Data Analysis for Psychology in R-2
2025/26

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 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: Study Brief
Linear Model Analysis: Analysis Report 		Block 1 Analysis & Write-Up Example
Sem 1 Week 6 No Lecture
Sem 1 Week 7 Categorical predictors and dummy coding Dummy coding
Sem 1 Week 8 Effect coding and manual post-hoc contrasts Effects coding
Sem 1 Week 9 Assumptions and diagnostics Assumptions and diagnostics
Sem 1 Week 10 Bootstrapping and confidence intervals Bootstrapping
Sem 1 Week 11 Categorical predictors: Study brief
Categorical predictors: Practice analysis 		Block 2 analysis and write-up example
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Sem 2 Week 1 Interactions I
Sem 2 Week 2 Interactions II
Sem 2 Week 3 Interactions III
Sem 2 Week 4 Analysing Experiments
Sem 2 Week 5 Interaction Analysis
Flexible Learning Week No Lecture
Sem 2 Week 6 Power
Sem 2 Week 7 Binary Logistic Regression I
Sem 2 Week 8 Binary Logistic Regression I
Sem 2 Week 9 Logistic Regression Analysis
Sem 2 Week 10 Exam Prep and Course Q&A

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