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? 

So, I recently shared a post detailing how to use the Cha-Cha Slide in your Intro Stats class. Today? Today, I will provide you with an example of how to use Kim Kardashian to explain test validity. So. Kim Kardashian-West stumbled upon a Buzzfeed quiz that will determine if you are more of a Kim Kardashian-West or more of a Chrissy Teigen . She Tweeted about it, see below. https://twitter.com/KimKardashian/status/887881898805952514 And she went and took the test, BUT SHE DIDN'T SCORE AS A KIM!! SHE SCORED AS A CHRISSY! See below. https://twitter.com/KimKardashian/status/887882791488061441 So, this test purports to assess one's Kim Kardashian-West-ness or one's Chrissy Teigan-ness. And it failed to measure what it claimed to measure as Kim didn't score as a Kim. So, not a valid measure. No word on how Chrissy scored. And if you are in you teach people in their 30s, you could always use this example of the time Garbage's Shirley Manson...

I think this blog just peaked. Why? I'm giving you a way to use the Cha-Cha-Slide ("Everybody clap your hands!") as a tool to teach basic descriptive statistics. Here is a list of the most frequently banned-from-wedding songs: Most Intro Stats teachers could use this within the first week of class, to describe rank order data, interval data, qualitative data, quantitative data, the author's choice of percentage frequency data instead of straight frequency. Additionally, Hickey, writing for fivethirtyeight , surveyed two dozen wedding DJs about banned songs at 200 weddings. So, you can chat about research methodology as well.  Finally, as a Pennsylvanian, it makes me so sad that people ban the Chicken Dance! How can you possibly dislike the Chicken Dance enough to ban it? Is this a class thing? 

In my stats classes, we talk about the replication crisis. When introducing the topic, I use this  reading from NOBA . I think it is also important for my students to think about how science could create an environment where replication is more valued. And the Dutch Organization for Scientific Research has come up with a solution: It is providing grants to nine groups to either 1) replicate famous findings or 2) reanalyze famous findings. This piece from Science details their effort s. The Dutch Organization for Scientific Research provides more details on the grant recipients , which include several researchers replicating psychology findings: How to use in class: Again, talk about the replication crisis. Ask you students to generate ways to make replication more valued. Then, give them a bit of faith in psychology/science by sharing this information on how science is on it. From a broader view, this could introduce the idea of grants to your undergraduates or get yo...

This NPR story is about reproducibility in science that ISN'T psychology, the limitations of expert intuition, and the story is a summary of a recent research article from PLOS Biology  (so open science that isn't psychology, too!). Thrust of the story: Cancer researchers may be having a similar problem to psychologists in terms of replication.  I've blogged this issue before. In particular, concerns with replication in cancer research, possibly due to the variability with which lab rats are housed and fed . So, this story is about a study in which 200 cancer researchers, post-docs, and graduate students took a look at six pre-registered cancer stud y replications and guessed which studies would successfully replicate. And the participants systematically overestimated the likelihood of replication. However, researchers with high h-indices, were more accurate that the general sample. I wonder if the high h-indicies uncover super-experts or super-researchers who have be...