Accessing Tutorials
Most R skills are covered in several tutorials/practicals in different modules. Where this repetition occurs, there are multiple resources to refer to for help.
Some of these R resources include interactive tutorials. These tutorials need to be installed & run within RStudio. To install the packages required to run them (“learnr”, “remotes”, “paas”, & “discovr”), type each of the commands in the code block below into the RStudio console one at a time and press Enter to run them.
install.packages("learnr")
install.packages("remotes")
remotes::install_github("SussexPsychMethods/PAAS", build = FALSE, upgrade = "never")
remotes::install_github("profandyfield/discovr")
Following installation, tutorials can be accessed through the “Tutorial” tab in RStudio, or by running the relevant code in the console, i.e.,
learnr::run_tutorial("discovr_01", package = "discovr")
The R-dex
Use the table below & hover over the modules to find out what resources cover each topic (i.e., tutorials (tut), practicals (pract), skills labs (lab), & seminars (sem)), & what week on Canvas to find them!
Yr 1: Psychology as a Science (PAAS)
Yr 1: Analysing Data (AnD)
Yr 2: Discovering Statistics (DS)
Yr 2: Qualitative and Quantitative Methods (QQM)
Optional SeminRs
| Topic | Module |
|---|---|
| R/RStudio installation |
Tut 01 
|
| RStudio tours |
Tut 01
|
| R Projects |
Tut 02 Pract 03
Pract 01
|
| File paths |
Tut 02 Pract 03
ES Sem 01 ES Sem 05 |
| R objects, functions & packages |
Tut 04 Pract 05 Tut 05 Pract 06 Tut 06 Pract 07
|
| The Pipe %>% |
Tut 09 Pract 10
Tut 02
ES Sem 03 |
| RMarkdown |
Tut 03 Pract 04
Tut 01 Pract 01 Tut 05
Lab 01
ES Sem 04 LU Sem 01 LU Sem 07 |
| Inline code |
Tut 05
LU Sem 04 LU Sem 08 |
| Importing & organising data |
Tut 07 Pract 08
Pract 02 Tut 05 Tut 06 Tut 07
Lab 11 Tut 11
Lab 05
ES Sem 01 |
| Exploring & inspecting data |
Pract 02 Pract 03 Pract 04 Tut 05 Pract 05 Tut 06 Tut 07
Lab 05
ES Sem 01 |
| General data wrangling/cleaning |
Tut 08 Pract 09 Tut 09 Pract 10
Pract 02 Tut 02 Pract 03 Pract 04 Tut 05 Tut 09 Tut 10 Pract 10 Tut 11 Pract 11
Tut 08 Lab 11
Lab 05 Lab 10 Tut 11
ES Sem 03 LU Sem 02 LU Sem 05 |
| Creating composites & reverse scoring |
Tut 07
Lab 06
|
| Recode/replace/change values/types |
Pract 03 Pract 04
Lab 11
Lab 05 Lab 06
|
| Summary stats & tables |
Tut 09 Pract 10 Tut 10 Pract 11
Pract 02 Tut 02 Pract 03 Pract 04 Tut 07 Pract 11
Lab 04 Tut 07 Tut 09 Tut 11
ES Sem 05 |
| Data visualisations & plots |
Tut 10 Pract 11
Pract 02 Tut 02 Tut 05 Pract 05 Tut 06 Tut 07 Tut 08 Pract 09 Tut 09 Pract 10
Tut 02 Lab 03 Tut 08 Tut 09 Tut 10
Lab 10 Tut 11
ES Sem 06 ES Sem 07 |
| Correlation |
Tut 05 Pract 05
Tut 06
|
| Chi-square |
Tut 06
|
| t-test |
Tut 07
|
| Simple regression |
Tut 08 Pract 09 Tut 09 Pract 10
Tut 02 Lab 02 Tut 04 Lab 05
|
| Multiple regression |
Tut 10 Pract 10 Tut 11
Lab 02 Tut 03 Lab 03 Tut 04 Lab 04 Lab 05
|
| Independent anova |
Tut 05 Tut 06 Tut 08
|
| Repeated measures anova |
Tut 09
|
| Ancova |
Tut 07
|
| Mixed designs |
Tut 10
|
| Logistic regression |
Tut 11
|
| Mediation |
Lab 11 Tut 11
|
| Moderation |
Lab 10 Tut 11
|
| Exploratory factor analysis |
Tut 06
|
| Reliability/validity/power analysis |
Lab 09
Tut 07
|
Analyses Covered
It is the supervisor’s responsibility to teach any analyses and statistical techniques not taught in methods modules. It is recommended that you familiarise yourself with the materials as detailed above when completing the dissertation analysis plan. In addition, a list of the analyses covered in first- and second-year can be found below:
Independent t-test
Chi-Square
Correlation
- Pearson’s \(r\) (2 continuous variables)
- Spearman’s \(r_{s}\) (but not in depth - used in factor analysis on likert scales)
Simple Regression
- 1 continuous predictor, 1 continuous outcome
Multiple Regression
- 2+ continuous predictors, 1 continuous outcome
- 2+ continuous predictors, 1 categorical predictor (2 levels), 1 continuous outcome
Independent Anova
- One-way: 1 categorical predictor (3 levels using dummy coding, planned contrasts, and post-hoc tests), 1 continuous outcome
- Two-way: 2 categorical predictors (3 levels & 2 levels)
- Simple effects analysis
Ancova
- 1 categorical predictor (3 levels with orthogonal contrasts), 1 continuous covariate, 1 continuous outcome
Repeated Measures Anova
- One-way: 1 RM categorical predictor (4 levels using planned contrasts and post-hoc tests), 1 continuous outcome
- Two-way: 2 RM categorical predictors (3 levels & 3 levels, 1 continuous outcome
- Simple effects analysis & post hoc interaction tests
Mixed Designs
- 1 independent categorical predictor (2 levels), 2 repeated categorical predictors (3 levels & 3 levels), 1 continuous outcome
Logistic Regression
- 1 categorical predictor (2 levels), 1 categorical outcome (2 levels)
- 1 categorical predictor (2 levels), 1 continuous predictor, 1 categorical outcome (2 levels)
Exploratory Factor Analysis
- Using likert scale data for questionnaire development
Reliability and Validity Analyses
- Omega & alpha
- Convergent and discriminant validity
Moderation
- 1 continuous predictor, 1 continuous moderator, 1 continuous outcome
- Simple slopes analysis
- Johnson-Neyman interval
Mediation
- 1 continuous predictor, 1 continuous mediator, 1 continuous outcome
- 1 continuous predictor, 2 continuous mediators, 1 continuous outcome
Useful Resources
Brush up on your stats knowledge with Prof Andy Field’s Lectures
Check out the Analysing Data website
Read through the R for Data Science book
Build your R Markdown knowledge with this definitive guide
Take a look at the R Cheat Sheets