  It would help if I liked math more.  But it is what it is.  

Of course, the difference between the undergraduate and graduate levels will be depth, and the relative depth of the coverage will also be contingent on the field itself. I'm not surprised than an undergraduate, intermediate econometrics sequence (in a fairly mathematical program) might cover nearly the same material at a comparable level to a graduate-level sociology sequence. (Some of the minor differences would likely include a greater emphasis on probit versus logit for analyzing dichotomous outcomes, but whatever.)

 

However, many undergrad econometrics and grad-level quant soc classes are hamstrung by the students' lack of exposure to probability and advanced statistics. Your background and current approach (hats off for using an engineering stats book) seem like they should prepare you well for being more than a "regression monkey" but be sure to check any prerequisites for graduate econ classes.  Again, the assumed level of mathematical preparation is typically much higher for doctoral students in economics.

 

Also, although many programs may not go so deep as measure theory, it's still worth keeping in mind that (forgive the bromide) you could end up losing the forest for the trees. As you've noted before, the structure of the first year in econ programs often differs from that of sociology, where much of the focus for the former is simply on preparing students for a qualifying exam. For most of these exams, the content will skew heavily toward the mathematical details. Here, I would think you would be better off knowing the distributional theory for regression/residual diagnostics (more applied) than knowing Black-Scholes or Karush-Kuhn-Tucker inside and out. It's not an either-or scenario, but I would guess (I don't know for certain) that many programs reserve more of the practice with the relatively applied concepts for third-semester courses in econometrics (or leave that for students to work through independently).

 

My friends' experiences are similar to jacib's, in that they've taken plenty of courses in stats departments, but rarely go into econ. One advantage with the stats coursework is that many of the non-phd-level stats courses are designed as service courses for graduate students in other departments. If I recall correctly, some programs like Michigan, Penn, and Stanford allow doctoral students in other programs to pursue concurrent MS degrees in statistics. A program like that should also be able to get you where you want to be. 

 

I'm really not trying to discourage you from taking an econometrics sequence, but would just remind you that you'll have finite hours each day that you'll also want to devote to soc theory and developing ideas for research. 

 

If you're looking into doing work with corpus linguistics, I would also recommend looking at the course listings in computer science, and maybe starting to familiarize yourself with Python and/or Perl now. R (with which it seems you'll gain some experience) will be useful for your statistical work, but most people who do serious work in NLP end up doing quite a bit with scripting languages.

Edited January 9, 2013 by health_quant

[gilbertrollins]

I don't know what I'm getting myself into, really.  I left economics precisely because I want to do a good deal of theory and do not like writing deductive proofs.  Some of that is based in the lack of motivation to learn various theories of optimization and games -- as these are done to death and for a variety of reasons not particularly useful to questions i want to ask.  Some of it is I think based in the usual math phobia and a lack of taste for it.  

 

On the other hand, complex adaptive systems pose I think the most compelling compromise between agency and structure to hit social science in decades, network theory is incredible and advancing, and like you said NLP is becoming a viable way to measure culture directly.  

 

Do I want to write theorems about semidirected graphs, and code Python all day?  Hardly.  Maybe it'll change when I get further away from econ, where the mathematics in a lot of ways feels like a rat race and signaling game, more than a collection of "tools" as it's presented.  Frankly a lot of people applying comp sci to natural language processing and text analysis, building agent based computational simulations, proving propositions about graphs, and so on are just fascinated with the whiz-bang technicality of the exercise itself.  I don't get those kicks, personally.  And that's not to knock it at all.  I'm friends with a lot of these guys and gals, and we need people like them.  

 

I'm thinking long run my comparative advantage will land somewhere in qual theory in combo with applied networks and stats to do empirical work, but not sure yet.  I've been waiting for two years to wake up and love math and deductive logic.  Hasn't happened yet.

[gilbertrollins]

However, many undergrad econometrics and grad-level quant soc classes are hamstrung by the students' lack of exposure to probability and advanced statistics. Your background and current approach (hats off for using an engineering stats book) seem like they should prepare you well for being more than a "regression monkey" but be sure to check any prerequisites for graduate econ classes.  Again, the assumed level of mathematical preparation is typically much higher for doctoral students in economics.

 

Also, although many programs may not go so deep as measure theory, it's still worth keeping in mind that (forgive the bromide) you could end up losing the forest for the trees. As you've noted before, the structure of the first year in econ programs often differs from that of sociology, where much of the focus for the former is simply on preparing students for a qualifying exam. For most of these exams, the content will skew heavily toward the mathematical details. Here, I would think you would be better off knowing the distributional theory for regression/residual diagnostics (more applied) than knowing Black-Scholes or Karush-Kuhn-Tucker inside and out. It's not an either-or scenario, but I would guess (I don't know for certain) that many programs reserve more of the practice with the relatively applied concepts for third-semester courses in econometrics (or leave that for students to work through independently).

Any of the people I know whom are actually prepared for T10 econ programs have taken several semesters of analysis, calculus of variations, and usually a handful of graduate micro theory and metrics courses already.  It's turned into a situation much like that in physics, engineering, or mathematics, where if you don't start tracking toward graduate degrees by your Junior year (if not sooner) -- you're going to have a hard time cracking T20.

 

The applied stats courses I'm taking in the stats department aren't proof heavy.  In fact the one I took two semesters ago only did a little bit of verifying some equalities of expectation, derived a couple estimators using calculus (taking partials and equating to zero -- tough stuff!).  The one next semester gets to multivariate regression (which I've covered in an applied setting in econometrics, but in a really crappy way -- like "look at the Excel plot for heteroskedasticity and serial correlation" stuff) but I don't think we even do method of moments.  

 

This is sort of a T20 standard econometrics text: http://www.amazon.com/Econometric-Analysis-7th-William-Greene/dp/0131395386

 

Pretty sure most programs don't get to look at a bunch of applied papers until their field courses (development, labor, industrial organization, environmental, etc) in year two.  And I don't think there's a lot of hand holding on how to use the packages -- these kids will be using MatLab and Mathematica in theory courses.  

Edited January 9, 2013 by econosocio

[magicunicorn]

First year seems much more mentally challenging that academically challenging.  I spent the winter break basically hibernating after my first semester.  

 

At my program, you'd probably get an eyebrow raise (in a bad way) if you asked to go into the econ department for econometrics instead of our quant. sequence.  Part of it is that the sociology department is responsible for your training.. when you graduate, you represent the department -- and lets say you missed something completely basic in linear regression that wasn't covered in the econ department..  and showed up at another campus for a job talk and everyone was like "omg, so and so doesn't know OLS regression" -- your department looks bad.  My department makes everyone take the methods sequence courses regardless of your background so they can ensure the quality of training everyone gets, and that its consistent for all of us.

 

I think the biggest pressure in graduate school, especially at a T10 school is the constant push to "make your mark" and to get publications out.  This is compounded by the fact that you are surrounded by superstar professors who were able to do just that, and do it well.. so you constantly worry you aren't going to measure up.

 

So I don't think its the work load or whether or not you should be reading up more on theory right now -- my advice is to just enjoy life at the moment since the "work" and "pressure" that comes in graduate school isn't something you can really ameliorate with advance prep.  Rather, build up a support network and do as much as you can now to rest up so when things really start slamming you around in graduate school, you are mentally up for the challenges.

 

They constantly tell us that graduate school is a marathon, not a sprint.. not to burn out too early or too fast, but rather pace yourself and keep your eyes on the larger picture..  its great advice.

[faculty]

While not directly related to your questions here, there was a thread on first year courses last year where I posted some thoughts that might be helpful here:

 

Don't underestimate the challenge of the first year in sociology. Comparing it to economics is apples to oranges. Yes, economics is brutal, but there are benefits to having a first year that is centered almost exclusively on studying a rather finite amount of material for an exam rather than a first year that is mean to open you up to an entire discipline and begin professionalizing you for academia immediately. It can be tremendously overwhelming, conjuring up all this uncertainty that others have alluded to.

 

I agree that it's fine to ask to test out of the first stats course. This is fairly common practice and the first course is usually referred to as "baby stats" or "intro stats" and is seen as a prerequisite to the actual sequence that starts with the subsequent course. However, I also agree that it would likely be frowned upon to take classes in econ instead. Departments generally prefer that student take classes in the department unless it's something that the department doesn't offer and is something that would enhance a students sociological work. Testing out will mean that you'll get an additional substantive course, keeping you busier with reading and writing than most other first years.

 

You'll take classical sociological theory that first year, but a number of departments offer a contemporary theory that you can take later that will look quite different. There is formal theory in sociology, although I don't know how much of it you'd learn even in the contemporary class. I see it as more common in social psychology - reflected well in Stanford's program - than anywhere else. You should get a bit of symbolic interaction in standard theory courses (either classic or contemporary, depending on the program - I know that my first theory class was just Marx, Durkheim, Weber, and Simmel).

 

Like previous posters have said, the first year varies a lot by department. In my experience, though, it varies even more by student, as it's ultimately what students are willing (and able) to put in to that first year than influences how hard - and fulfilling, productive, etc. - it will be.