Guidelines for doing a PhD with me
[Acknowledgements: Many thanks to Professor Mary Beckman
for helping me improve the text.]
I'm always interested in working with motivated and hard working students on PhD projects. I spend
a lot of time with my students on their projects. Because each PhD student is a major investment of time, money, and energy, I thought it would be useful both for prospective students and for me to clarify my expectations. So here they are.
After you start the PhD
Once you start the PhD, you are expected to learn these Big Five skills. You should take care that acquiring these skills takes time, so one has to start on them from day 1. By the end of the second year, you should have made solid progress on each front.
- Writing and communicating results: You will need to be able to write coherently, accurately, and to deliver writing on time (meeting deadlines). You don't have to aim to be a great writer; clarity and coherence are enough.
Grammatical sentences are desirable but if you are not a native speaker, I can relax standards there, initially (by the time you graduate, most of your common errors should be ironed out, however). Reading books on how to write will help a lot. Reading good writing will too.
- Statistical data analysis and programming skills:
- You will need to acquire programming ability in R, as it is needed for (pre-)processing data
- Depending on your PhD topic, you might need to acquire some minimal programming ability in other languages as well.
- You will need to acquire fairly detailed knowledge of statistical theory (see course list below)
- You will need to develop an ability to plot and visualize data
- Experimental and/or computational modeling skills:
- You will need to have acquired knowledge of the design of experiments
- You will need to learn how to implementing experiments from scratch. Whatever method you use, you must aim to be able to do everything from scratch, and you must do the work yourself.
- If you are doing a modeling dissertation, you will need to learn to implement computational models in whatever programming language is appropriate for you, and to evaluate model fit (this presupposes the statistical and experimental knowledge mentioned above).
- Domain knowledge:
You will need to acquire complete control over the literature in the area related to your PhD topic, and in all closely related areas. This knowledge really has to be on your fingertips; you should not have to look up things to know who wrote/said what. You will need to know what the current issues are, what the major problems/questions in the current issues are, and what the current proposed solutions/answers are. After a couple of years of doing a PhD, you should be able to outperform me in the facts department; if you find yourself correcting my understanding of this or that, it's a sign you're doing OK.
- Ability to apply domain knowledge:
You should have learnt to deploy your domain knowledge when analytically thinking about your research problem and writing up your conclusions about your own research.
Formal coursework (helpful in achieving goals 2,3, and 4)
Although no formal coursework is usually required for a PhD at Potsdam, my PhD students have to take these courses for credit
to remain in good standing. You can skip courses if you can demonstrate you already know the material.
Regarding statistical background, you should take (for credit) my Intro to Statistics (Summer), Linear Modeling (Winter), and Advanced Data Analysis (Winter). You must also have at least a passive knowledge of the material taught in the Foundations of Mathematics course. See my home page for course listings.
For methods, take (for credit) the eyetracking course (Winter). For domain knowledge, take any course we teach on psycholinguistics.
How I grade a PhD
I look at the following when I evaluate progress on a PhD and the PhD dissertation itself: Did you acquire the Big Five skills?
That's it. That's all I expect.
Ethical conduct in science and authorship
Outright scientific fraud
and lesser forms of scientific misconduct, including questionable research practices
, remain a serious problem in all areas of science; see here for an ongoing listing
of mistakes and/or fraud. Sexual misconduct/abuse also seems to be a serious problem, as the case at the University of Rochester
, and Kristian Lum
It is very likely that the proportion of researchers actively engaged in deception and these other kinds of misconuct is extremely small.
I imagine it's possible to make mistakes (e.g., not realizing that what one is doing might violate the DFG guidelines); but we must learn from them.
It is our responsibility to make sure that we do not do anything (inadvertently or not) that amounts to misconduct of any kind.
For this reason, before embarking on a career in science, you must read and understand the white paper from the DFG on safeguarding good scientific practice
, and you must make an effort to follow these guidelines and recommendations. It is also helpful to read codes of conduct, such as this one
; these apply to day-to-day academic life as well.
Please also read the authorship guidelines
prepared by my lab. These are based on various official guidelines and are the ones we follow.