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Showing posts with the label qualitative data

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Updated example of using baby names to illustrate bimodal distributions.

The thing about my way of teaching stats is that my cool, novel examples get old really fast. Like, pop culture is on a thirty-day cycle. Anyway, YEARS ago, 2014 to be exact, I blogged about illustrating bimodal data distributions  using baby name data from the Social Security Administration. Specifically, when an older name comes back into vogue, it has a bimodal distribution when you plot time on the X axis.  I stand by this example. However, all of the data illustrations come from 2014. When our traditionally aged college students were 6 or 7. Which makes this example feel a little stale. However, here is some data,  courtesy of Redditor  fevaah ,  showing  old names that are back in vogue through 2025 ! And Eleanor, Violet, Nora, and Hazel are here, illustrating bimodal distributions for a new generation. If you used my old baby-name data, this is a quick, easy refresh for your lectures (or a fun new addition if you didn't).  If this idea was help...
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Hickey's "The Ultimate Playlist Of Banned Wedding Songs"

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? 
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Collin's "America’s most prolific wall punchers, charted"

C ollin gleaned some archival data about ER visits in America from US Consumer Product Safety Commission. For each ER visit, there is a brief description of the reason for the visit. Collin queried punching related injuries. See his Method section below, which describes how he set the parameters for his operationalized variable. With a bit of explaining, you could also describe how Collin took qualitative data (the written description of the injury) and converted it into quantitative data: http://qz.com/582720/americas-most-prolific-wall-punchers-charted/ Then he made some charts. The age of wall punchers is right-skewed. And probably could be used in a Developmental Psychology class to illustrate poor judgment in adolescents as well as the emergence of the prefrontal cortex/executive thinking skills in one's early 20s. http://qz.com/582720/americas-most-prolific-wall-punchers-charted/ The author looked at wall punching by month of the year and uncovered a fairly un...
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