Friday, August 10, 2018

Talking to your students about operationalizing and validating patient pain.

Patti Neighmond, reporting for NPR, wrote a piece on how how the medical establishment's method for assessing patient pain is evolving. This is a good example of why it can be so tricky to operationalize the abstract. Here, the abstract notion in pain. And the story discusses short comings of the traditional numeric, Wong-Baker pain scale, as well as alternatives or compliments to the pain scale.



No one is villifying the scale, but recent research suggests that what a patient reports and how a medical professional interprets that report are not necessarily the same thing. From Dr. John Markman's unpublished research:


I think this could also be a good example of testing for construct validity. The researcher asked if the pain was tolerable and found out that their numerical scale was NOT detecting intolerable. This is a psychometric issue.

One of the recommendations for better operationalization: Asking a patient how pain effects their ability to perform every day tasks. I don't think this is a new idea (as a patient, I've completed such scales for PT) but it shows a move away from a numeric scale. I think this could be a good example of ecological validity: Pain, being assessed, as it exists in day to day life.

I think that if you use this piece, it would also be worth while to point out the strengths of the Baker-Wong. It seems to be popular in a wide variety for developmental research. Kids do better with faces, it seems. Also, this scale could be useful in a medical emergency with someone who can't speak or can't speak the same language as their care team.

Saturday, August 4, 2018

A curvilinear relationship example that ISN'T Yerkes-Dodson.

I'm such a sucker for beer related statistics examples (1, 2, 3). Here is example 4.

Now, I don't know about the rest of you psychologists who teach statistics, but I ALWAYS show the ol' Yerkes-Dodson's graph when explaining that correlation ONLY detect linear relationships but not curvilinear relationships. You know...moderate arousal leads to peak performance. See below:
http://wikiofscience.wikidot.com/quasiscience:yerkes-dodson-law



https://twitter.com/CNN/status/1024990722028650497



Friday, July 27, 2018

Ben Jones' NFL player descriptive statistics and data distributions.

I think this is a fun questions perfect for that first or second chapter of every intro stats text. The part with data distributions. And it works for either the 1) beginning of the Fall semester and, therefore, football season or 2) beginning of the Spring semester and, therefore, the lead up to the Superbowl.

Anyway, Ben Jones tweeted a few bar chart distributions that illustrate different descriptive statistics for NFL players.

https://twitter.com/DataRemixed/status/1022553248375304193
 He, kindly, provided the answers to his quiz.

How to use in class:
1) Bar graphs!
2) Data distributions, and asking your students to logic their way through the right answers...it makes sense that the data is skewed young, right. Also, it might surprise students to see that very, very high earners in the NFL are outliers among their peers.
3) Distribution shapes: Bimodal because of line backers. Skewed because NFL players run young and have short careers. Normal data for height, because even with the higher than average average, it is still normal.
4) Descriptive statistics are presented as averages in the first Tweet. Is mean the best way to describe this data, especially the skewed data? Why or why not?