Make sure you complete the week’s tutorial ahead of your practical session!
In our first tutorial we will re-visit R Markdown and learn about code chunk labels and options and why they are very useful. We will also talk more about the YAML header and some the options it takes
In this tutorial we will go over the essential R skills you acquired in Psychology as a Science last term. We’ll do some piping and data wrangling with tidyverse and throw in a plot or two for a good measure
In this tutorial we’ll walk through calculations of critical values and proportions of the area below the curves formed by certain probability distributions. ‘Let’s talk about shapes, baby’ and other bad 90s references
In the most bizarre game of D&D you’ll have ever played, we’ll use the Null Hypothesis Significance Testing framework to solve a mildly intriguing mystery
In this tutorial we will learn how to run a correlation analysis in R. We’ll create correlation matrices and do tests of significance to learn how to report and interpret the results
In this tutorial we will learn how to run and report a chi-square analysis in R. We’ll create bar plots to visualise the data and do tests of significance to learn how to report and interpret the results
In this tutorial we will learn how to run and report a t-test in R. We’ll create means plots to visualise the data and do tests of significance to learn how to report and interpret the results
We explore the equation of the linear model and begin creating models in R
Extending Tutorial 8, we talk about finding the line of best fit, testing the model within the framework of NHST, and evaluating how well the model fits the data
Building on Lecture 9, this tutorial lets you practice building linear models with multiple predictors and interpreting and evaluating their output
The final tutorial of Analysing Data focuses on including and interpreting categorical predictos in linear models, on assessing model fit, and on comparing several models against each other