Introduction to Mixed Effects Modelling: Online - (2 days)

This course is designed as an introduction to mixed effects modelling. These models involve data arising from longitudinal studies or studies where the data exhibits some form of hierarchy, and sometimes referred to as multilevel modelling.

 

 

This course will be offered online via Zoom And will run to the following timetable:

 

  • 9.30am -11.00am: Instructional Zoom session
  • 11.00am-11.30am: Break
  • 11.30am-12.30pm: Instructional Zoom session
  • 12:30pm-1.30pm: Lunch
  • 1.30pm-3.00pm: Instructional Zoom session
  • 3.00pm-3.30pm: Break
  • 3.30pm-4.30pm:  Exercises and discussion

 

Please note: Courses will run on Australian Eastern Standard Time (GMT +10)

 

 

 
Master Class - runs over 2 days
Instructor: 
About this course: 

This course is designed as an introduction to mixed effects modelling. These models involve data arising from longitudinal studies or studies where the data exhibits some form of hierarchy, and sometimes referred to as multilevel modelling.

 

Mixed effects modelling is used when observations are not independent of each other (e.g., clustered data, repeated measures). This type of analysis is regularly used in such areas as educational research when studying the performance of students within schools and in medical research when investigating the outcomes over time following major trauma.

 

Mixed effects refer to the inclusion of both fixed effects (i.e., the variables that are constant across individuals) and random effects (i.e., account for variability among subjects around the relationships captured by the fixed effects).

 

This course will be discussing the linear mixed effects models in which the outcome of interest is continuous. Discussion of some of the uses of mixed effects models in publications will be discussed at the end of the course.

 

Course syllabus: 

This course is broken up into the following sections:

 

Part I: Introduction to the Mixed Effects Model
Part II: Types of Hierarchical Data
Part III: Defining the Covariance Structures
Part IV: Inclusion of Random Effects
Part V: Mixed Effects Models in Publications

 

Participants will be given time to do some exercises on their own to practise what they have learned.

Exercises and solutions will be provided in Stata, R and Python software.

 

Course format: 

This workshop will take place online using Zoom.

You will need your own computer with Stata, or Python and/or R installed, and an internet connection.

A second screen/monitor is recommended.

Recommended Background: 

This course assumes that participants have:

(1) Sound familiarity with at least one of the three software packages Stata, R and/or Python.

(2) sufficient understanding of statistics to be able to comprehend the material covered in the course outline, such as a basic grounding in multiple regression (e.g., linear, logistic, Poisson)

(3) access to either Stata, R and/or Python

(4) some experience in using Microsoft Word and Excel or their equivalent

(5) experience using a text editor such as Notepad.

 

Recommended Texts: 

Course notes will be supplied. Please include a shipping address when you enrol. Your notes will be express posted to this address.

 

No specific references are suggested but a number will be supplied with the notes handed out for the course.

Program where course next likely to be offered: 
Master-class September 2021: Mixed Effects Modelling: Online