The_Old_Wise_One

1) I don't know about everyone else, but my current PI asked me when I was interviewing–"What would you want to work on for your first year project?". I was not really expecting this and so it caught me off guard. I would recommend thinking about specific real-world projects/interests that you would want to work on with your POI; when they ask WHY you want to work on this project, don't just say "to help people". Give them a real reason for the importance of your topic of interest. 
2) If they are having you interview with multiple faculty members, make sure to get some understanding of what they work on. This can help a lot when interviewing. Also, other faculty members are oftentimes interested in hearing you defend why your research interests are important, so be prepared (like in #1 above) to talk about this. 

FYI–The office suite (Word, PowerPoint, Excel) runs great on OSX.

1) I don't know about everyone else, but my current PI asked me when I was interviewing–"What would you want to work on for your first year project?". I was not really expecting this and so it caught me off guard. I would recommend thinking about specific real-world projects/interests that you would want to work on with your POI; when they ask WHY you want to work on this project, don't just say "to help people". Give them a real reason for the importance of your topic of interest. 
2) If they are having you interview with multiple faculty members, make sure to get some understanding of what they work on. This can help a lot when interviewing. Also, other faculty members are oftentimes interested in hearing you defend why your research interests are important, so be prepared (like in #1 above) to talk about this. 

1) I don't know about everyone else, but my current PI asked me when I was interviewing–"What would you want to work on for your first year project?". I was not really expecting this and so it caught me off guard. I would recommend thinking about specific real-world projects/interests that you would want to work on with your POI; when they ask WHY you want to work on this project, don't just say "to help people". Give them a real reason for the importance of your topic of interest. 
2) If they are having you interview with multiple faculty members, make sure to get some understanding of what they work on. This can help a lot when interviewing. Also, other faculty members are oftentimes interested in hearing you defend why your research interests are important, so be prepared (like in #1 above) to talk about this. 

I agree that being cordial should be preferred, but only so long as it is effective. History shows us that cordiality in academia – when it comes to pointing out flaws in methodology – almost always leads nowhere. Academics engrossed in methodology write books, opinion articles, etc., and yet hardly anyone in the field bats an eye. Gelman makes an excellent point of this when he brings up Meehle's criticisms of social sciences. 
The major difference between people like Paul Meehle versus someone like Gelman is that Meehle never made it personal. In other words, he never said "X person did Y thing wrong". Obviously, Gelman is doing just that and it is causing some friction. However, this is exactly what science needs right now. What better way is there to create change? Since individual people are being criticized, they must now defend their reasoning. If they cannot defend their reasoning, then they are doing bad science. If they cannot admit to doing bad science, then they are obviously not trying to learn from their mistakes; learning from mistakes is an absolute in science – there is no debate on that.
All being said, if the reputations/careers of researchers – that refuse to admit and learn from their wrongs – are tarnished, what is the problem? Would we prefer that they continue on?   

1) I don't know about everyone else, but my current PI asked me when I was interviewing–"What would you want to work on for your first year project?". I was not really expecting this and so it caught me off guard. I would recommend thinking about specific real-world projects/interests that you would want to work on with your POI; when they ask WHY you want to work on this project, don't just say "to help people". Give them a real reason for the importance of your topic of interest. 
2) If they are having you interview with multiple faculty members, make sure to get some understanding of what they work on. This can help a lot when interviewing. Also, other faculty members are oftentimes interested in hearing you defend why your research interests are important, so be prepared (like in #1 above) to talk about this. 

Sorry about that. I thought measurement and quantitative psychology were similar, as I have come across a lot of personality psychologists that call themselves quantitative psychologists, and they do not create novel methods. I'll delete my post.

Perhaps I should have been more clear in my original post. I don't view myself as the next person to come up with Cohnen's d, but maybe that has more to do with confidence. I've actually done a fair amount of multilevel modeling for repeated measures data. It was the most enjoyable aspect of my research experience. In addition, I would look into apply item-response theory to personality measures and scales. I think people are getting hung up on my comment about not wanting to create my models. It was more of a misunderstanding in how I wrote it.
I agree that a strong math background would make me a better applicant. Thankfully, my Master's program is very flexible so I'm pursuing several courses in the statistics graduate program. Also, I am going to do the independent study on R.
As for looking into a measurement PhD in Education Psychology. I did investigate that area, but I get hung up on the research interests. I'm not particularly drawn to what they are researching.

Hello! Sure, I would be happy to help. I am a 3rd-year PhD student (graduating within a few months, actually) and I work both in a Quant Psych lab and an Educational Measurement one, so I tend to see how things are done in both types of programs. Honestly believe you may benefit by looking a little into Educational Measurement programs as well.
Lemme address each one of your questions one-by-one and I’ll post everything in the forum so other people can benefit from them. I kind of feel like Quant Psych is “psychology’s best public secret” because, although it is a very interesting and fun area to be in (with lots of opportunities for growth and employment both inside and outside academia) there is little info about this area out there and its number of PhD graduates still remains quite low (at least per stats in North American programs). The Crisis of Replicability has been shining the spotlight on us a little bit (particularly because we, as methodologists/statisticians, have been forecasting this crisis for about 30yrs or more) so let’s see how things change in the future. In any case….
My big question to you, is what can I do to improve my application beyond the basic have a good GRE, good GPA, etc.? I am not applying for programs until the Fall of 2017.
 
For this question the advice is somewhat standard. Find a lab where you can gain research experience and volunteer. Ideally, a Quant Psych lab would be the best one so you can directly look into what goes in the daily research life of people in these types of programs. Quantitative Psychology can also be very mathematical so it wouldn’t hurt if you have taken Mathematics/Statistics classes outside of Psychology. I place emphasis on outside because, in my experience, courses in research methods/statistics for social scientists are a tad bit skimpy on the theory behind the methods and you want to learn how to do these things beyond the “cookbook” level. I mean, it’s not super necessary but it’s gonna look good on your application.
I spoke with a Quantitative professor that offered to teach me R in an independent study. Is that a good idea?
 
It’s more than a good idea… I’d say you’re probably gonna be expected to know some R, SAS, STATA or some other programming environment by the time to apply. But R is very powerful and popular so I would place the bulk of my efforts on learning R. I mean, you can apply without knowing any of this but then you’re gonna be stuck with both having to learn how to program while taking classes, undergoing research, etc. You’re also not going to look as good on your application package when compared with people who already know R. At this level, SPSS is just not gonna cut it anymore so don’t forget everything you know but be prepared to rarely use SPSS ever again. I think I haven’t used SPSS in more like 2 or 3 years? Everything I do is in R. So yes, the faster you can learn R, the better.
The other software I would recommend you to become familiar with is MPLUS because that is the default now on latent variable modeling. R can do a lot of what MPLUS can, but people just use it a lot so knowing MPLUS syntax will let you communicate with other people who don’t use R. It wouldn’t hurt you to learn about other programming languages and have some idea of how to do database management (SQL) or data-visualization (Tableau), but this is really not as necessary.
The one thing that you *should* start becoming familiar with is how to code Monte Carlo simulations. Your research as a Quant Psych person happens primarily inside the computer and simulations are our bread and butter. You are gonna end up running A LOT of those so try to become familiar with the basic structure of for() and while() loops, how to optimize computer time and (if you use R) the family of apply() functions. A book I recommend first year students to get themselves started with is this one:
https://us.sagepub.com/en-us/nam/monte-carlo-simulation-and-resampling-methods-for-social-science/book241131
so if you can at least start going through some of the chapters and try to reproduce some of the examples it would place you ahead of most people by the time you begin.
I’ll admit I don’t have a calculus background beyond Calc I, but I do have a course on multivariate statistics. Do you think this will be a problem?
 
Not really, but then again it depends on what your ultimate goal is. Most people (like yourself) find this field by accident so almost everyone who applies starts off without a strong base of Mathematics/Statistics. You will learn most of what you need as you go but the downside is that (a) you will only know things at an “intuitive” level that does not necessarily generalize to the wider types of data you will encounter and (b) you may not be able to read and use the literature produced by and for Quantitative Psychologists. The primary journal of our field is Psychometrika. It is the most prestigious place to publish and what most people aim for. But, to be honest, without at least some notions of calculus, linear algebra and mathematical statistics most people can’t make it past the first two pages or so of any given article. So, would this be a problem to get in? No, I don’t think so. But it can end up becoming a problem in the future.
As a curious side-note, I did my BSc in Mathematics with some hints of Psychology and I found it somewhat peculiar that, when I was having my interview, my advisor had highlighted all the Math courses I took and basically ignored anything related to Psychology. The interview also went into that direction and I feel the reason for that is because very few people with background in Math/Stats/CompSci etc. wander into Quant Psych so whenever that happens, advisors are very happy to snatch you from the get-go. Your application does stand over other people’s if you can palpably demonstrate some sort of technical expertise (where technical means theoretical math or an ability to code).
Is there anything that applicants say/do that is specifically a deal breaker in quant programs?
 
Uhm… not that I’m aware of? Just make sure you don’t show your preference for Bayesian statistics in front of a frequentist professor (<--- HOHOHOHO I’m so clever… Am I not? Anyone? OK, I’ll let myself out now… :D). Although this may tie in with another question of yours which is…
I am not particularly interested in creating new statistical methods myself. I am more interested in tackling other people's data and looking into multi-level modeling. Is that a problem?
 
For the most part, yes. This will become a problem for you sooner or later. And the reason it will become a problem is because you’re aiming at doing the most basic implementation (i.e. data analysis) of what Quantitative Psychology has to offer. It is also a problem because, in reality, any skilled social/ clinical/personality/insert-your-area-of-choice psychologist can do the same thing. As a Quant Psych your selling point is something like “not only can I do data analysis. I can do data analysis, I can create new methods for data analysis and I can evaluate data analysis methods”. If you stop at the “I just want to do data analysis” well, that’s not gonna get you very far. And that is something that in my opinion (and from talking to other people in my area in conferences and whatnot) would be a deal-breaker if you’re trying to get into a program. I mean, think about it… from the get-go, you’re already signalling that you’re not interested in doing what most of us in the field are doing so the immediate question that pops up is “is this person even supposed to be here?”.
If you’re mostly focused on data analysis over research on statistic and theoretical psychometrics then I would encourage you to apply into a more substantive program (social/clinical/personality/etc.) and just either do a minor in Quant Psych or take as many statistics/methods courses as you can. The fact of the matter is that a Quant Psych PhD program looks more like a watered-down Statistics PhD program (with a few exceptions, Ohio State comes to mind) than a Psychology program. You’ll find out soon enough that most of your research happens inside the digital bowels of a computer and not so much going out in the field and talking to real people. I mean, you do some of that but that’s definitely not what your training as a Quant Psych will do for you.
Is a Quantitative psychology PhD program a good place for someone particularly interested in measurement of personality and psychological disorders?
 
You can do that but if what you are really looking for is the measurement aspect of things and not necessarily the statistical aspect, a program in Educational Measurement might be a better fit for you. In my assessment, Quantitative Psychology programs are more programs in Statistics with some Psychometrics thrown into them, whereas Educational Measurement programs are more programs in Psychometrics with some Statistics thrown into the mix. I do find that Educational Measurement programs tackle some interesting aspects of scale construction and development (like how to create norms, psychometrically-sound ways to score tests, etc.) that do not necessarily make it into Quant Psych. And the reason behind this is Item Response Theory, IRT. Educational Measurement programs have been, for the most part, the bastion of IRT because the sample sizes you need to run these models accurately can easily go into the 1000s. And, at least form my experience, your standard Psychology research sample size is somewhere in the low 100s. Plus Educational Measurement programs place heavy emphasis on what happens outside the context of data analysis (The Standards for Educational and Psychological Testing will become your new go-to book for everything) whereas I  feel like Quant Psych programs place a lot more emphasis on the data analysis part itself.
 
 
Now, again, this is just a wide scope, generalization of how Quant Psych and Educational Measurement works. I’m sure if you look hard enough, you may find the one program with that one faculty member that does exactly what you want to do. But from the type of questions that you’re asking, I’m wondering whether Quant Psych is actually the right fit for you and if you may be better off in another program and just being very studious with your methods. Or perhaps an Educational Measurement program, have you looked into those? I feel the faculty in those programs is a little bit more diverse as far as research interests go. Another thing I would recommend you to do is to grab maybe some of the high impact journals in the field and have a look at what kind of research they publish. When you have the time, look into these 3 journals: Psychometrika, Multivariate Behavioural Research and the British Journal of Mathematical and Statistical Psychology. Go through the most recent issues, see the kind of stuff that gets published there and ask yourself: “is this the kind of research I would like to do for the rest of my professional career?” if your answer is “yes”, then Quant Psych is definitely your field. If your answer is “no” then… well, I think looking at other options might be worthwhile.

I agree that being cordial should be preferred, but only so long as it is effective. History shows us that cordiality in academia – when it comes to pointing out flaws in methodology – almost always leads nowhere. Academics engrossed in methodology write books, opinion articles, etc., and yet hardly anyone in the field bats an eye. Gelman makes an excellent point of this when he brings up Meehle's criticisms of social sciences. 
The major difference between people like Paul Meehle versus someone like Gelman is that Meehle never made it personal. In other words, he never said "X person did Y thing wrong". Obviously, Gelman is doing just that and it is causing some friction. However, this is exactly what science needs right now. What better way is there to create change? Since individual people are being criticized, they must now defend their reasoning. If they cannot defend their reasoning, then they are doing bad science. If they cannot admit to doing bad science, then they are obviously not trying to learn from their mistakes; learning from mistakes is an absolute in science – there is no debate on that.
All being said, if the reputations/careers of researchers – that refuse to admit and learn from their wrongs – are tarnished, what is the problem? Would we prefer that they continue on?   

This is a great read. Just to clear things up: "replication guru" aren't really the right words to describe Andrew Gelman. He is one the (if not THE) leading minds in statistics. People who go to his talks literally ask for his autograph – he is just that good at what he does.
That said, he has a big problem with a lot of the things people do to leverage statistical testing in a way that favors their own theories, and his blog describes these things.
This is a problem with people doing bad science, not a political "I don't like you so I'll write a blog post about you" cat fight. The take away for me is – choose an advisor who keeps up with current methods.

This is a great read. Just to clear things up: "replication guru" aren't really the right words to describe Andrew Gelman. He is one the (if not THE) leading minds in statistics. People who go to his talks literally ask for his autograph – he is just that good at what he does.
That said, he has a big problem with a lot of the things people do to leverage statistical testing in a way that favors their own theories, and his blog describes these things.
This is a problem with people doing bad science, not a political "I don't like you so I'll write a blog post about you" cat fight. The take away for me is – choose an advisor who keeps up with current methods.

Gelman's reputation is far from tarnished. In fact, he is a hero in many people's mind for coming out and telling researchers that they are abusing statistical methods in order to perpetuate their own theories. The only people who don't appreciate what Gelman is doing for science are people who have not thought critically about the effects that bad methods have on society, and also those who refuse to admit that they are wrong. 
Comparing this with Trump is absurd. First off, it isn't a minority of people that are taking these issues seriously, it's a large number of people across every field. Second, Gelman has absolutely nothing to gain from doing so this; he is doing it because he wants to see people do better science. Others have tried in the past, and they have failed because they do not take a direct approach. 

I agree with you that it is important for people who do study methodology to create tools for others to use – but if you are using this argument against Gelman, you must not know how much he has contributed to the scientific community. He had published numerous textbooks on methodology, and he has also created state of the art software (Stan) for people to do Bayesian statistics.
That being said, most of his criticism is not on the methods themselves (e.g. ANOVA, regression, etc.) but instead he criticizes how people use and interpret these methods and their results. In other words, I can have an idea, design a study, collect multiple types of data, and then test every variable for the effect I want and when I find something significant – I can write it up as if that was my hypothesized finding all along. This will always lead to spurious results, and everyone knows it.  
"These new requirements" are not new in any temporal sense of the word, but they are "new" because people did not ask questions about significance in the past. As scientists responsible for creating knowledge for the world, it is our responsibility to think critically about the methods used to justify our claims – that's it. That is the whole idea that Gelman is trying to get across.
The problem is that nobody has been listening. People in high profile positions continue to publish research conducted using bad methodology, and they continue to train new scientists to do the same. Is that the kind of world you want to live in? One where you cannot even trust science? 
At this point, expressing ideas in the open for all to see is the best way to create a conversation about the changes that need to be made in science. It allows everyone to join the conversation, not just high profile researchers protected by their friends on the editor boards of journals.

I second this. My advisor kicks up a lot of dirt publishing methods papers and bringing up issues not everyone wants to talk about. I respect her for that and I would choose to work with her again for her focus on methodological issues and doing research properly.
There's a real shift in the way people are thinking about science and doing science, and there are people who are not adapting to these new requirements. Is it politics to call them out on it?
Reputation in academia is one of those things everyone likes to talk about, but no one can really defend when it comes down to it. It's a very vague notion built on the assumption that people have to like you to cite your work, or if everyone likes everyone else that we will all be more successful. But that's not true IMO.

This is a great read. Just to clear things up: "replication guru" aren't really the right words to describe Andrew Gelman. He is one the (if not THE) leading minds in statistics. People who go to his talks literally ask for his autograph – he is just that good at what he does.
That said, he has a big problem with a lot of the things people do to leverage statistical testing in a way that favors their own theories, and his blog describes these things.
This is a problem with people doing bad science, not a political "I don't like you so I'll write a blog post about you" cat fight. The take away for me is – choose an advisor who keeps up with current methods.

Ha yes it is a new idea really. It involves the use of some modeling techniques from math psych, but the goal is to create systems to reclassify mental illnesses based on biological, cognitive, and other more objective markers (in comparison to pure symptomology-based diagnoses). 
They are trying to move away from a DSM-like system to a more data-driven one, really useful if you ask me!

Honestly, I do not know much about California but can try to give you some feedback based on what you said. 
For your goals, it doesn't sound like you really need a PhD or psyd, so you could get your degree in a lot less time and start working with an MSW. also, psyd programs at school like Alliant are going to cost a ton and. Degrees from degree mills like Alliant are not really going to carry any sort of prestige (if you are ever going to be doing anything besides clinical practice) and likely won't be substantially more valuable than a far cheaper MSW from a school like UCLA or USC. And if you are interested in long term learning opportunities in psychology, all licensed mental health professionals have to continue learning so the learning doesn't stop at the end of the MSW program.   Phd programs in general, but especially at any UC, are very competitive in clinical psych and if you don't have research aspirations than you likely won't be admitted or be happy in a phd program. For clinical, if you don't need a doctorate to accomplish your goals, the application process alone is probably not worth the headache. I would say MSW would be your best bet, and your might not even have to take the GRE so that's an added bonus. 

Wonderful to hear! Good luck with your future endeavors! I am sure that it will be groundbreaking!

I'm actually starting school this Fall in a clinical program where my PI is very involved in this type of research. I was interested to see if anyone else out there was doing something similar. 
I have found that a lot of people in clinical tend not to take advantage of the techniques used in quantitative and mathematical psychology as well as the advances made within machine learning research. I think that a translational approach to clinical psych could really help alleviate some of the issues we currently encounter in psychological assessment, and I look forward to finding out! 

That is really cool! I'm not too familiar with specific programs that may offer that as it seems kind of niche. It may be worthwhile to look at universities that may have a clinical psychology program, with opportunities to explore quantitative methodologies. For example, in my hometown of Toronto, there is a university that has a clinical psychology program with a quantitative diploma as an add-on for students interested in statistics and mathematics.

What top schools only look at last 2 years' GPA? Every program I applied to wanted my major and cumulative GPA when filling out the application, none even mentioned wanting the last 2 years only.
Adding to that, most schools have a minimum GPA for fellowships, so funding can be an issue when your GPA is below a certain threshold.
That said, if your GPA is low, everything else best be spectacular. Being that OP mentions one not so good LOR, I would be cautious about top tier PhD programs. 

Hello all,
First off, good luck to everyone out there applying for grad programs in psychology. It may be a lot of work, but it will be worth it when it pays off! 
Now, on to the point. I have been lurking around on GradCafe for some time now, but I just recently created this account in order to make some posts and such. After reading through the numerous postings, I have come to realize that a lot of people ask or comment on the GRE, both general and subject versions. One of the common discussions is that the general GRE is a bad predictor of graduate performance, it only measures one's ability to take the GRE, etc. Everyone is quick to demean the test. While these arguments may be valid, there is one thing that I have not seen mentioned––funding. Graduate programs, particularly clinical psychology Ph.D. programs (I say this because I am most familiar with them), oftentimes fund the entire education (up to 5 years) of those who are admitted to the program. Actually, if you check the admissions data given by most schools for clinical psych Ph.D. programs, it is rare to see data showing that a student's education was not 100% funded. Now, where does the funding come from? Well, it comes from a pool of money that the university keeps aside for funding graduate students (makes sense).
What most people may not know is that funding is: (1) an important factor in students deciding which programs to apply to, and (2) spread out across departments. While many people here likely understand #1, I doubt that many know about #2. This means that whether you are seeking admission and funding within a doctoral physics program or doctoral psychology program, the funding money comes from the same pool. That being said, what goes into deciding how much money is offered to each department? This is where things get interesting! Since most doctoral programs cost about the same (within the same university, of course), admissions committees need to come up with ways in which they can compare students across disciplines (it's starting to come together....). As many of you may know, GPA would be a terrible way to compare people from different fields. Is a 3.8 in undergraduate theoretical mathematics equivalent to a 3.8 in undergraduate psychology? Absolutely not! So, what are our options? Well, here is where the general GRE comes in!
The GRE is a baseline used to compare students across disciplines. It is simple as that. It is one of the only metrics that makes this possible. This is why some scholarships and outside funding options for masters programs require general GRE scores. This is why programs that do not fund their students are less likely to require general GRE scores. This is why the general GRE is an important factor when it comes to selecting doctoral candidates! Graduate programs have to compete with one another for a piece of the funding, thus we are all studying and banging our heads against keyboards trying to get a 320+ (and all the while wandering why this stupid test exists!). 
I hope that what I said above gives the community here a bit more insight into why programs require tests like the GRE. The test was created for the sake of making fair comparisons across disciplines, and regardless of how silly it may initially seem, I hope that people can realize how necessary it is. 
Please post comments below for good discussion! Also, post about any insights you may have that could help others understand the application process in more detail. Thanks for taking the time to read  

Hello all,
First off, good luck to everyone out there applying for grad programs in psychology. It may be a lot of work, but it will be worth it when it pays off! 
Now, on to the point. I have been lurking around on GradCafe for some time now, but I just recently created this account in order to make some posts and such. After reading through the numerous postings, I have come to realize that a lot of people ask or comment on the GRE, both general and subject versions. One of the common discussions is that the general GRE is a bad predictor of graduate performance, it only measures one's ability to take the GRE, etc. Everyone is quick to demean the test. While these arguments may be valid, there is one thing that I have not seen mentioned––funding. Graduate programs, particularly clinical psychology Ph.D. programs (I say this because I am most familiar with them), oftentimes fund the entire education (up to 5 years) of those who are admitted to the program. Actually, if you check the admissions data given by most schools for clinical psych Ph.D. programs, it is rare to see data showing that a student's education was not 100% funded. Now, where does the funding come from? Well, it comes from a pool of money that the university keeps aside for funding graduate students (makes sense).
What most people may not know is that funding is: (1) an important factor in students deciding which programs to apply to, and (2) spread out across departments. While many people here likely understand #1, I doubt that many know about #2. This means that whether you are seeking admission and funding within a doctoral physics program or doctoral psychology program, the funding money comes from the same pool. That being said, what goes into deciding how much money is offered to each department? This is where things get interesting! Since most doctoral programs cost about the same (within the same university, of course), admissions committees need to come up with ways in which they can compare students across disciplines (it's starting to come together....). As many of you may know, GPA would be a terrible way to compare people from different fields. Is a 3.8 in undergraduate theoretical mathematics equivalent to a 3.8 in undergraduate psychology? Absolutely not! So, what are our options? Well, here is where the general GRE comes in!
The GRE is a baseline used to compare students across disciplines. It is simple as that. It is one of the only metrics that makes this possible. This is why some scholarships and outside funding options for masters programs require general GRE scores. This is why programs that do not fund their students are less likely to require general GRE scores. This is why the general GRE is an important factor when it comes to selecting doctoral candidates! Graduate programs have to compete with one another for a piece of the funding, thus we are all studying and banging our heads against keyboards trying to get a 320+ (and all the while wandering why this stupid test exists!). 
I hope that what I said above gives the community here a bit more insight into why programs require tests like the GRE. The test was created for the sake of making fair comparisons across disciplines, and regardless of how silly it may initially seem, I hope that people can realize how necessary it is. 
Please post comments below for good discussion! Also, post about any insights you may have that could help others understand the application process in more detail. Thanks for taking the time to read  

Hello all,
First off, good luck to everyone out there applying for grad programs in psychology. It may be a lot of work, but it will be worth it when it pays off! 
Now, on to the point. I have been lurking around on GradCafe for some time now, but I just recently created this account in order to make some posts and such. After reading through the numerous postings, I have come to realize that a lot of people ask or comment on the GRE, both general and subject versions. One of the common discussions is that the general GRE is a bad predictor of graduate performance, it only measures one's ability to take the GRE, etc. Everyone is quick to demean the test. While these arguments may be valid, there is one thing that I have not seen mentioned––funding. Graduate programs, particularly clinical psychology Ph.D. programs (I say this because I am most familiar with them), oftentimes fund the entire education (up to 5 years) of those who are admitted to the program. Actually, if you check the admissions data given by most schools for clinical psych Ph.D. programs, it is rare to see data showing that a student's education was not 100% funded. Now, where does the funding come from? Well, it comes from a pool of money that the university keeps aside for funding graduate students (makes sense).
What most people may not know is that funding is: (1) an important factor in students deciding which programs to apply to, and (2) spread out across departments. While many people here likely understand #1, I doubt that many know about #2. This means that whether you are seeking admission and funding within a doctoral physics program or doctoral psychology program, the funding money comes from the same pool. That being said, what goes into deciding how much money is offered to each department? This is where things get interesting! Since most doctoral programs cost about the same (within the same university, of course), admissions committees need to come up with ways in which they can compare students across disciplines (it's starting to come together....). As many of you may know, GPA would be a terrible way to compare people from different fields. Is a 3.8 in undergraduate theoretical mathematics equivalent to a 3.8 in undergraduate psychology? Absolutely not! So, what are our options? Well, here is where the general GRE comes in!
The GRE is a baseline used to compare students across disciplines. It is simple as that. It is one of the only metrics that makes this possible. This is why some scholarships and outside funding options for masters programs require general GRE scores. This is why programs that do not fund their students are less likely to require general GRE scores. This is why the general GRE is an important factor when it comes to selecting doctoral candidates! Graduate programs have to compete with one another for a piece of the funding, thus we are all studying and banging our heads against keyboards trying to get a 320+ (and all the while wandering why this stupid test exists!). 
I hope that what I said above gives the community here a bit more insight into why programs require tests like the GRE. The test was created for the sake of making fair comparisons across disciplines, and regardless of how silly it may initially seem, I hope that people can realize how necessary it is. 
Please post comments below for good discussion! Also, post about any insights you may have that could help others understand the application process in more detail. Thanks for taking the time to read