Data Analysis for Psychology in R 2
2024/25
Course description
Data Analysis for Psychology in R 2 (DAPR2) is a course taken by 2nd year students in Psychology. The course introduces methods for analysing both observational and experimental psychological studies, with a particular focus on linear models. The course is taught across the full academic year – spanning two semesters – split into four teaching blocks. The first block will introduce students to linear models, where they will learn to build and interpret linear models for continuous outcomes with single and multiple predictors. The second block extends from multiple linear regression to include interactions, as well as introducing assumptions and diagnostics checks, and bootstrapping. The third block sees a shift from correlational to experimental designs, where students will be introduced to multiple regression with categorical predictors, and will learn how to conduct multiple comparisons, apply different types of corrections, and run model comparisons. The fourth and final block focuses on more niche and advanced topics within the realm of linear regression. Students will be introduced to generalized linear models for binary outcomes before focusing on wider issues within the psychological literature, such as replication, power, pre-registration, and open science.
| Week | Slides | Workbook |
|---|---|---|
| Sem 1 Week 0 | Course Introduction | No Lab |
| Sem 1 Week 1 | Intro to Linear Models | Simple Linear Regression |
| Sem 1 Week 2 | Interpreting Linear Models | Multiple Linear Regression |
| Sem 1 Week 3 | Testing Individual Predictors | Multiple Linear Regression & Standardization |
| Sem 1 Week 4 | Model Testing & Comparison | Model Fit and Comparisons |
| Sem 1 Week 5 | Linear Model Analysis | Write Up & Block 1 Recap |
| Sem 1 Week 6 | No Lecture | No Lab |
| Sem 1 Week 7 | Categorical Predictors & Dummy Coding | Dummy Coding |
| Sem 1 Week 8 | Effects Coding & Coding Specific Contrasts | Effects Coding |
| Sem 1 Week 9 | Assumptions & Diagnostics | Assumptions and Diagnostics |
| Sem 1 Week 10 | Bootstrapping | Bootstrapping |
| Sem 1 Week 11 | Categorical Predictor Analysis | Write Up & Block 2 Recap |
| --- | ||
| Sem 2 Week 1 | Interactions I | Interactions I: Num x Cat |
| Sem 2 Week 2 | Interactions II | Interactions II: Num x Num |
| Sem 2 Week 3 | Interactions III | Interactions III: Cat x Cat |
| Sem 2 Week 4 | Analysing Experiments | Simple Effects, Pairwise Comparisons, & Corrections |
| Sem 2 Week 5 | Interaction Analysis | Write Up & Block 3 Recap |
| Flexible Learning Week | No Lecture | No Lab |
| Sem 2 Week 6 | Power | Sample Size and Power Analysis |
| Sem 2 Week 7 | Binary Logistic Regression I | Logistic Regression I |
| Sem 2 Week 8 | Binary Logistic Regression I | Logistic Regression II |
| Sem 2 Week 9 | Logistic Regresison Analysis | Write Up & Block 4 Recap |
| Sem 2 Week 10 | Exam Prep and Course Q&A | Mock Exam |