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

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

 

In a study comparing blood concentrations of caffeine after coffee or energy drink consumption, blood caffeine levels peaked at about 60 minutes in all conditions.

Plan accordingly.https://t.co/cWWakGGtHe pic.twitter.com/c5Nn3x3w1f

— Kevin Bass (@kevinnbass) November 30, 2021

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.