Introduction to Behavioural data analysis using R (IBDA01)
14 October 2019 - 18 October 2019£275.00 - £520.00
This course aims to provide participants with the skills needed to analyse a wide range of behavioural data in the R statistical environment. We will focus on the linear model and the various ways it can be extended to accommodate the different kinds of data commonly encountered in behavioural research. Participants will learn how to model continuous, binary, count (including zero-inflated) and time-to-event data and how to include and interpret continuous and categorical predictors, interactions and random effects.
Any researchers (from postgraduate students to senior investigators) interested in analysing behavioural data. Examples will be primarily from non-human animal behaviour studies, but the methods will also be applicable to many researchers studying human behaviour.
Venue – PS statistics head office, 53 Cook Street, Glasgow, G5 8LB – Google map
Availability – 20 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments and welcome meal Monday evening.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, refreshments and welcome dinner Monday evening. Self-catering facilities are available in the accommodation. Accommodation is approximately a 6-minute walk from the PS statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 13th October (after 5pm) and departure Friday 21st October (accommodation must be vacated by 9am).
Other payment options are available please email firstname.lastname@example.org
PLEASE READ – 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 a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PR statistics) will be credited. However, PS statistics will not be held responsible/liable for any travel fees, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of this PS statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economical viable.
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
Knowledge of multiple linear regression
Assumed computer background
Basic use of R e.g. fitting a regression model using the lm function
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.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Meet at 43 Cook Street, Glasgow G5 8JN at 17:00 and 21:00
Monday 14th – Classes from 09:30 to 17:30
The statistical modelling process
The linear model: fitting regression models, categorical predictors, testing for and interpreting
interactions, data exploration and testing assumptions
Tuesday 15th – Classes from 09:30 to 17:30
Extending the linear model to include random effects using linear mixed models (LMMs). A brief look
at generalized least squares (GLS) models.
An introduction to maximum likelihood.
Modelling binary and count data using the generalized linear model (GLM). Interpreting coefficients.
Testing model assumptions.
Wednesday 16th – Classes from 09:30 to 17:30
Modelling time-to-event data and survival analysis using the Cox regression model.
Thursday 17th – Classes from 09:30 to 17:30
An introduction to Bayesian analysis and the use of Markov Chain Monte Carlo (MCMC) to fit
complex models. Generalized linear mixed models (GLMMs). Models for zero-inflated data.
Friday 18th – Classes from 09:30 to 16:00
Open session- depending on needs of group and progress so far.