STATS 768 : Longitudinal Data Analysis


2020 Semester Two (1205) (15 POINTS)

Course Prescription

Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.

Course Overview

STATS 768 describes mixed models, also known as hierarchical models or multilevel models, a data analysis approach that extends regression by incorporating models for the unexplained variation and correlation in residuals. Mixed models have many applications in applied statistics. They are important for the modelling of repeated measurements, and part of the course will focus on their use in the analysis of epidemiological data. Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: this includes mixed models, marginal models, dropout, and causal inference. Students will be expected to program in SAS and to be able to interpret the resulting output. Examples of SAS code will be given in the lecture notes and explained. The skills developed in this course are particularly useful for those wishing to have a career involving the analysis of epidemiological and clinical trial data collected in the health sciences, census information collected by the NZ government, and data collected for biological experiments.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Distinguish between dependent and independent data (Capability 1, 2 and 3)
  2. Apply the techniques of basic theory of mixed and generalized linear mixed models to construct appropriate working models to answer a specific research question (Capability 1, 2 and 5)
  3. Apply model building techniques (Capability 1, 2, 3 and 5)
  4. Apply model selection techniques (Capability 1, 2, 3 and 5)
  5. Implement an analysis plan when faced with clustered or longitudinal data (Capability 1, 2, 3 and 5)
  6. Produce results in a way that researchers can understand (Capability 4 and 5)
  7. Use and interpret the results back to the research question(s) (Capability 3, 4 and 5)


Assessment Type Percentage Classification
Assignments 40% Individual Coursework
Final Exam 60% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Final Exam
Must obtain 50% or better for total of assignments.
Must obtain 50% or better for the final exam.

Key Topics

1. Random effects models
2. Repeated measures models
3. Random coefficients models.
for Normally distributed data and for non-normally distributed data.

Learning Resources

A coursebook will be available from the Student Resource Centre.

Special Requirements

Must obtain 50% or better for assignments overall.
Must obtain 50% or better for the final exam.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, a 1 hour computer laboratory, and 6 hours of reading and thinking about the content and working on assignments and/or exam preparation each week.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 10/07/2020 01:27 p.m.