PSY4210 -- Statistics and Data Science for Psychology

This is a hands on, applied statistics and data science unit. It will introduce methods both from a conceptual and applied perspective. The unit will make use of R, a programming language and environment for statistics.

R is completely controlled by written code. R also is open source, meaning that all of the code used in R and in any analysis are publicly and freely viewable. This is good for science as it means it is possible to verify any aspect of R. R is available free of cost as it is written and maintained by a community of thousands of developers. Because it is community driven, instead of every feature coming with R by default. Most of R features come through add on packages. You can think of these like apps on a phone. R is the operating system (Android, iOS), but often you may spend most your time using apps (in R lingo, packages). Like apps, there are thousands of R packages, and this extensive ecosystem makes R one of the most powerful and flexible environment for statistics.

This unit does not assume any background in R. Over the semester, you will learn some basics of R programming, but mostly, you will be able to copy and paste existing code and just change the dataset and variables to suit your specific analytic needs.

This GitHub repository has a number of resources for the unit. All code and much of the lecture content (both as HTML, R Markdown and Word Documents and PDFs are available as well). If you want, you can sign up for notifications any time any of the files in this repository are changed.

• Week 1 Introduction to R and Setup.
  Getting R installed, setup, and learning a few basics. Please start with the page Intro to R. Next, we'll work with our first R markdown file.

• Week 2 Working with Data. Please see the Content.

• Week 3 Visualizing Data (Part 1). Please see the Content.

• Week 4 Generalized Linear Models (Part 1). Please see the Content.

• Week 5 Generalized Linear Models (Part 2). Please see the Content.

• Week 6 Visualizing Data (Part 2). Please see the Content.

• Week 7 Missing Data. Please see the Content.

• Week 8 Introduction to Linear Mixed Models (LMMs). Please see the page LMM Intro.

• Week 9 Linear Mixed Models (LMMs; Part 1). Please see the page LMM1.

• Week 10 Linear Mixed Models (LMMs; Part 2). Please see the page LMM2.

• Week 11 Interactions and Moderation for LMMs. Please see the page LMM Moderation.

• Week 12 Model Comparisons for LMMs. Please see the page LMM Comparison.