The PSYC 615 lab covers applications of the general linear model to research design and analysis. This repository contains lab materials for students using R or jamovi.
Each week you will learn something new in this lab. All of the learning material in this course is cumulative, so it is important not to skip anything.
Please read and work through the Preface, Chapter 1, Chapter 2, and Chapter 3 of Hands-On Programming with R before next week's lab. Make sure to program along with the book; it will help you learn better and set you up for success for the rest of the course. If you run into problems or have any questions write them down and ask about them in next week's lab.
Optionally, you can also go through Chapters 4 to 7 of the book if you would like. Otherwise you will go through them in the following week.
Also check out the Base R and RStudio cheat sheets! They cover what you learned this week in a condensed manner (plus some extra tips and tricks) and are an excellent reference if you forget something. You can find the cheat sheets at cheatsheets/base-r.pdf and cheatsheets/rstudio-ide.pdf.
Please read and work through Chapter 4, Chapter 5, Chapter 6, and Chapter 7 of Hands-On Programming with R before next week's lab. This will be your last reading from the Hands-On Programming with R book (although we encourage you to finish the remainder of the book when you have time). It is a valuable resource so refer back to it if you need an in-depth refresher on any of the R basics.
Please also read and work through this week's Help Yourself R Markdown document for Assignment 1. You can find the R Markdown file at labs/02_assumptions-ttests/help-yourself_assignment-1.Rmd. This document contains everything you need to complete Assignment 1 (and a little bit extra). This first Help Yourself is a bit long because it covers basic processes you will need to use on other assignments---such as reading, wrangling, and visualizing data---as well as applications of the statistics you learned this week. The remaining Help Yourself documents should not be as long.
Cheat sheets you might find helpful this week include the:
- R Markdown cheat sheet found at
cheatsheets/rmarkdown.pdf - Data Import cheat sheet found at
cheatsheets/data-import.pdf - Data Transformation cheat sheet found at
cheatsheets/data-transformation.pdf - Syntax cheat sheet found at
cheatsheets/syntax.pdf
Please read and work through this week's Help Yourself R Markdown document for Assignment 2. You can find the R Markdown file at labs/03_anova-followups/help-yourself_assignment-2.Rmd.
Please read and work through this week's Help Yourself R Markdown document for Assignment 3. You can find the R Markdown file at labs/04_orthogonal-contrasts/help-yourself_assignment-3.Rmd.
Please read and work through this week's Help Yourself R Markdown document for Assignment 4. You can find the R Markdown file at labs/05_factorial-anova/help-yourself_assignment-4.Rmd.
Please read and work through this week's Help Yourself R Markdown document for Assignment 5. You can find the R Markdown file at labs/07_ancova/help-yourself_assignment-5.Rmd.
Please read and work through this week's Help Yourself R Markdown document for Assignment 6. You can find the R Markdown file at labs/08_within-subjects-anova/help-yourself_assignment-6.Rmd.
Please read and work through this week's Help Yourself R Markdown document for Assignment 7. You can find the R Markdown file at labs/09_trend-analysis/help-yourself_assignment-7.Rmd.
Please read and work through this week's Help Yourself R Markdown document for Assignment 8. You can find the R Markdown file at labs/10_mixed-anova/help-yourself_assignment-8.Rmd.
R is an open source programming language for wrangling, visualizing, modelling, and communicating data, and so much more. It is widely used among researchers, statisticians, and data scientists in a variety of fields. There is an active and welcoming community of R users and programmers on Stack Overflow, GitHub, and Twitter, so it is often easy to get help or help yourself when you run into problems.
R can be downloaded from CRAN (the comprehensive R archive network) using the following link https://cloud.r-project.org. A new major version of R comes out once a year, and there are 2-3 minor releases each year.
RStudio is an integrated development environment, or IDE, for R programming. Download and install it from http://www.rstudio.com/download. RStudio is updated a couple of times a year. When a new version is available, RStudio will let you know. It's a good idea to upgrade regularly so you can take advantage of the latest and greatest features.
The following books will get you started learning and mastering R:
This book will teach you how to program in R, with hands-on examples. It was written for non-programmers to provide a friendly introduction to the R language. You'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools. Throughout the book, you'll use your newfound skills to solve practical data science problems.
This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
In this book you will learn how to: (1) Install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE; (2) Develop a few key workflows that cover your most common tasks; (3) Integrate Git and GitHub into your daily work with R and R Markdown. The target reader is someone who uses R for data science. The use of Git/GitHub in data science has a slightly different vibe from that of pure software develoment, due to differences in the user's context and objective. Happy Git aims to complement existing, general Git resources by highlighting the most rewarding usage patterns for data science.
jamovi is an open source statistical spreadsheet software for wrangling, visualizing, and modelling data. It is designed to be easy to use and offers a compelling alternative to costly statistical products such as SPSS and SAS. jamovi is built on top of the R programming language, and the underlying R code for analyses can be made available with jamovi's syntax mode.
jamovi can be downloaded from the following link https://www.jamovi.org/download.html.
The following resources will get you started learning and mastering jamovi:
The jamovi user guide has everything you need to get up and running with jamovi. After that the community resources have everything you need to learn more.
Learning statistics with jamovi covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students. The book discusses how to get started in jamovi and covers the analysis of contingency tables, correlation, t-tests, regression, ANOVA, and factor analysis.
You can clone this repository to your computer by either:
- Using the GitHub Desktop application.
- Using RStudio's git integration.
- Downloading a
.zipfile of the repository directly from GitHub.
All three of these options will get the repository onto your computer. Option 1 or 2 allow you to see when changes to the repository have been made (and to apply those changes on your computer). Option 3 does not allow this capability, but it is the simplest method.