PSY612: Data Analysis II
Below are links to the labs that I created and taught in Winter 2020.
- This lab will focus on correlations. We will discuss how to calculate a correlation coefficient between two variables, how to assess statistical significance of correlations, and a variety of tools for visualizing correlations, especially among large groups of variables.
Simple Regression and the General Linear Model
- This lab will briefly review univariate regression and then will discuss how to summarize and visualize uncertainty in regression models using a variety of plotting methods. We will then touch on how to estimate regression coefficients using matrix algebra. Lastly, we will introduce the General Linear Model and demonstrate how GLM can be used to understand all of the statistical tests we have learned so far (t-tests, ANOVA, correlations, regressions) within one (beautiful!) unifying framework.
Regression with Categorical Predictors
- This lab will focus on one-way ANOVA. However, rather than spend a lot of time on the traditional way of doing an ANOVA, we will instead focus on running ANOVA as a linear regression with a categorical predictor. In particular, we will discuss how to code categorical variables in R and how this affects interpretation of model coefficients.
- This lab will review how to run models containing interactions between two continuous predictors. We will go over how to specify interaction terms in R, how to interpret the model output and how to visualize the results.
- Factorial ANOVA refers to a special case of the general linear model in which there is an interaction of two or more categorical variables (i.e. factors). A factorial design is used when there is an interest in how two or more variables (or factors) affect some outcomes variable. Rather than conduct separate one-way ANOVAs for each factor, they are all included in one analysis. Today we will review how to run factorial ANOVA models in R and how to interpret and visualize the results.
See the full course website here.