The Hedonometer measures the overall happiness of Tweets on Twitter. It provides a simple, engaging example for  Intro Stats since the data is graphed over time, color-coded for the day of the week, and interactive. I think it could also be a much deeper example for a Research Methods class as the " About " section of the website reads like a journal article methods section, in so much that the Hedonometer creators describe their entire process for rating Tweets. This is what the basic table looks like. You can drill into the data by picking a year or a day of the week to highlight. You can also use the sliding scale along the bottom to specify a time period. The website is also kept very, very up to date, so it is also a very topical resource. Data for white supremacy attack in VA In the pages "About" section, they address many methodological questions your students might raise about this tool. It is a good example for the process researchers go ...

I finally have an example of Spearman's rank correlation to share. This is a political example, looking at how Twitter language usage differs in US counties based upon the proportion of votes that Trump received. This example was created by  Jack Grieves , a linguist who uses archival Twitter data to study how we speak. Previously, I blogged about his work that analyzed what kind of obscenities are used in different zip codes in the US . And he created maps of his findings, and the maps are color coded by the z-score for frequency of each word. So, z-score example. Southerners really like to say "damn". On Twitter, at least. But on to the Spearman's example. More recently, he conducted a similar analysis, this time looking for trends in word usage based on the proportion of votes Trump received in each county in the US. NOTE: The screen shots below don't do justice to the interactive graph. You can cursor over any dot to view the word as well as the cor...

I think this is a good first day of class example. It shows how data can make a powerful argument, that argument can be persuasively illustrated via data visualization, AND, maybe, it is a soft sell of a way to keep your students from drunk driving. It also touches on issues of public health, criminal justice, and health psychology. This article from The Economist  succinctly illustrates the decrease in drunk driving incidents over time using graphs. This article is based on a  working paper  by PhD student Jessica Lynn (name twin!) Peck. https://cdn.static-economist.com/sites/default/files/imagecache/640-width/20170408_WOC328_2.png Also, maybe your students could brainstorm third variables that could explain the change. Also, New Yorkers: What's the deal with Staten Island? Did they outlaw Uber? Love drunk driving?