Guest post by Meghan Mastroberardino, third year undergraduate student in Psychology at Concordia University
So, you think you might want to have a career in psychology? In North America, most people who end up calling themselves psychologists began as undergraduate students in a Bachelor of Psychology program and have then completed a PhD. I am a third year undergraduate student in Psychology at Concordia University. One of the best ways to get a better picture of grad school is to volunteer in a research lab and take part in research projects. I have found that it has been challenging journey but it’s when I joined the Concordia Infant Research Lab and met my supervisor in my second year, Dr. Krista Byers-Heinlein, that I felt that maybe psychology really was meant for me. I pushed myself and took on as much responsibility as I could in the lab and for the past year, she and I have worked closely together on a large-scale, pre-registered study called ManyBabies1. Continue reading Through the eyes of an undergraduate student: I was part of ManyBabies, an international collaboration project
In the last part of Page‘s R course we will continue learning about LMEMs by using contrast coding and model comparison. We will also extended our use of inline R code in an R Markdown document.
Continue reading R Course: Lesson 6, Part 2
Christina, Page and I like meta-analyses. We are convinced they are a great tool to leverage past research in order to move forward: To gain an overview of the state of a field, to get an idea of research practices, to plan new experiments, and even to get novel theoretical insights.
Continue reading How to run a meta-analysis? A video tutorial
The new year is here, and many of us start off with some resolutions. Following this trend, I thought it would be fun to share some things I’ve been doing and will continue to do that mostly (with the exception of point 3) require only little effort on my side and which positively impact my sciencing and that of those around me. Continue reading 7 small things you can do for science in 2017
Today we’ll learn how to run an ANOVA. We also use the packages tidyr and ez to modify a data frame’s format and run ANOVAs of different types, and as always expanded our knowledge of dplyr and ggplot2 calls.
Continue reading R Course: Lesson 5
Today we’ll learn how to take an old statistics test (logistic regression) but expand it to when you have two variables (multiple regression). The package purrr is introduced and, as always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 4.
Here you can revisit Lesson 0, Lesson 1, Lesson 2, and Lesson 3.
Continue reading R Course: Lesson 4
Following up linear regression, in this lesson we’ll learn the math of logistic regression, and run a logistic regression in R. As always, we’ll expand our knowledge of dplyr and ggplot2.
For full materials, see the course website for Lesson 3.
Here you can revisit Lesson 0, Lesson 1 and Lesson 2.
Continue reading R Course: Lesson 3
Guest post by Page Piccinini
With some basics under our belt from Lesson 1, in this lesson we’ll continue working with dplyr and ggplot2, while also learning about the math behind linear regression and how to implement it in R. Plus you get to finish with a report about how the popularity of your name changes over time.
For full materials, see the course website for Lesson 2.
Here you can revisit Lesson 0 and Lesson 1.
Continue reading R Course: Lesson 2
Our kickstarter project #barbarplots reached its funding goal and will thus become reality! In the 30-day campaign, 173 backers pledged a total of 3,479 Euro to send #barbarplots t-shirts to editors of major scientific journals. We are very excited and want to thank you for the tremendous support – not only by pledging, but also by spreading the word via email, Facebook, Twitter, and by wearing and carrying tote bags and t-shirts with the following meme around the world. Continue reading Bar Bar Hooray!! #barbarplots reached its funding goal
A while ago our resident ExpeRt Page blogged about the disadvantages of bar plots when plotting distributions, accompanied by this summary graphic.
This post not only generated a lot of online reactions (>200 retweets already – for our standards that’s close to breaking the internet!), but also a lot of discussion among colleagues in the lab. And indeed, while plotting might seem like something you just to in addition to the actual analysis, doing it the right way is arguably as important as analyzing the right way since, after all, the figures are the things most of us look at when we try to understand the results of an article.
Continue reading Kickstarting a Plotting Revolution