OLET5610: Intensive June, 2023

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Units OLET5610 Intensive June 2023 [Online]

Unit of study_

OLET5610: Multivariate Data Analysis

Intensive June, 2023 [Online] - Camperdown/Darlington, Sydney

Overview

When undertaking research and critically judging the research of others with many variables, a key approach is use of multivariate data analysis. This online unit provides comprehensive skills essential for postgraduate students doing multivariate data analysis and for critically judging the research of others. We focus on the underlying principles you need to explore multivariate data sets and test hypotheses. In so doing, the unit provides you with an understanding of how multivariate research is designed, analysed and interpreted using statistics. The unit will cover the commonly used multivariate data analyses of principal components analysis, cluster analysis, discriminant functions analysis and non-metric multidimensional scaling, as well as parametric and permutational hypothesis testing techniques. Examples of data will be cross-disciplinary, enabling students from many disciplines to appreciate the techniques. Analyses will use the R statistical environment, furthering student skills in this programming environment. By doing this unit, you will be able to use multivariate data analyses using a wide-range of data and present in a format for publication.

Unit details and rules

Unit code	OLET5610
Academic unit	Life and Environmental Sciences Academic Operations
Credit points	2
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Mathew Crowther, mathew.crowther@sydney.edu.au
Lecturer(s)	Alex McBratney, alex.mcbratney@sydney.edu.au
	Mathew Crowther, mathew.crowther@sydney.edu.au
	Floris Van Ogtrop, floris.vanogtrop@sydney.edu.au
	Januar Harianto, januar.harianto@sydney.edu.au

Type	Description	Weight	Due	Length
Tutorial quiz	Online Quiz 1 Online task	40%	Week 05 Due date: 29 Jun 2023 at 16:00	1 hour
Outcomes assessed:
Tutorial quiz	Online Quiz 2 Online task	40%	Week 05 Due date: 29 Jun 2023 at 16:00	1 hour
Outcomes assessed:
Assignment	Scientific report Scientific report	20%	Week 05 Due date: 23 Jun 2023 at 16:00	2000 words
Outcomes assessed:

Assessment summary

Two online quizzes (80%) and one report (20%).

The quizzes test knowledge of the multivariate analysis topics. Two quizzes must be attempted. There are 3 attempts per quiz and the highest mark is recorded.

The report is the methods and results of the analysis section of a scientific paper. The report must use 2 of the multivariate analyses in the course and the analyses must be done in the R Statistical Environment. Students to select their own datasets. The report is maximum 2000 words, not including tables, figures or references.

These assessments are all compulsory and failure to attend, attempt, or submit will result in the award of an AF grade.

Assessment criteria

Result

name

Mark

range

Description

85-100

75-84

65-74

50-64

<50

An excellent performance in all tasks

A very good preformace in all tasks

A good perfomance in all tasks

An adequate performance in all tasks

An inadequate performance in all taks

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

5% / day

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Principal Components Analysis	Online class (4 hr)
	Multidimensional Scaling	Online class (4 hr)
	Discriminant Functions analysis and classification	Online class (4 hr)
	MANOVA and hypothesis testing	Online class (4 hr)
	Permutational hypothesis testing	Online class (4 hr)
	Random Forests	Online class (4 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 2 credit point unit, this equates to roughly 40-50 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. demonstrate a key understanding of multivariate data, it limitations and uses
LO2. design studies with multivariate data
LO3. use a number of ordination methods with statistical tests for multivariate data
LO4. able to perform multivariate analyses within the R statistical environment
LO5. able to present multivariate analysis graphs for publication

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

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Additional information

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Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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