Introduction to statistical modelling for psychologists in R (IPSY01)
16th April 2018 - 20th April 2018£260.00 - £500.00
This course will provide an introduction to working with real-life data typical of those encountered in the field of psychology. The course will be delivered by Dr. Dale Barr and Dr. Luc Bussière, who are practicing academics in the fields of psychology and evolutionary biology respectively, with many years of expertise with R and statistical modelling as both scientists and instructors. This five-day course will consist of series of modules (each lasting roughly half a day) covering topics including the basic ‘canon’ of psychological statistics (t-test, correlation/regression, ANOVA) presented within the framework of general linear models, and building up to logistic regression and linear mixed-effects modelling. Along the way you will gain in-depth experience in data wrangling using the R ‘tidyverse’, data and model visualisation and plotting, as well as exploring and understanding model diagnostics. Classes will consist of a mixture of lectures and practical exercises designed to either build required skills for future modules or to perform a family of analyses that is frequently encountered in the psychological literature.
Any researchers (from postgraduate students to senior investigators) wanting to learn how to correctly analyse data typical to the field of psychology using the R programming language.
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, dinner, refreshments, minibus to and from meeting point and accommodation. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms. Arrival Sunday 15th April and departure Friday 20th April PM.
Other payment options are available please email firstname.lastname@example.org
Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PS statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PS statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
There will be a combination of lectures and practicals. Practicals will be based on the topics covered in the preceding lectures. Data sets for computer practicals will be provided by the instructors
Assumed quantitative knowledge
A basic understanding of statistical concepts, including statistical significance and hypothesis testing
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots. Relative newcomers to programming in R will be provided (by the instructors) with some introductory exercises to complete prior to the course. This will introduce some of the core features of R and RStudio before the course starts.
Equipment and software requirements
A laptop/personal computer with a working version of R or RStudio. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) as internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Meet at the Tullie Inn, Balloch at approximately 18:30 before being taken by minibus to SCENE (Download directions PDF).
Monday 16th – Classes from 09:00 to 17:00
Introduction to R/RStudio
• interacting with the RStudio IDE
• installing add-on packages
• R scripts and R notebooks
• coding style guidelines
• session management and project organization
Data wrangling and reproducible workflows with the tidyverse
• loading datasets (csv, excel, SPSS, google drive)
• filtering, sorting, and reshaping data
• grouping and summarizing data
• combining datasets using joins
• chaining commands together using ‘pipes’
Tuesday 17th – Classes from 09:00 to 17:00
Data visualization with ggplot2
• the ‘grammar of graphics’ philosophy
• univariate plots: histograms, density plots, boxplots, bar graphs, violin and pirate plots
• bivariate plots: scatterplots, line graphs, interaction plots
• enhancing plots using labels and themes
• creating subplots with faceting
The psychology stats ‘canon’ and the General Linear Model
• t-tests, confidence intervals, effect size, and power
• correlation matrices and simple linear regression
• contingency tables; chi-square tests
• correlation and simple regression
Wednesday 18th – Classes from 09:00 to 17:00
• coding categorical predictors
• detecting and dealing with multicollinearity
• polynomial models for time-series data
• model comparison and information criteria
• model checking/validation, plotting predictions
Thursday 19th – Classes from 09:00 to 17:00
Analysis of Variance in the GLM framework
• one-factor designs
• multifactor designs: main effects and interactions
• within-subject and mixed designs
• checking assumptions (sphericity, normality, homogeneity of variance)
• plotting and interpreting interactions
• follow-up tests and contrasts
Generalized Linear Models
• binary data (logistic regression)
• count data (Poisson regression)
• generating and plotting model predictions
Friday 20th – Classes from 09:00 to 16:00
Introduction to Linear Mixed-Effects Models
• crossed random effects of participant and item
• understanding variance components through data simulation
• specifying the random effects structure
• translating study design into lmer model syntax