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.
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.
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.
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.
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.
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
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.
Guest post by Page Piccinini
In this lesson you will be introduced to the basics of R, including how to read in and manipulate data with dplyr, and how to make figures with ggplot2. You’ll also get experience writing up your results in an R Markdown document. Finally, we’ll put all of that initial set-up to good use by saving everything to Git and pushing it up to Bitbucket for better version control management.
The great preregistration challenge is here, so this is a perfect time to preregister your next study. After all, when do you get the chance to win money for your research? But some might wonder what this pre-thingy is…
Preregistration is a simple, and yet surprisingly novel (as far as I know), idea to ensure that researchers follow the scientific method. In other words, a preregistration means you decide before data collection what (which phenomenon, population) you want to test how (procedure, stimuli, measures, and perhaps most importantly statistics). This is the very definition of testing hypotheses, because commencing data collection should be marking a point of no return when it comes to hypotheses, variables, and statistics. The exception is exploratory work, I will go into detail later on that topic. But back to a typical experiment, the (idealized) lifecycle is illustrated below. Note the arrows going only in one direction and the red line you should definitely not cross between data collection and planning your analyses based on pre-specified hypotheses.