Learning Objectives

• How to describe one variable (Univariate Descriptives)
  • Describe data distributions via graphical (e.g., histograms) and summary statistics (e.g., mean, standard deviation) techniques that evaluate center and spread.
• How to describe how two variables relate (Bivariate associations)
  • Describe bivariate relationships via graphical (e.g., scatterplots) and summary statistics (e.g., covariance, correlation) techniques that evaluate the strength of linear associations.
• How to construct graphics to show relationships between variables (Structural Equation Modeling Diagrams)
  • Show hypothesized causal and associative relationships between variables through accepted SEM diagramming techniques.
• Sampling Distributions
  • Describe the process of the creation of sampling distributions and describe their distributional properties (e.g., spread or standard error).
• Statistical language fluency
  • Demonstrate mastery of statistical terminology (e.g., p-value, standard errors) through the application and the implication on the stories we can tell from data.
• Regression Betas
  • Recite from memory the definition of an unstandardized and standardized beta in a linear regression context and apply its definition in understanding its implication in data analysis.
• Relate to traditional statistics
  • Demonstrate mastery of how SEM techniques are equivalent to traditional statistical methodology (e.g., ANOVA, t-test, etc).