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Advanced Design and Data Analysis (PSYC40005)

HonoursPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1 (Early-Start)
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This subject provides an introduction to multivariate data analysis in the behavioural and social sciences, including the nature, rationale and application of a number of widely used multivariate data analysis models. For each model, issues covered include the nature of the model and its assumptions; situations in which the model might be applied; diagnostics for model adequacy; estimation and inference; interpretation; the use of statistical software for model-fitting. Models will be selected from multiple regression; logistic regression; an introduction to path analysis and structural equation modelling; multivariate analysis of variance and discriminant analysis; multilevel models; principal components analysis and factor analysis; models for multivariate categorical data; cluster analysis and multidimensional scaling.

The first two lectures/tutorials of the subject will be taught on one day (six hours) in Orientation Week, thereby allowing students time to work on assessment tasks at the beginning of the semester.

Intended learning outcomes

Upon completion of the subject, students should demonstrate the following competencies in psychology:

Knowledge

 

Students should demonstrate knowledge of:

  • The forms of major multivariate techniques including multivariate analysis of variance and variants, multilevel models, methods for categorical data analysis and structural equation modelling;
  • The role of, and methods for, exploratory analysis of multivariate observations such as factor analysis, multidimensional scaling, and clustering;
  • The appropriate analysis tool required for a particular dataset.

Skills


On completion of the subject students should be able to:

  • Execute complex multivariate methods for data analysis;
  • Explore and visualize data using clustering and multidimensional scaling;
  • Reduce complex data to a set of interpretable factors using principal components analysis and factor analysis.

Application of knowledge and skills

 

On completion of the subject, students should be able to apply their knowledge and skills to: 

  • Design research studies requiring complex quantitative observations;
  • Apply major multivariate techniques to large datasets with psychological variables
  • Critically evaluate and interpret complex quantitative information.

Generic skills

On completion of this subject, students should have a greater ability to:

  • present and analyse complex quantitative information;
  • synthesize, interpret, and communicate information in ways that others can understand
  • think critically about which tools are required for different types of problems
  • critically evaluate assumptions, advantages, and limitations of different analytic techniques

Last updated: 9 February 2024

Eligibility and requirements

Prerequisites

Admission into or selection of one of the following:

  • GDA-PSYCH Graduate Diploma in Psychology (Advanced)
  • MC-AP Master of Applied Psychology
  • MC-PROPSYC Master of Professional Psychology
  • Psychology specialisation (formal) in the BH-ARTS Bachelor of Arts (Degree with Honours)
  • Psychology informal specialisation in the BH-BMED Bachelor of Biomedicine (Degree with Honours)
  • Psychology informal specialisation in the BH-SCI Bachelor of Science (Degree with Honours)

Corequisites

None

Non-allowed subjects

None

Recommended background knowledge

An accredited psychology major sequence

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 9 February 2024

Assessment

DescriptionTimingPercentage

A written report of no more than 1000 words

  • 1000 words
Early in the teaching period20%

A written report of no more than 1500 words

  • 1500 words
Mid semester30%

And an examination of no more than two hours

  • 2 hours
During the examination period50%
Hurdle requirement: Each piece of assessment must be completed. Attendance at 80% or more of the laboratory classes is a hurdle requirement. In case of failure to meet the hurdle requirement, additional work related to the missed class activities (e.g., short 500 word essay on missed topic) will be required before a passing grade can be awarded.Throughout the teaching periodN/A

Last updated: 9 February 2024

Dates & times

  • Semester 1 (Early-Start)
    Principal coordinatorAndrew Legg
    CoordinatorGeoff Saw
    Mode of deliveryOn Campus (Parkville)
    Contact hoursSix-hour workshop day in Orientation Week; 24 hours of lectures, 12 hours of laboratory classes.
    Total time commitment170 hours
    Teaching period23 February 2024 to 26 May 2024
    Last self-enrol date 1 March 2024
    Census date 3 April 2024
    Last date to withdraw without fail 3 May 2024
    Assessment period ends21 June 2024

    Semester 1 (Early-Start) contact information

    Principal Coordinator:

    Andrew Legg
    andrew.legg1@unimelb.edu.au

    Coordinator:

    Geoff Saw
    gsaw@unimelb.edu.au

Time commitment details

Estimated total time commitment of 170 hours.

What do these dates mean

Visit this webpage to find out about these key dates, including how they impact on:

  • Your tuition fees, academic transcript and statements.
  • And for Commonwealth Supported students, your:
    - Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).

    Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.

Last updated: 9 February 2024

Further information

Last updated: 9 February 2024