Data Analysis for Psychology in R 2
2022/23

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 Course Introduction
Intro to the linear model 		Functions and Models
Sem 1 Week 2 Linear Model: Fundamentals Intro to Linear Regression
Sem 1 Week 3 Testing and Evaluating LM Multiple Regression
Sem 1 Week 4 F-tests & Standardisation Model Fit and Standardisation
Sem 1 Week 5 Categorical Predictors Categorical Predictors & Recap
Sem 1 Week 6 No Lecture
Sem 1 Week 7 Interactions 1 Interactions I: Num x Cat
Sem 1 Week 8 Interactions 2 Interactions II: Num x Num
Sem 1 Week 9 Interactions 3 Interactions III: Cat x Cat
Sem 1 Week 10 Assumptions & Diagnostics Assumptions & Diagnostics
Sem 1 Week 11 Worked Example Lecture Write Up & Recap
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Sem 2 Week 1 Model Comparisons Model Comparison
Sem 2 Week 2 Effects Coding Effects Coding
Sem 2 Week 3 Testing Contrasts and One-way Analyses Contrasts
Sem 2 Week 4 Analyzing Experiments Simple Effects & Pairwise Comparisons
Sem 2 Week 5 Bootstrapping Write Up & Recap
Flexible Learning Week --- ---
Sem 2 Week 6 Binary Logistic Model Binary Logistic Regression
Sem 2 Week 7 Introduction to Power Analysis More Logistic Regression
Sem 2 Week 8 Understanding Linear Models Sample Size & Power Analysis
Sem 2 Week 9 Exploratory & Confirmatory Data Analysis Write Up & Recap
Sem 2 Week 10 Course Q&A and Exam Prep Mock Exam

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