Data Analysis for Psychology in R 3
2024/25

Course description

Data Analysis for Psychology in R 3 (DAPR3) is a course undertaken by 3rd year students in Psychology. DAPR3 builds on the content of DAPR2 and covers more advanced methods that are invaluable for analysing many types of psychological study, preparing students for their dissertations. The course offers students a solid foundation in multilevel modeling, expanding the linear model to analyze "hierarchical data". Such data often involves observations clustered within higher-level groups, such as trials within participants, timepoints within individuals, or children within schools. In the second half of the course, we delve into data reduction techniques. These methods allow us to effectively summarize multiple correlated variables, either through weighted composites or by positing underlying latent factors. Additionally, students will gain insights into crucial concepts, including measurement error, validity, reliability, and replicability. These concepts are especially essential for researchers in psychology, where surveys or questionnaires are used to conduct studies of underlying constructs that cannot be directly measured.

Week Slides Workbook
Welcome Course Intro
Week 1 Regression Refresh & Group-Structured Data
Reading: Group-Structured Data
Exercises: Regression & Clustered Data
Week 2 Multilevel Models
Reading: The Multi-level/Mixed-effect Model
Exercises: Introducing MLM
Week 3 Random Effect Structures & Model Building

Reading: Random Effect Structures
Reading: Model Building
Exercises: Nested & Crossed Structures
Week 4 Assumptions, Diagnostics & Centering

Reading: MLM Assumptions & Diagnostics
Reading: Centering Predictors in MLM
Exercises: Centering
Week 5 Recap & The Research Process
Reading: Reporting on analyses with MLM
Exercises: Bringing it all together
Week 6 No Lecture No Lab
Week 7 Introduction to Psychometric Testing
Reading: Questionnaire Data Wrangling
Exercises: Questionnaire Data & Scale Scores
Week 8 Principal Component Analysis (PCA)
Reading: PCA
Exercises: PCA
Week 9 Exploratory Factor Analysis (EFA)
Reading: EFA
Exercises: EFA
Week 10 More EFA Exercises: More EFA, replicability, reliability
Week 11