Simplistics (Live) – February 2025

Learning Objectives

• Understand how my approach differs from the traditional approach
• Understand the advantages of the modeling approach
• Know the key players in the replication crisis and the role they played
• Be able to differentiate between CDA, rough CDA, p-hacking, HARKing, etc.
• Understand the three dimensions of data analyst intention and what each dimension means
• Understand how to conduct data analysis ethically
• Understand the five grassroots values
• Know the difference between the status quo values and the grassroots values
• Understand how to make changes to the scientific culture

Learning Objectives

Scatterplots

• interpret scatterplots
• confirmation bias, graphics, and statistics
• lowess curves versus regression lines
• transparency and sampling
• identify
  • nonlinearity
  • bivariate outliers
  • influential datapoints
  • high leverage datapoints

Plotting Categorical Data

• interpret bar plots, boxplots, violin plots, beeswarm plots
• know why barplots suck
• know what jittering means
• know the weaknesses of barplots, boxplots, violin plots, and beeswarm plots
• how to recognize skewness and group imbalances

Bivariate Estimates

• Know why we need visuals AND estimates
• Know what a conditional mean is
• Know what a conditional variance is
• Know how to interpret the following
  • intercept
  • slope
  • correlation coefficient (r)
  • group mean difference
  • cohen’s d
• relationship between mean difference and a slope
• “benchmarks” for small/medium/large correlation coefficients and cohen’s ds
• How to use slope and intercept to predict someone’s score

R

• How to plot a scatterplot
• How to plot a beeswarm plot
• How to compute estimates in R
• How to fit a linear model in R

Learning Objective

• components of a model (fit and error/residual)
• know what a residual tells you about your model
• the four critical assumptions of linear models
• normality of variables versus normality of residuals
• homo (or hetero) skedasticity — what does it mean?
• what does independence mean?
• why do we assess assumptions?
• how to interpret a residual dependence plot (and what it tells you)
• how to interpret an SL plot (and what it tells you)
• how to generate diagnostic plots in R

Learning Objectives

• Understand the three reasons we’d want to use multivariate LMs
  
• Understand multicollinearity and why it’s a problem
• Understand what conditioning is conceptually
• Understand how residualizing relates to conditioning
• The danger in doing multivariate GLMs with p-values
• How to avoid the p-value danger
• Map “control” language into parameter estimates
• How to visualize conditioning relationships
• What R code to use to visualize conditional relationships