Below are a list of resources for your own professional developmment. These inlcude professional organization to join as well as other outlets to learn about statistics and research. You are encouraged to engage these communities for both knowledge and inspiration.
Join and Present your Work Here
Association for Psychological Science has been at the forefront of the open science movement. I strongly encourage you become a student member and consider submitting to their conference to present your work. The APS annual conference is typically at the end May with deadlines to submit in January. Graduate student affiliation is about $80.
Western Psychological Association is our regional group where you can freely present your research findings to a very, very friendly and welcoming group of people. WPA’s annual convention is held at the end of April. The poster sessions are, however, sometimes sparcely attended and not very active in giving you feedback. WPA is a good first conference. Submission deadline is usually in November or December. Membership is free for one-year.
Attend, Learn, and Participate
Society for the Improvement of Psychological Science is a conference where open science is the topic of conversation. It’s not your typical conference. At SIPS you are encouraged to join working groups, mingle, and actively participate in projects that further the advancement of psychological science.
R Studio Conference is quite spendy for both registration and pre-conference workshops. However, you’ll learn from the best and brightest from R Studio covering topics from data wrangling to machine learning in R using R Studio.
Use R! was recently in Brisbane, Australia (2018) and will be in Toulouse, France in 2019!. Check out the 2018 site for topics covered.
Statistical Modeling, Causal Inference, and Social Science certainly does not sound like the most interesting blog to read. However, Andrew Gelman’s sass and irreverance make him the #1 methodological terrorist that will keep you reading. You’ll also learn a lot about statistics and, specifically, Bayesian modeling.
Data Colada will make all your false-positive and asterisk/significance chasing dreams come crashing down to terra firma. It will also simply make you a better researcher.
The 20% Statistician is by the author of my favorite coursera course that should be required watching for all of humanity.
Podcasts can be very motivating and fun. It’s nice to hear people talk about the things you learn in class so you know you’re not alone and what you’re learning in meaningful. It’s especially helpful when the podcast creators are fun, genuine, and down-to-earth. There are many data science and reproducibility podcasts but the following are my favorite:
Not So Standard Deviations is fun and light but with enough content to get you excited about the world of statistics and data science.
Partially Derivative is no longer in production but you can’t go wrong listening to folks drink their beer and talk about data, R, and statistics. I’m fond of this episode from season one. I also love Chris Albon’s flashcards; a great way to learn.
DataFramed is a blog on data analysis and data science from the folks at DataCamp. The podcast can be fairly technical but can be very informative and help you think outside the “psychology box.”
