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Showing posts with the label personality psychology

Johnson & Wilson's The 13 High-Paying Jobs You Don’t Want to Have

This is a lot of I/O and personality a little bit of stats. But it does demonstrate correlation and percentiles, and it is interactive. For this article  from Time, Johnson and Wilson used participant scores on a very popular vocational selection tool, the Holland Inventory (sometimes called the RAISEC), and participant salary information to see if there is a strong relationship between salary and personality-job fit. There is not. How to use in class: -Show your students what a weak correlation looks like when expressed via scatter plot. Seriously. I spend a lot of time looking for examples for teaching statistics. And there are all sorts of significant positive and negative correlation examples out there . But good examples of non-relationships are a lot rarer. -If you teach I/O, this fits nicely into personality-job fit lecture. If you don't teach I/O but are a psychologist, this still applies to your field and may introduce your students to the field of I/O. ...

Wilson's "America’s Mood Map: An Interactive Guide to the United States of Attitude"

Here is a great example of several different topics, featuring an engaging, interactive m ap created by Time magazine AND using data from a Journal of Personality and Social Psycholog y article . Essentially, the authors of the original article gave the Big Five personality scale to folks all over the US. They broke down the results by state. Then Time created an interactive map of the US in order to display the data. http://time.com/7612/americas-mood-map-an-interactive-guide-to-the-united-states-of-attitude/ How to use in class:

Five Lab's Big Five Personality Predictor

Five.com created an app to predict you score on the Big Five by analyzing your FB status updates. five.com's prediction via status update It might be fun to have students use this app to measure their Big Five and then compare those findings to the  youarewhatyoulike.com app ( which I previously discussed on this blog ), which predicts your scores on the Big Five based on what you "Like" on FB. youarewhatyoulike.com's prediction via "Likes" As you can see, my "Likes" indicate that I am calm and relaxed but I am a neurotic status updater (crap...I'm that guy!). By contrasting the two, you could discuss reliability, validity, how such results are affected by social desirability, etc. Furthermore, you could also have your students take the original scale and see how it stacks up to the two FB measures. Note: If you ask your students to do this, they will have to give these apps access to a bunch of their personal informat...

Burr Settles's "On “Geek” Versus “Nerd”"

Settles decided to investigate the difference between being a nerd and being a geek via a pointwise mutual association analysis (using archival data from Twitter). Specifically, he measured the association/closeness between various hashtag descriptors (see below) and the words nerd and geek. Settles provides a nice description of his data collection and analysis on his blog. A good example of archival data use as well as PMA.

University of Cambridge's Facebook Research

University of Cambridge's Psychometric Center has used statistics to make make personality predictions based upon an individual's Facebook "likes" . For instance, your likes can be used to create your Big Five personality trait profile. Your students can have their data FB "likes" analyzed at YouAreWhatYouLike.com  as to determine their Big Five traits. After your students complete the FB version of the scale, you could have your students complete a more traditional paper and pencil version of the inventory and discuss differences/similarities/concurrent validity between the two measures. Below, I've included a screen grab of my FB-derived Big Five rating from YouAreWhatYouLike.com. Note: Yes, that is how I score on more traditional versions of the same scale. Generated at YouAreWhatYouLike.com In addition to Big Five prediction, the researchers also used the "like" data to make predictions of other qualities, like sexual orientatio...