Thriving while PhDing, Part 1: Academic Resources

As I’m wrapping up my PhD, I’ve been reflecting a lot (my alternative is to write my dissertation, so…). How have I gotten where I am? What are the ingredients that have helped me develop a research program on the relationship between metaphor and cognition, to present and publish this work? What wisdom have I absorbed as I’ve woven eleven experiments into a behemoth of a thesis?

I credit much of my own success to many resources that other people have been generous enough to create and share. Here I’ve compiled a list of my favorites — those that provided ideas or skills that I latched onto and others that I wish I had discovered earlier.

General Guides & PhD Advice

  • Philip Guo’s (free!) PhD memoir: It’s awesome to read about someone else’s experience doing a PhD, even though much of the PhD process differs greatly from one person to the next.
  • So long, and thanks for the Ph.D.! by Ronald Azuma: Discussing many of the most important traits for success in a PhD program, including initiative, tenacity, flexibility, interpersonal skills, organization skills, and communication skills. Azuma also includes insights on choosing an advisor and committee, keeping perspective while in grad school, and seeking a job after.
  • Some Modest Advice for Graduate Students by Stephen Stearns: This one has some points and that I especially appreciate now, at the tail end of my PhD, like psychological problems are the biggest barriers and avoid taking lectures — they’re usually inefficient.
  • Ten types of PhD supervisor relationships – which is yours? by Susanna Chamberlain. Every advisor is unique, so might not actually fit into these 10 types, but your relationship with them is crucial for your mental help and academic success. Put in the effort to figure out the dos and don’ts of working with your supervisor, and take managing that relationship very seriously.
  • How to manage your PhD supervisor by Kevin O’Gorman & Robert MacIntosh. This topic is seriously really important. This piece includes concrete recommendations.
  • Five things successful PhD students refuse to do by Dr. Isaiah Hankel. Because what you don’t do is sometimes just as important as what you do.
  • Deliberate Grad School by Andrew Critch. The point is simple, yet something that’s really hard to remember when you’re entrenched in a PhD: “you have to be deliberate to get the most out of a PhD program, rather than passively expecting it to make you into anything in particular.” This article is focused on the ways you can actually make the world a better place while working on your PhD, which I find a really productive way to think about the process.
  • A survival guide to starting and finishing a PhD by Nathan Yau. A post in which the author reflects on what he’d tell his pre-PhD self if he could go back in time. His heartening conclusion: “A PhD can be fun if you let it.”
  • The N=1 guide to grad school (and hopefully, knowledge work) by Adam Marcus, with friends. Some of the advice here applies mainly to computer science, but it’s a thorough and interesting read with plenty more links contained within for those who want to go down the grad school rabbit hole.

Intangibles

  • Academic “older siblings”: These people don’t need to actually be older than you, they just need to have some wisdom and background in your field that you admire. Ideally, they’re not faculty, but are instead grad students or post docs, since they’ll be much more likely to have time to walk you through that new analysis or might be better at identifying with your grad school troubles. My academic older sibs were not in my lab, but our research areas were similar. It was always a morale boost to be able to learn from and emulate people a few steps ahead of me in their academic careers.
  • Talks and questions: Go to as many talks as you can in your first couple of years. Pay attention to the way the speaker frames their topic — what kinds of information are they telling the audience? How do they weave theory and experiments together? How do they present their findings? What kinds of questions do people in the audience ask? This will provide implicit learning opportunities. Before you can do great research, you have to truly internalize what great research in your field is. Reading papers is another way to do so, but I found the in-person observation experiences to be irreplaceable.
  • Contribute to the academic community: It’s important to pull yourself out of your own work and participate in your intellectual community. You can pick up beer for happy hour, cook a dish for the department holiday party, or volunteer more regularly. I spent one year as the grad student rep at faculty meetings, which taught me tons about the dynamics of the department and allowed me to make sure grad student voices were heard when topics of interest to us were discussed. I also spent two years as the larger Cognitive Science Society’s grad student rep, and contributed to the society website and social media, served on a committee to assist scientists who couldn’t come to our annual conference because of the travel ban, and created an event at the conference to offer a professional development opportunity to grad students. It’s important to do things like this because we depend on our departments and societies to support and promote our work, and it sometimes has unexpected personal benefits too, since influential people in your field now know who you are and that you can get stuff done.

Specific Skill Resources

If you’re in a science field, there will probably be technical skills you need to learn or improve for your research. For me, that was mainly programming: I had to figure out efficient ways to implement experiments on the computer, often online, and to analyze the data they generated.

  • Data Science courses from Johns Hopkins on Coursera. I did a handful of these courses, and they were helpful for learning to use R for statistical analyses. A strength of these courses was that they gave a good sense of context, so I could actually apply the principles they discussed to my own data. Importantly, you do not need to pay for these. You can audit every class in the series.
  • R Resources. Dan Mirman’s Cheat Sheet here is extremely helpful. It’s well-organized so that even when you’re not quite sure what function you’re looking for, you have a sense of where on the sheet to look. Once you find the function, the sheet tells you how to use it.
  • How much statistics do psychological scientists need to know? Also, a reading list by Xenia Schmalz. Her answer to “how much statistics…?” is “As much as possible,” which resonates with my experience. I actually just recently found this guide so haven’t taken advantage of many of the resources suggested, but they look great.
  • Statistics Tutorials by Bodo Winter. Linear models and mixed models have become extremely popular in my field, because they allow you to model your data and understand how much variance your factors (as main effects and interactions) explain, while also taking individual participants and stimuli into account. Because they’re so powerful, they’re also a bit complicated to learn, but I’ve returned to Bodo Winter’s tutorials many times because they describe what’s really going on when you use these models and include detailed examples.
  • jsPsych by Josh de Leeuw. jsPsych is a “JavaScript library for creating and running behavioral experiments in a web browser,” which is incredibly useful for making experiments available to a broader audience than the typical participant pool (undergraduates who can participate in person) and for collecting data quickly. There’s thorough documentation, a tutorial for getting started, and a Google group for getting help when you hit snags. I used jsPsych for at least half of the experiments that have made it into my dissertation.
  • Research Digest: Thinking about Statistics by Christopher Madan. A great reading list covering statistics concepts to actually help you understand what all your numbers and analyses mean.

These lists just scratch the surface of resources that have helped me thrive academically while working on my PhD. Please let me know if you have other favorites I should consider adding.

In my next post, I’ll continue to share resources that have been crucial to my success in grad school, but this time I’ll focus on my top personal resources — things that helped me stay healthy, both physically and mentally, and motivated to do my work.

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2 thoughts on “Thriving while PhDing, Part 1: Academic Resources

  1. I just came across your blog today, and as a fourth-year student, I’ve gained a lot of insight into what grad school would hold for me. I love how a lot of the advice you gave can be easily moulded to help students entering undergrad. But at the same time, adding PhD student experiences takes it a level further. And yes, thank you for the stats resources! I hit a major roadblock once because I’d never learned about the softwares our lab used. Stepping up stats skills is a definite aid. I was wondering if you could do a post on what types of softwares are used for specific testing?

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