Suicide hotline efficacy data: Assessment, descriptive data, t-tests, correlation, regression examples abound

ASIDE: THIS IS MY 500th POST. PLEASE CLAP.

Efficacy data about a mental health intervention? Yes, please. The example has so much potential in a psych stats classroom. Or an abnormal/clinical classroom, or research methods. Maybe even human factors, because three numbers are easy to remember than 10?

This post was inspired by an NPR story by Rhitu Chatterjee.

It is all about America's mental health emergency hotline's switch from a 10-digit phone number to the much easier-to-remember three digits (988), and the various ways that the government has measured the success of this change.

How to use this (and related material) in class:

1) Assessment.

In the NPR interview, the describe how several markers have improved: Wait times, dropped calls, etc.

Okay, so the NPR story sent me down a rabbit hole of looking for this data so we can use it in class. Here is the federal government's website about 988 and a link to their specific 988 performance data, which is a nice demonstration of simple descriptive data as well as assessment data.

Descriptive data for 988, in a table labeled "988 Lifeine Routed Network Contacts (Excludes VCL*): 371,655

https://www.samhsa.gov/find-help/988/performance-metrics

2) This data is available.

I can imagine many tests you can run looking at the data aggregated by state, found here at 988's website. Here is a screenshot of one of their monthly reports, with the previously mentioned descriptive statistics divided up by state/territory.

A table that displays assessment data divided by state/territory

https://988lifeline.org/wp-content/uploads/2023/01/FINAL-2022-12_988-State-Report-1.pdf

Yes, you need to convert this to a .CSV. Or you can just use this copy of the Dec22 data, in which I also converted all of the times from minutes:seconds to seconds. But after you do that...

Your students can perform a paired t-test on any of the monthly reports. How? Find two months. Compare all of the states/territory performance between the two months. You can have your students replicate the exact data that Chatterjee mentions in her report.

For any given month's data, you could run a correlation and/or regression between any of the quanty variables.

And if you are fancy, you could teach repeated measure design with this data as well, using multiple months' data.

You could practice data visualization with this data as well.

3) Share a fantastic resource with your students. I know I sound dramatic, but you could introduce this resource to a student who really needs it, or loves someone who really needs it.

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr. Molly Metz maintains a curated list of hilarious "How you doing?" scales. 2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables. So, I decided to create a post about three ways to use these scales in your stats classes: 1) Teaching the basics of variables. 2) Nominal vs. ordinal scales. 3) Daily check-in with your students. 1. Teach your students the basics...

Leo DiCaprio Romantic Age Gap Data: UPDATE

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing, I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

Tyler Vigen's Spurious Correlations

Tyler Vigen has has created a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/

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