[Quantitative Psychology PhD]

It is a tad bit broad of a question, LOL. What’s your angle? Would you like to work on some theoretical result within statistics? Would you like to do a Monte Carlo simulation study comparing various methods? Would you like to apply some novel methodology to some interesting dataset? I think you'd need to narrow it down a little bit.

The first one (at least this is what appeals to me most), "work on some theoretical result within statistics". Although to be honesty I have no idea what this would entail however, I may have two years to get his done ! Since I might end up lab manager of my current lab which is a 2 year commitment. Anyways, point being, yes I am most probably starting at below ground zero but I have two years right right? D: lol. Any suggestions?  

[Quantitative Psychology PhD]

@ Spunky, 

I'd like to get your input on how one who's school has no quantitative psychology faculty would go about publishing a quantitative psych paper (maybe this question is too broad but let's see)

[Quantitative Psychology PhD]

Thank you for your insights and comments! I recently mentioned quantitative psychology to my current cog neuro advisor and he essentially said it's a dying field,  and that the quantitative side of psychology is already incorporated in all the psychological disciplines. Although, to be fair, any time I bring a program / potential area of interest to him which isn't the program at my current university, specifically his lab, he sort of attempts to down play it. (I think he's trying to nudge me into applying to grad school at my current university, as a student in his lab lol ) 

I honestly just can't wait until I'm able to apply to quant programs, my days in undergrad go as follows, "why do I have to write so much? why am I taking graduate courses? I just want to do my probability problem sets".  The programs I'm hoping to apply to are UNC - Chapel Hill, UIUC, University of Minnesota, UCLA, Michigan State University, University of Michigan, & USC. Hopefully I can get into at least one of those. 

[Low quant GRE: successes and failures]

i do believe, however, that the bigger issue is how can universities develop a common standard along which they can compare their students. like how do you know that a 4.0 GPA from university A is equivalent to a 4.0 GPA from university B? there is no easy way to go about this from a standardization perspective unless you're willing to step on a lot of peoples toes and get yourself in situations like, i dunno, someone claiming "how can it be that a 4.0 from some crappy state school translates into a 2.0 from Standford U!?!" 

Not sure if this is what you're saying but I think I can relate since I transferred from a community college to my current university, in some subjects courses are about equal in terms of work load and difficulty however in others, they're worlds apart. (particularly in mathematics) 

[Low quant GRE: successes and failures]

Ah, okay. Sorry, I think I was in full-on debate mode and went a little overboard.

It's all cool, I get the same way at times. 

[Low quant GRE: successes and failures]

And what is your point? You have provided an anecdote just as I have. All anecdotes do is indicate what is possible, not probable.

 

Either way, our results say absolutely zip about the utility of the GRE. Only predictive validity can do that.

Lol, I was conceding to your point, should have made that more clear. 

[If you could design an introductory stats course....]

If I could design an intro stats course, I wouldn't have any exams. All assignments would be more like short 1-2 page papers where students would have to work through problems to show their knowledge of the material. Students could re-do these assignments for better grades, provided they've made a significant improvement on the assignment. In a perfect world, students would discuss concepts in class (the professor/ta would lead the lecture/discussion but students would need to have mandatory discussion questions) & then during lab students would apply the concepts from the lectures. 

Maybe a mock up assignment would be, given a data set students would need to analyze the data based on a target goal set by the instructor. On top of accurately applying the concepts, students would need to justify the type of analysis. Why they choose analysis x over analysis y, what information does it provide, what are the drawbacks.  

[Low quant GRE: successes and failures]

GRE success is probably a better indicator of an applicant's SES (a strong predictor of grad school success through no fault of their own) than tenacity. In fact, I have actually heard that very few people manage to improve significantly between testings, which has lead some to advocate preparing and taking the test only once. [actually, ETS aims for this very thing in the name of "accuracy"] It also appears to be the case that you have purposefully ignored my earlier post about comparison to the SAT. I studied quant for 3 months using the exact study schedule you put forward with absolutely no improvement to my score. 

 

There is nothing wrong, in theory, with using a standardized test. However, if the standardized test is not supported by empirical data to be a better predictor of success than other measures, it is a indeed a bad and arbitrary requirement. In fact, such a lack of support defeats the purpose of using a standardized test as a semi-objective measure. 

Maybe I ignored your comments comparing your SAT with GRE scores and the lack of improvement but not purposely, it just wasn't in mind at the time.  I will admit, I'm extremely biased when it comes to arguing for the impact of 'tenacity' or effort, since in terms of SES, and what I'll call here "having a leg up in life" I'm at the very end of the line (by US standards) & I've never been 'good' at test taking.  

[Low quant GRE: successes and failures]

Does anybody defending the GRE actually think it is a useful test or is this thread now just a giant pissing contest of "but I had it worse" stories?

I honestly think it has its use. Although I wouldn't say it's useful in predicting success in graduate school(I actually have no data to back this up) where I would say it's useful is in getting graduate programs  top students. Now, a top student may be someone who has little difficulty on the GRE, took it once and aced it. A top student may also be someone who found it extremely difficult but despite, managed a great score. 

I mean starting a PhD program, it's a pretty big commitment on both parts, the incoming graduate students and the universities. If I were a university I would want something in place that may weed out people (although it may prevent people who'd do great from getting it as well) who didn't do what was necessary, whatever that may be in their case, to make sure their GRE scores wouldn't be a problem when it came to admissions decisions. This is variable however, and sometimes it could simply be the case that it just isn't worth it for person x to sacrifice n amount of something(s) just to get into graduate school but ultimately that's what I think is the purpose of this exam (or at least where I see it's use). Universities would rather be safe than sorry, they have to protect themselves, and I believe it's understandable.   

[Inquiries about other interviews]

Thanks all! I really wish there was a way to reciprocate the question to get a gauge on whether we're going to get in! 

Well, I don't know if it helps but the same faculty said that it was also fair game for you (the potential graduate student) to ask them about their current funding, how many people they have working on current projects going on in their labs, and if they need people on a particular project. These questions may help? 

[Inquiries about other interviews]

3 faculty from my university recently had a talk about this in a sort of informal course where graduate students, RA's, lab managers, and faculty speak on topics in academia. One of those topics was (about two weeks ago) on graduate school admissions. All 3 of them had the same reasoning for why they ask students about other programs they've applied to. It really came down to "are you going to come here?", that's what they want to know when they ask you this question, this is a type of gauge, they want to weight the odds of you accepting an offer at their program when their program might have to compete with school X 

[Low quant GRE: successes and failures]

:raises hand meekly:

I had to literally reteach myself everything.

Everything.

I work in research. I get paid to calculate Cohen's d, eta squared, and Bayesian probabilities by hand. I use R and SPSS to do everything else from CFAs and binary logistic regressions to ANOVAs.

I haven't had a math class with exception to stat since... 2008.

I don't know wtf the square root of -54 divided by 72 to the 5th exponet plus Z cubed is.

Sorry everybody.

 

Sameish, except last non-stats math class was in 2005. It's difficult to cram a bunch of essentially useless math back into your head after 10-15 years. I studied, I'm actually really good at math & had great ACT/SAT scores pre-college, and I'm disappointed in my quant score.

 

Agreed!

I'm also a female over the age of 25. According to the means ETS posts for my demographic, I did a spectacular job.

(Q particularly deteriorates with age, and females score lower on Q and V). According to the expectations of PhD programs, I bombed it :x.

I would honestly like to know how you guys(sorry as I think 2? of you are female) prepare. I only ask because I definitely don't have "natural" talent for mathematics. Coming out of high school I couldn't properly add fractions, (yea, while it was still fresh in my head) and my SAT quantitative score was abysmal, it was around 200. To share what personally worked for me, 1. changing my attitude about mathematics. 2. dedicating an inordinate amount of time to mechanics based practice. 3. (I think this may be the most important) Tackling difficult problems, one which incorporate mechanics but emphasize problem solving (and not giving up right away, thinking through a problem, even if that single problem took me several days).  When I took calculus I went through 3 entire five subject notebooks (not because this much work was given but because I needed this much practice just to get an A) while most of my classmates would just need a single subject notebook. 

Fast forward now to my current discrete math course, I'm out performing most of my classmates which are either applied math majors or computer science majors (and I'm a psychology major) without the need to dedicate that much more time and effort.(Though, it could be argued these applied math majors and CS majors also have a bunch of other math courses, but I would argue my current schedule compares to a typical math major student's schedule) 

Now I know this may seem to go against what I previously stated, as a time period of 3 months probably doesn't compare to the amount of time I put in to studying math,  but this was in regards to mathematics as subject, not a math section on an exam. Anyways, maybe you can also try documenting where you get stuck, and going through those notes to find out where your sticking points are, maybe you'll find interesting patterns?. There are also websites where you can ask a question and receive help almost immediately (yes, free help)

What I'm really trying to get across here is, anyone who's having/has had difficulties with the quant section of the GRE can get through it, and do well on it at the same time. & I mean this more in an encouraging way rather than a "you're just not trying" way. 

[Low quant GRE: successes and failures]

I really hate to be the one to say this but I honestly doubt that anyone who dedicates 2-3 hours each day,  for say a 3 month period to studying and prepping for the quantitative section of the GRE will get anything below 70%.  Unless of course you truly do have a learning deficiency in mathematics, in which case I have no idea what would be needed to improve your score. 

[What are your areas of research? Finding like-minded people...]

I would like to know more about this. A friend of mine has become recently interested in studying linguistics in a sense, but in a different way than you. He wants to look into how word usage might reinforce stereotypes or create more division of groups than intended. 

 

I love the idea of studying online language processing. This is definitely important in this day and age! 

 

It seems like your friend is gravitating towards the Sapir Whorf hypothesis, a "language dictates thought" way of thinking. You should advise him to read the literature regarding this. 

 

I'm interested in online language processing & production! I'm particularly interested in how we use our abstract representations of language and item-specific representations to make predictions in real time about upcoming language stimuli (and relatedly, what happens when these representations clash). Right now I'm looking at this in a sentence processing context where we use abstract constraints but also item-specific knowledge to shape predictions about word order in binomial expressions, but I'm also interested in a variety of other contexts this happens in -- how frequency affects phonetic production, how the use of a particular construction influences our predictions about semantic & pragmatic meaning, how expectations of information density shape expectations about upcoming words, how task relevance changes what representations we use during processing, etc. Fun stuff!!!

In a way, I'm kinda working one level down of you in the whole *language investigation* thing, kinda lol. BTW when you say "how frequency affects phonetic production, what exactly do you mean?   

[Anybody else feel like they were "bullied" into their thesis?]

I'm currently and undergrad senior, my advisor pretty much gave me my topic and project (a part of a larger project he's been working on since his dissertation). I don't necessarily *like* it but I don't dislike it at all, either. I see it more as a larger assignment, similar to what you'd get in any course. Aside from that, there are some pretty interesting questions that can be answered by the work I'm doing. I think the fact that I don't enjoy the topic yet, the fact that I think researching it may provide insights to questions which are still unanswered is what allows me to sort of disconnect from it. In the end this allows me to work on it as if I were a fricken drone. 

[Job interview at Moodys, Tips?]

So I have a job interview for Moodys, at my university.  http://en.wikipedia.org/wiki/Moody%27s_Corporationif anyone is curious.

I've never interviewed for a job like this, and I'm wondering if its necessary to wear a suit? Or will a button up shirt, tie, dress pants, and dress shoes be enough?

Also it should be noted, I'm a psychology major, but I think they selected me for an interview because of my research experience and stats background since the job title is "Associate Analyst III". 

Has anyone here interviewed for this place? If so could you give me any tips? Or just tips from anyone in general would be appreciated. 
 

Thanks! 

[Academic placement rate of UChicago Psychology]

Question, if you'd be an "average" candidate at UChicago, are you saying you wouldn't be an "average" graduate student at a place like Yale, Harvard, Princeton, Stanford, etc? My university turned down people from Harvard and Princeton to hire a faculty member who received his PhD from UMass, and another who received his PhD from the University of Iowa. 

What's going to matter most is your productivity and what you contribute to your field. I'd take someone who changed the game at a community college over some who barely added anything to my field at Harvard , any day. 

Some of these things may often co-occur, that is to say game changers may come from really prestigious universities but the fact that they do doesn't mean that's the reason why the did so well in their research. What you need to worry about most, first is, making sure you're entering into a program/field where you'll obsess over your work. Then making sure you're going somewhere productive to your productivity. Is the lab funded, do you like the PI, do you like the other grad students. THEN all else being equal you may worry about prestige. 

[Favorite books / references for your respective sub-fields]

Hi all, 

So I thought I'd make this thread. Essentially I'm asking anyone/everyone who is willing, to post the information (title/edition/authors) of your favorite books/references for your respective fields/topics of study. (This can include your favorite methods/stats books)

I enjoy the fact that psychology is more or less an all encompassing field & I find a lot of other topics within psychology very interesting(of course some more than others). &.....I NEEDS MOAR NOLEDGE lol.

Anyways I guess I'll start:

Statistical Inference by George Casella & Roger L. Berger
 

Cognitive Neuroscience by Marie T. Banich & Rebecca J. Compton

Awk Programming by Arnold Robbins 

Introduction to Psycholinguistics: Understanding Language Science by Matthew J. Traxier
 

[I just turned down a $118k job...]

Details? 

[Does taking more undergrad classes post-master's look bad to competitive PhD programs? Would it improve your application if it improved your GPA?]

I was going off the assumption that you had already completed a Masters program. Anyhow, you can always apply to joint MD/PhD programs. As according to your most recent response it would seem to fit you best.  Although they are generally more competitive. However there are ways to give yourself a better chance. 

If you are a minority and have not yet started your masters one option could be to NOT pursue a masters and apply to what are called PREP programs funded by the NIH. These are post - bac research education programs. There are some great schools which house these programs and as I've been in told, in person by the director of the program at Yale, they want the individuals they take in from their PREP programs to attend graduate school/med school at Yale. 

Oh and if you have a masters or have began any type of graduate study you're disqualified from applying. 

Also, being white doesn't necessarily disqualify you from participation in the PREP program. What they don't want isn't white people, it's majorities in science, so Jewish, some middle eastern/asian populations. And probably a few others. However there are white minorities in science so that counts. 

website:http://publications.nigms.nih.gov/multimedia/map/prep/

[Question for recent and current PhDs: which skills are most useful in the job market?]

Stats & tech skills will often come hand in hand. There isn't a single sector which is without the need of data analysis. Specifically, someone competent who can extrapolate meaningful information from data.

It is my realization that just about all "professions" or "work like" environments need to do one thing (of course, not just this one thing). That is, they need to sell something and data analysis provides the selling points. 

[Who Has Forgone the PhD Offers for a Master's?]

Can you wait it out to see whether or not you receive a funded offer towards a PhD? That would be my suggestion and if so, for the case that you did receive such an offer I'd say go with the PhD and not UPenn's masters program. Reasons: debt. Especially if you already have undergraduate debt, but, if you don't I'd still say to consider this heavily. 

[Regarding (undergraduate) honors thesis]

Ya, if you're doing six courses at a time including your honours thesis you're definitely not lazy. I only have three classes right now while I'm doing my honours plus TA and RA work and that is more than enough to keep me busy so good on you for being able to handle six. 

Well, I was enrolled in six the previous semester (first half of the senior thesis). This semester I am enrolled in 5, each 3 credits (including senior thesis).

Could you tell me about your thesis? 

[Regarding (undergraduate) honors thesis]

I don't think you're being lazy at all. An honours thesis is easily as much work as an average class if not more but with far less structure and guidance. Although for me at least I have found that the very beginning of the year and the very end are where most of the hard work takes place/will take place (i.e., ethics application and revisions at the beginning and data analysis/manuscript preparation at the end). 

 

Also, are the courses at your university worth 5 credits each? Just wondering how many courses 20 credit hours works out to? Where I go to school that would be 6 or 7 classes as they are 3 credits each and I've seen other schools where classes are 1.5 credits each. 

The courses at my university are the standard (I assume) 3 - 4 credit courses. I had two 4 credit courses and four 3 credit courses.  

 

 

Wow, that's intense. I'm sorry, but I have no advice to offer you on how to go about fixing this, especially if you've already gone about trying to automate the process through programming. Do your advisors/thesis members have any advice on this?

 

Do any of  your sources have similar designs? I've emailed professors before about how to go about scoring and interpreting surveys we've both used. Maybe someone else has created a decent method to cut down on the work required here. Since you're an undergrad, they may take pity. Alternately, they may actually be interested in your results! A professor I just emailed for access to his survey scoring materials expressed that he would be interested in learning the results of my experiment and gladly handed over the information I needed.

My advisor's solution was to hire help, in the form of an RA working for 3 credits. In short, the help quit after finishing 1 set (each set contained approx 1500 responses) & failed to send me her completed work. 

Usually the type of work I'm doing which is, partially, creating a resource for future research, is done on a larger time frame with more than one person working on it. My advisor has told me I've done a significant amount of work & that a write up of the work I've done so far would be sufficient however, there would be no experiment. I'd have simply created a resource & this alone doesn't sit well with me. 

[Regarding (undergraduate) honors thesis]

My sample size is 200, 200 participants gave up to 5 different definitions for different sets of words, each set containing 60 words, with 25 participants working on a particular set. So 8 sets, 25 participants per set & 60 words per set. At the outset I attempted creating a natural language parser using python, so that my work would be cut by some & succeeded but the results were sub par and unreliable.  I can't extrapolate on a portion of the data since each definition is specific to a word and the content of the definition is the valuable information. 

Also, the platform on which participants provided their response was not any type of proprietary software package/website. The website and the task were individually coded and set up on a server on my university.