Monday, January 10, 2022

Adam Ruben's How to Read a Scientific Paper

One of the nine stages of reading a scientific paper.
 

Ahahaha. This article by Adam Ruben, writing for Science, makes fun of how difficult it is to read scientific papers. I think your students will enjoy it, especially halfway through their senior thesis/project. It pairs nicely with more traditional guides on reading scientific papers, like the classic Jordan and Zanna piece or this more recent blog post from Dr. Jennifer Raff.

It captures some real gems. Peer review, while imperfect, still scrutinizes research papers like no other form of publication:


It also contains some niche humor your students may not appreciate, but I do.




Wednesday, January 5, 2022

JAMA visual abstracts: A great way to illustrate basic inferential tests

So, the Journal of the American Medical Academy publishes visual abstracts for some of its research articles. I've written about them before (in particular, this example that illustrates an ANOVA). These abstracts succinctly summarize the research. They feel like an infographic but contain all of the main sections of a research paper. I think they are great. They quickly relate the most essential parts of a research study, and I think they have a home in Intro Stats. 

I love them in Psych Stats and use them for a few different reasons.

1. Using medical examples reminds Psych Stats students that Psych Stats is really Stats Stats, and stats are used everywhere.

2. These are real-world examples, simplified. I believe that JAMA creates these in order to help highlight important facts for journalists and the public, so Intro Stats students are more than ready to take these on.

3. I like to use these as a quick review of some of the inferential tests we teach in stats. This is no guarantee that basic stats were used in the project, but most of the posters do report effect sizes, CIs, etc.

Here are a few examples from psychology-adjacent research:

T-test thinking

https://media.jamanetwork.com/news-item/visual-abstract-effect-of-values-affirmation-on-reducing-racial-differences-in-adherence-to-high-blood-pressure-medication/


Factorial ANOVA thinking

https://media.jamanetwork.com/news-item/visual-abstract-outcomes-of-delirium-prevention-program-in-older-adults-after-elective-surgery/

More factorial ANOVA thinking
https://media.jamanetwork.com/news-item/visual-abstract-in-school-screening-to-identify-evaluate-reduce-depression-among-adolescents/

Repeated measure design

https://media.jamanetwork.com/news-item/visual-abstract-effect-of-interventions-for-young-people-with-borderline-personality-disorder/





Thursday, December 16, 2021

Use this caffeine study to teach repeated measure design, ANOVA, etc.

Twitter is my muse. This blog post was inspired by this Tweet: 

 


This study is straightforward to follow. I, personally, think it is psych-friendly because it is about how a drug affects the body. However, it doesn't require much psych theory knowledge to follow this example. Sometimes I'm worried that when we try too many theory-heavy examples in stats class, we're muddying the waters by expecting too much from baby statisticians who are also baby psychologists.

Anyway. Here are some things you can draw out of this example:

1. Factors and levels in ANOVA

The factor and levels are easy to identify for students. They can also relate to these examples. I wonder if they used Bang energy drinks? They are trendy around here. 

2. Within-subject/repeated-measure research design

The within-subject design also makes sense: The researchers used plasma harvests10 times to study how caffeine affects their systems. 

3. Honestly, talk to your students about healthy dosing for caffeine. 

At least one kid in every one of my classes with an iced coffee from Tim Horton's. Every day. Really, they need their coffee an hour before my class. 

4. I like how this data emphasizes mean differences and means and standard deviations. It is helpful to show our students how estimates can overlap in research. 


P-values and effects sizes are great, but I like how the researchers presented their SDs, allowing the reader to see how much overlap there is in these findings.

5. A significant ANOVA with no significant pair-wise comparisons.

This is a thing that can happen when we ANOVA, and it is good to show your example of such a thing.