Multivariate Statistics - 2017

To enrol in this unit, you must be accepted into a course from the provider. Read before you start

Unit summary


  • Level of Study: Postgraduate
  • Study load: 0.125 EFTSL
  • Delivery method: Fully Online
  • Prerequisites: Yes
  • Duration: 13 weeks
  • Government loans available: FEE-HELP
  • Availability for 2016: SP1 , SP3
  • Availability for 2017: SP1 , SP3
  • Assessment: Assignments (40%) , Invigilated Exam (50%) - Learn more

Unit provided by

2017 Fees
Domestic 2,600.00
Domestic continuing 2,472.00
International 2,850.00
International continuing 2,722.00

2017 Swinburne tuition fees are reduced for continuing postgraduate students.
Please login in to view the applicable amount.

The aims of this unit of study are to identify and apply the multivariate techniques most commonly used in social and health research, and to appreciate the strengths and limitations of various statistical techniques, including an understanding of assumptions underlying their use. Techniques learnt in the unit of study are used in subsequent units of study in the course including  Structural Equation Modeling and are used by many masters’ students in their research projects. Furthermore, knowledge of these methods is particularly helpful for students to gain employment in these areas. The topics include multiple regression, introduction to logistic regression, multivariate analysis of variance, and factor analysis.

Please note: assessment values are indicative only, details will be advised at the start of the unit.

At the completion of this unit students will be able to: 
  1. Summarise the objectives of a study, specify variables of interest in a study and identify their level of measurement
  2. Prepare the data file for analyses (i.e., screening for out of range and missing values, outliers, checking the distributional properties of the variables to be used in the study, understand when it is appropriate to transform variables and be able to transform variables when appropriate
  3. Analyse the data using SPSS for Windows (i.e. be able to identify and apply the correct technique to address the study’s research questions/hypotheses)
  4. Interpret the results for each technique studied (i.e., be able to use the outcomes of data analysis to address the research question/hypotheses)
  5. Test the assumptions underlying a statistical analysis and state the limitations of a study
  6. Write a research report.
  • Assignments (40%)
  • Invigilated Exam (50%)
  • Quiz — Online (10%)
For more information on invigilated exams see Exams and results

Equivalent units

You cannot enrol in this unit if you have successfully completed any of the following unit(s) because they are considered academically equivalent:

  • HMS780 — Multivariate Statistics

Mandatory prerequisites

You must have successfully completed the following unit(s) before starting this unit:

If you have completed equivalent study at another university, please contact a Student Advisor for advice.

  • Broadband access
  • Other special requirement — You will require access to a recent version of SPSS - at least the SPSS Grad Pak.

In order to enrol in this unit, you must be accepted into one of the following courses:

Please visit the course details page and read the Requirements tab for more information about eligibility.

This unit addresses the following topics.

1Multiple regression
2Multivariate analysis of variance
3Factor analysis
4Discriminant function analysis
5Logistic regression
6Bivariate regression
7Data screening and data modification
8Understanding partial and part correlation coefficients
9Presentation of results, testing assumptions and other practical issues
10Further testing of mediated and moderated models in regression
11Steps involved in factor analysis and using SPSS to perform factor analysis
12Constructing scales
13Refining a factor analysis
14Review of ANOVA
15Single factor between-subjects MANOVA
16Performing factor analysis
17Completely randomised factorial MANOVA
18Single-factor with-subjects MANOVA
19Mixed design MANOVA
20Assumptions underlying MANOVA
21Classification methods: Logistic regression, discriminant analysis

This unit is delivered using the following methods and materials:

Instructional Methods

  • Discussion Forum/Discussion Board
  • Online Quizzes/Tests
  • Online assignment submission
  • Web links

Print based materials

  • Welcome Letter

Online materials

  • Printable format materials

This unit is a core requirement in the following courses:

This unit may be eligible for credit towards other courses:

  1. Many undergraduate courses on offer through OUA include 'open elective' where any OUA unit can be credited to the course. You need to check the Award Requirements on the course page for the number of allowed open electives and any level limitations.
  2. In other cases, the content of this unit might be relevant to a course on offer through OUA or elsewhere. In order to receive credit for this unit in the course you will need to supply the provider institution with a copy of the Unit Profile in the approved format, which you can download here. Note that the Unit Profile is set at the start of the year, and if textbooks change this may not match the Co-Op textbook list.

Textbook information for this unit is currently being updated and will be available soon. Please check back regularly for updates. Alternatively, visit the The Co-op website and enter the unit details to search for available textbooks.

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