This is a nice, concise hand out from APA that touches on the main points for evaluating research. In particular, research that has been distilled by science reporters. It may be a bit light for a traditional research methods class, but I think it would be good for the research methods section of most psychology electives, especially if your students working through source materials. The article mostly focuses on evaluating for proper sampling techniques. They also have a good list of questions to ask yourself when evaluating research: This also has an implicit lesson of introducing the APA website to psychology undergraduates and the type of information shared at APA.org. (including, but not limited to, this glossary of psychology terms .)

How much do our personalities change when we're drunk? Not as much as we think. We know this due to the self-sacrificing research participants who went to a lab, filled out some scales, got drunk with their friends. For science! Here is the research, as summarized by the first author .  Here  is the original study. This example admittedly panders to undergraduates. But I also think it is an example that will stick in their heads. It provides good examples of: 1) Self-report vs. other-report personality data in research. -Two weeks prior to the drinking portion, participants completed a Big Five personality scale as if they were drunk. So, there is the self-report of Drunk!Participant. And during the drinking session, participants had their Big Five judged by research assistants coding their interactions with friends, allowing a more object judgment of the Drunk!Participant. The findings: https://www.psychologicalscience.org/news/releases/personality-may-change-whe...

Brenner, writing for Distractify , has compiled a very healthy list of terrible, terrible graphs and charts . How to use in class: 1) Once you know how NOT to do something, you know how to do it. 2) Bonus points for pointing out the flaws in these charts...double bonus points for creating new charts that correct the incorrect charts. A few of my favorites:

I think that language is fascinating. Back when I taught developmental, I always liked to teach how babies learn to talk in sort of the same way all across the world. I like regional difference in American English (for example, swearing and regional colloquialisms ). So, I really like this research that investigates the rise and fall of slang in America. And I think it could be used in a statistics class. How to use in class? 1. Funny list of descriptive statistics. 2. Research methodology for using Google searches to answer a question. A good opening for discussion of archival data, data mining, and creating inclusion criteria for research methodology. 3. Using graphs to illustrate trends across time. This feature is interactive. 4. Further interactive features demonstrating how heat maps can be used to demonstrate state-by-state popularity over time. Here, "dank memes" peaked in April 2016 in Montana. 5. The author eye-balled the data can came up ...

http://gazomg.deviantart.com/art/Star-Trek-Redshirt-6-The-Walking-Dead-483111105 io9 recaps a talk given by mathematician  James Grime . He addressed the long running Star Trek joke that the first people to die are the Red Shirts. Using resources that detail the ins and outs of Star Trek, he determined that: This makes for a good example of absolute vs. relative risk. Sure, more red shirts may die, absolutely, but proportionally? They only make up 10% of the deaths. Also, I think this is a funny example of using archival data in order to understand an actual on-going Star Trek joke. For more math/Star Trek links, go to space.com's treatment of the speech.

Pew Research Center  is an excellent source for data to use in statistics and research methods classes. I have blogged about them before (look  under the Label pew-pew! ) and I'm excited to share that Pew is starting up a series of videos dedicated to research methods. The new series will be called Methods 101 . The first describes sampling techniques in which weighing is used to adjust imperfect samples as to better mimic the underlying population. I like that this is a short video that focuses on one specific aspect of polling. I hope that they continue this trend of creating very specific videos covering specific topics. Looking for more videos? Check out Pew's YouTube Channel . Also, I have a video tag for this blog. 3/25/2018 They have posted their second video, this one on proper wording for research questions as to avoid jargon and bias.

This article, written by Matt Daniels  for The Pudding , allows you to play around with a whole bunch of Spotify user data in order to generate visualizations of song popularity over time. You can generate custom visualizations using the very interactive sections on this website. For instance, there is a special visualization that allows you to finally quantify the Biggie/Tupac Rivalry. So, data and pop culture are my two favorite things. I could play with these different interactive pieces all day long. But there are also some specific ways you could use this in class. 1) Generate unique descriptive data for different musicians and then ask you students to create visualizations using the software of your choosing. Below, I've queried Dixie Chicks play data. Students could enter their own favorite artist. Note: They data only runs through 2005. 2) Sampling errors: Here is a description of the methodology used for this data: Is this representative of all data...

I found this video at the Student Problems Page on Facebook . I don't know who to attribute it to, but it was probably a smart, sarcastic Intro Stats student.

As I've discussed in this space before, I think that it is just as important to show our students how to use statistics in real life as it is to show our students how to conduct an ANOVA. The "You Draw It" series from the New York Times provides an interactive, personalized example of using data to prove a point and challenge assumptions. Essentially, this series asks you to predict data trends for various social issues. Then it shows you how the data actually looks. So far, there are three of these features: 1) one that challenges assumptions about Obama's performance as president, 2) one that illustrates the impact of SES on college attendance, and 3) one that illustrates just how bad the opioid crisis has become in our country. Obama Legacy Data This "You Draw It" asks you to predict Obama's performance on a number of measures of success. Below, the dotted yellow line represents my estimate of the national debt under Obama. The blue line shows t...

In my Honors Statistics class, we have days devoted to discussing thorny issues surround statistics. One of these days is dedicated to the disconnect between science and science reporting in popular media. I have blogged about this issue before and use many of these blog posts to guide this discussion: This video by John Oliver is hilarious  and touches on p-hacking in addition to more obvious problems in science reporting, this story from NPR demonstrates what happens when a university's PR department does a poor job of interpreting r esearch results. The Chronicle covered this issue, using the example of mis-shared research claiming that smelling farts can cure cancer (a student favorite), and this piece describes a hoax that one "researcher" pulled in order to demonstrate how quickly the media will pick up and disseminate bad-but-pleasing research to the masses . When my students and I discuss this, we usually try to brain storm about ways to fix this problem. Pro...

You guys, there is a new subreddit just for sharing silly stats memes. It is called r/data_irl/ . The origin story is pretty amusing. I have blogged about the subreddit r/dataisbeautiful  previously. The point of this sub is to share useful and interesting data visualizations. The sub has a hard and fast rule about only posting original content or well-cited, serious content. It is a great sub. But it leaves something to be desired. That something is my deep desire to see stats jokes and memes. On April Fool's Day this year, they got rid of their strict posting rules for a day and the dataisbeautiful crowd provided lots of hilarious stats jokes, like these two I posted on Twitter: The response was so strong, because there are so many of people that love stats memes, that a new sub was started, data_irl JUST TO SHARE SILL STATS GRAPHICS. It feels like coming home to my people. 

A pretty video featuring Billy Barr, a gentleman that has been recording weather day in his corner of Gothic, Colorado for the last 40 years.  Billy Barr This brief video highlights his work. And his data provides evidence of climate change. I like this video because it shows how ANYONE can be a statistician, as long as... They use consistent data collection tools... They are fastidious in their data entry techniques... They are passionate about their research. Who wouldn't be passionate about Colorado?

Let's make the academy better for science and better for our students, and let's make it better for free. Want to learn how? I recommend a Open: The Philosophy and Practices that are Revolutionizing Education and Science , edited by Rajiv Jhangiani and Robert Biswas-Diener. In the spirit of open resources, it is totally free. In the spirit of open pedagogy and quick sharing of teaching ideas, I wrote a chapter for the book about how I've gone about sustaining a blog dedicated to teaching for the last four years . The basic message of my chapter: I blog about teaching, and you can, too!  Here are all the chapters from the book: Introduction to Open Rajiv S. Jhangiani & Robert Biswas-Diener A Brief History of Open Educational Resources M. Smith & T. J. Bliss Open Licensing and Open Education Licensing Policy Cable Green Openness and the Transformation of Education and Schooling David M. Monetti & William G. Huitt What Can OER Do f...