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.

Course materials 2021/22

Semester 1

Week	Lecture	Workbook
1	Preliminaries Functions & Models	Functions & Models
2	Linear Model Intro Linear Model Coefficients	Simple Linear Regression
3	Model Evaluation Standardized Coefficients Binary Predictors	Model Fit
4	Multiple Predictors Categorical Predictors with >2 Levels	Multiple Linear Regression
5	Interactions (Continuous * Categorical)	Interactions I
6	– Break Week –	–
7	Interactions (Continuous * Continuous)	Interactions II
8	Interactions (Categorical * Categorical)	Interactions III
9	Assumptions Diagnostics I Diagnostics II	Assumptions & Diagnostics
10	Bootstrap Theory Bootstrap for Regression	Bootstrapping Regression
11	– No Lecture –	Writing Up

Semester 2

Week	Lecture	Workbook
1	Model Comparison	Model Comparison
2	Coding Categorical Predictors	Coding Categorical Predictors
3	Experimental Designs	Contrasts, Study Design & Factorial ANOVA
4	Factorial Designs	Two-Way ANOVA
5	Multiple Comparison & Assumptions	Assumptions, Multiple Comparisons, Corrections & Writing up
6	– Break Week –	–
7	Binary Logistic Regression I Binary Logistic Regression II	Logistic Regression
8	Intro to the Generalised Linear Model (GLM) Intro to Missing Data	More Logistic Regression
9	Exploratory vs Confirmatory Analysis	Exploratory vs Confirmatory Analysis
10	Power for Linear Models	Power for Linear Models
11	Reproducibility, Open Science & Preregistration	Recap

Archives

2020/21 Archive