The_Old_Wise_One

The writing score is irrelevant. It does not reflect the nature of scientific writing, and the faculty making decisions know that. I'm at a large state school, and I know that writing scores aren't even used here when making cut off decisions for admission and funding. The verbal, quant, and undergrad GPA are all that matter.

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

What are the classes?

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.

No hard feelings! I just wanted to clear things up. There's nothing worse than delving into a Ph.D. program that you end up hating... I know some people that have gone through this and it is rough

Addressing some comments made by eternallyepmemeral above: (2) Advanced mathematics is definitely required for a Quantitative Psychology Ph.D. Probability theory involves complex integrals (i.e. it requires advanced calculus knowledge), and advanced statistics courses will require knowledge of linear algebra. The statistics courses you will take in a quant psych program will assume you can understand these subjects. (3) Quantitative Psychology programs will expect you to come up with novel methods of analyzing data, so it will be an issue if this is not something you want to do. In fact, the degree will be focussed almost purely on you coming up with novel methods/mathematical models for analyzing psychological data. I say this having managed a Quantitative Psychology lab and worked with many quant pscyh Ph.D. students in the past few years. But you don't need to take my word for it – a search on wikipedia gives this information: "Quantitative psychology is a field of scientific study that focuses on the mathematical modeling, research design and methodology, and statistical analysis of human attributes and psychological processes.[1] Quantitative psychologists research traditional and novel methods of psychometrics, a field of study concerned with the theory and technique of psychological measurement.[2] At a general level, quantitative psychologists help create methods for all psychologists to test their hypotheses." and from the APA website: "Quantitative psychology is the study of methods and techniques for the measurement of human attributes, the statistical and mathematical modeling of psychological processes, the design of research studies and the analysis of psychological data." Looking at these definitions, it is obvious that quant psych programs will be focused on you creating novel methods. I don't mean to come off as rude, but people should refrain from giving advice when they do not have knowledge of a subject matter. Posters on this sub count on the community to give credible advice.

This is very well thought out advice. Quantitative psychology is much like a statistics degree, and to really excel in it you will have to know advanced mathematics (i.e. calculus and linear algebra) as well as have a solid foundation in probability theory. The research consists almost purely of generating new mathematical models to describe cognition, learning, measurement, etc., and it takes quite a lot of time and effort to learn this sort of stuff if you have no prior experience. I am not saying it can't be done, just that you really have to devote all your time to catching up with people who already have the background. It is doable if you love it, as I know someone who was pursuing a developmental psych degree before he switched to quant psych (he had to take calc, linear algebra, and probability theory during one semester just to meet pre-reqs for other classes). As for actually doing the research – you will not be using SPSS in a quant psych program. You will likely be using something like R along with other similar statistical programming languages. The reason for this is that SPSS comes with pre-packaged stat tools, whereas by definition a Quantitative psychology Ph.D. will have you focus on creating new models/methods that have not ben used before. Some above users have said that you will not be creating new methods... this is not true. You will most definitely be pushed to come up with new ways of doing analysis (e.g. creating novel mathematical models), and I would be very surprised if a Ph.D. program would graduate you without you first having done something novel. "Novel methods" does not mean paradigm shifting ideas, but you will at least have to create a variation of some existing model/method and show that it works better than other existing ones. Essentially, I would advice against pursuing a Quantitative Psychology degree unless you want to develop new mathematical models and/or methods of analyzing psychological data. If you just like to analyze data with existing tools, pick a sub-field of psychology that collects data that interests you and keep up with the state-of-the art methods in that particular sub-field. At the end of the day, the most important factor for grad school is that you maintain your interest in your day-to-day activities.

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?

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.

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.

I second what the commenter above stated. Your best best is to wipe the grades clean with a new degree... As unfortunate as that is.

Thank you! I'm hoping for the best

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!

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!