future_changes

*update to quarto?
* add shiny app for interaction power: https://david-baranger.shinyapps.io/InteractionPoweR_analytic/

lectures overall
* add 60 second break in the middle 
* email students -- share FYP for class?
* more in-class activities (breakout groups like simple slope drawings)
* make a flow chart or organization for material? See example shared by S Lieber

labs overall
* show only broom and kable for creating table summaries throughout
* in lab 9, show them papaja in depth (add links to other options, like stargazer, apaTables, and sjPlot to just create tables in Word)
* incorporate in-text R code to summarize results throughout 
* most useful to teach from R, not from html output
* should we replace papaja with data cleaning (for loops, functions, regular expressions, etc)
* integrate correlation lab with Markdown (to Word/PDF and integrating figures and IN-LINE evaluation)

final-project.html
* add descriptive statistics back in?

1-correlation.html
- trim interpretations of correlations (only effect size, remove vectors)
- other (better?) ways of describing symmetry assumption

2-correlation.html
  
3-regression.html

* prediction is tricky -- can't predict for observation with X out of range. bad idea to use regression models to estimate changes over time. 

4-regression.html
  
2-lab.html

  * this is a bit long.
  * wasn't able to cover stargazer/sjPlot and other formatting code

5-regression.html

  * move gganimate from lab 3 to here 

6-glm.html

  * possible terms to use for model comparison 
    * compact model (simpler)
    * augmented model (more complex, wanted to test)
    * proportional reduction in R^2
  * why is GLM taught before ANOVA?
  
3-lab.html

  * make sure time for finishing minihacks
  * remove matrix algebra mini hack
  
  * labs feeling repetitive
    * could this be a pandemic-specific thing?
    * options
      * move papaja lab here?
      * more applied lab? 
        * here's a question -- how would you test it?
    

7-partial.html

  * arrow from parent ses to peer relations
  * when to use each: only care about X and Y and controlling for variable; comparions between diff x's and incremental validity; network analysis
  
8-m_regression.html
  
  * remove ID variable from dataset
  * no matter where you cut the plane, the angle is the same
  * walk through GIF -- break this up into steps and different slides. May require building my own figures
  
lab-4.html

  * no changes needed

lab-5.html

  * something weird happened with data in 2022 -- check that it makes sense and maybe consider fixing to make the example make sense.
    * shuffle groups

  
11/12-diagnostics.html

  * better example for homoscedasticity (it wasn't very clear)
  * add code for multicollinearity
  * explain independence plots more -- give strong examples of each end (dependence, independence)

lab-6.html

  * this is a boring lab. How can we spice it up?
    * murder mystery mini-hacks (something's wrong with this model)

13-dag.html
* add paper by Grosz, Rohrer, & Thoemmes (2020) "The taboo against explicit causal inference in non experimental psychology." As optional reading. Describe during class?
* clarify "causality"

homework-2.html
* make due last week of class
* weights are 1/predicted values, not predicted values!!!

lab-8.html
* add power and cohen's D
* remove manual calculation of SS

15-interactions.html
* write out equations on the screen 

16-18-factorial.html
* switch order of EMM and pred in sjPlot

19-interactions.html
* blank slides

20-poly-boot.html
* keep -- when do I use this!

lab-9.html
* do NOT work from the website version of this lab. Instead, note that there is a static version on the website available for reference, but spend the entire lab opening a papaja document and working ONLY on that.
* if too long, remove bibliography stuff (sorry Cam)