Through a sequence of four learning modules, this course will enhance your skills in the analysis phase of the research lifecycle. It begins with an introduction to the basics of statistical modelling using R, progressing to more complex concepts and models in later modules. Each module consists of interactive online tutorials plus a Zoom sessions in which the instructor facilitates discussions of the material contained in the tutorials. Students who register to participate in the Zoom session will be expected to actively participate in the discussions through group work or with the entire class.

This course is suitable for HDR students in the second or third stage of their candidature. It supports the students’ development in the Research Knowledge and Skills element of the MAPARC research student capability framework and the Research Lifecycle element of the UTS Research Outcomes Capability framework.

Time: 9:30 AM - 11:30 AM

Facilitator: A/Prof Tapan Rai

Day Four: Monday, 2 November 2020

Analysing Longitudinal (Panel) Data: Generalised Estimating Equations and Linear Mixed Models

This module will introduce you to statistical models that are used for longitudinal (panel) data. These models work by relaxing the assumption of independence of observations that were strictly required for models introduced in the previous modules. The material covered on Days 1 and 2 is a pre-requisite for this module. Specifically, you will:

Appreciate the issues that arise in modeling longitudinal (panel) data
Run a Generalized Estimating Equations (GEE) Model to model longitudinal (panel) data
Interpret the output of a GEE model, draw inferences and report them
Choose an appropriate correlation structure based on the data or theoretical considerations
Run a Linear Mixed Model (LMM) to model longitudinal (panel) data
Interpret the output of a linear mixed model, draw inferences and report them
Assess the fit of a linear mixed model through the use of residual plots
Compare GEE models and Linear Mixed Models

Zoom meeting link:
https://utsmeet.zoom.us/j/92915532718

Students need to complete the LinkedIn Learning course Learning R and to install R and RStudio on their computer prior to participating in this short course. For free access to all LinkedIn Learning Courses (including Learning R), sign in to LinkedIn with your UTS email address.

FAQ'S

What if I can no longer attend, do I need to cancel?

Yes. Please contact the Researcher Development team at grs_rd@uts.edu.au for workshop cancellation.

If you fail to cancel your registration and are absent three or more times in a session, you will be suspended from the program for 1 year. You will be notified by email prior to your suspension being applied.

If a course is more than one day, am I required to attend all days?

We strongly recommend that you attend all days in a course in order to get the full learning experience that it has to offer. However, if you need to miss one of the days, please go through the material for that day on UTS Online before participating in the subsequent Zoom sessions for that course.

Also, If you are unable to attend one of the days, please contact us at grs_rd@uts.edu.au prior to the session, so that we can plan the best experience for the other students in the course.

Who should I contact if I have any further questions?

Please contact the Researcher Development team at grs_rd@uts.edu.au. Please note you must use your UTS student email address for official university correspondence