Behavioural data analysis using maximum likelihood in R (BDML01)
https://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-likelihood-bdml01/
Instructors - Dr William Hoppitt
19th March 2018 - 23rd March 2018, Scotland
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Course only and accommodation packages available
• 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 18th March and departure Friday 23rd March PM.
Course Overview:
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.
Monday 19th – Classes from 09:00 to 17:00
Module 1: The process of statistical inference and the role of statistical models. Why learn likelihood techniques? Course outline
Module 2: Maximum likelihood estimation: single parameter models for binary data
Tuesday 20th – Classes from 09:00 to 17:00
Module 3: Models with several parameters for binary data, optimization algorithms
Module 4: Testing hypotheses and constructing confidence intervals
Wednesday 21st – Classes from 09:00 to 17:00
Module 5: Modelling count data and the Poisson distribution
Module 6: Modelling continuous data, the normal distribution and the relationship of maximum likelihood to least squares
Thursday 22nd – Classes from 09:00 to 17:00
Module 7: Modelling time to event data and the exponential distribution
Module 8: Akaike’s information criterion (AIC) and model averaging
Friday 23rd – Classes from 09:00 to 16:00
Module 9: A brief introduction to Bayesian analysis, the practical advantages, and its relationship to maximum likelihood
Afternoon: Trouble shooting and final summary
Please send enquiries to [log in to unmask] or visit www.psstatistics.com
Other upcoming courses listed below
Introduction to Bayesian hierarchical modelling using R (IBHM02)
https://www.psstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/
Behavioural data analysis using maximum likelihood in R (BDML01)
https://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-likelihood-bdml01/
Introduction to statistical modelling for psychologists in R (IPSY01)
https://www.psstatistics.com/course/introduction-to-statistics-using-r-for-psychologists-ipsy01/
Social Network Analysis for Behavioural Scientists using R (SNAR01)
https://www.psstatistics.com/course/social-network-analysis-for-behavioral-scientists-snar01/
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