PPLS Summer Training (R and Stats)
Overview of the Course
Programme
Learning Outcomes
Preparation
Expectations
The team
List of extra resources
Authorship
Last, but not least
1
Extra Resources
1.1
More R practice
1.2
Data Cleaning
1.3
Visualisations
1.4
Other Common Methods in R
1.5
Big Data
2
Introduction to R and RStudio
2.1
First Step
2.2
Second Step
2.3
R as an interactive envrionment
2.4
Setting Up Your Working Directory
2.5
Spacing
2.6
Typos
2.7
Unfinishe…. d
2.8
Basic Arithmetic
2.9
Using Functions for Calculations
2.10
Short Example
2.11
Exercise 1
2.12
Exercise 2
3
Tests and modelling in R
3.1
Hypothesis testing
3.2
T test
3.3
Chi squared distribution and test
3.3.1
Contingency tables
3.4
Chi squared distribution
3.5
One way Anova
3.6
Sharks and ice cream example
3.7
Simple Linear Regression in R
3.8
Regression Diagnostics - assess the validity of a model
3.8.1
Violations of the assumptions: available treatments
3.9
Standardisation
3.10
Interaction (simple slope) and multiple explanatory factors
3.11
AIC & BIC
4
Simple Linear Model and Mixed Methods
4.1
Data sets
4.2
Longitudinal Data
4.3
Why a new model?
4.4
Ecological Fallacy (quick illustration) - no need to run
4.5
Simple Example
4.6
Now for Advanced: Model set up
4.6.1
Pooling
4.6.2
No pooling
4.6.3
Partial Pooling (varying intercepts)
4.6.4
Partial Pooling Extended - (varying intercepts and/or slopes)
4.7
Multilevel modelling with random intercepts and slopes
4.7.1
Overview of the data set
4.7.2
Prepare
4.8
Random slopes, intercepts and cross level interactions (optional)
5
Testing the assumptions
6
Coffee break
7
Logistic setting
7.1
Simple Example
7.1.1
Optional (Odds Refresher)
8
Now for Advanced: logistic mixed effects
9
Now, over to you!
9.1
Data Description
10
week 4
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Data Analysis for Psychology Using R
Chapter 6
Coffee break