I have been thinking a lot lately about how to communicate quantitative results to a wider audience. A big part of the problem lies on concepts that are very specific to the the field (a problem that happens on physics, chemistry, basically any science). So when we talk about means, distributions, t-tests what do we actually mean? And can we explain it to people outside the field with simple words? Well I decided to try! I used the xkcd’s simple writer which checks whether you are using words only on the top 1000 most used English words. I will be updating this glossary as I think of more terms or figure out how to explain them!
As I have only read 7 of these year’s 24 book goal I have decided to be part of #TheReadingQuest that will run from Sunday 13th August to Sunday 10th September, 2017. This is basically like playing a quest videogame. It is hosted by Aentee’s (Read At Midnight) and the art for this quest was made possible by CW of Read, Think, Ponder. Be sure to check it out if you are interested if you need some motivation to read more or you have a huge to read list! If you want to see the character I chose and the books I am reading keep reading! I will also be documenting my reading challenge on this post! If you decide to enter feel free to link to your post on the comments or send me a tweet. See bottom for update and how much I read on that month
As part of my ongoing effort to improve as a researcher and help people understand social science better I am blogging my analytic memos on my recent research trip to Chicago. As my notes were in the form of tweets it was easier for me to build me memo as a storify so here it goes enjoy! 🙂
This post will review a study published in the literature, and follow the steps to replicate the results using an independent samples t-test and calculating the effect size of the difference. The post will focus on the syntax to reproduce this example in R, however the dataset and syntax to run this example in stata will be linked.
I will follow the following steps, the objective is not only to understand how to run an independent samples t-test in R but also to understand why and when run this type of analysis. The steps will be as follows:
- Understanding the claims made in a research paper
- Think through the analysis that one needs to do to support this claims
- Replicate the results using a real dataset in R