Advanced Design and Data Analysis

Subject PSYC40005 (2010)

Note: This is an archived Handbook entry from 2010.

Credit Points: 12.50
Level: 4 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2010:

Semester 1, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable


Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: Twenty-four hours of lectures, 12 hours of laboratory classes. [Estimated total time commitment of 120 hours.]
Total Time Commitment: Not available
Prerequisites: No prerequisites are required for this subject
Corequisites: No corequisites are required for this subject
Recommended Background Knowledge: An accredited psychology major sequence
Non Allowed Subjects: There are no non allowed subjects
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards of Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry.

The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit Website: http://www.services.unimelb.edu.au/disability/

Coordinator

Assoc Prof Richard Bell

Contact

Psychology

12th floor Redmond Barry Building (Building 115 Map)

Telephone: + 61 3 8344 6377

Email: enquiries@psych.unimelb.edu.au

Web: http://psych.unimelb.edu.au

Subject Overview:

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 the software package SPSS for model-fitting. Models will be selected from multiple regression; logistic regression; an introduction to path analysis; multivariate analysis of variance and discriminant analysis; principal components analysis and factor analysis; models for multivariate categorical data; cluster analysis and multidimensional scaling.

Objectives:

This subject aims to:

  • develop an appreciation of the role and methods for exploratory analysis of multivariate observations such as factor analysis; and multidimensional scaling and clustering
  • develop an understanding of the forms and application of some major multivariate techniques including multivariate analysis of variance and variants, methods for categorical data analysis and structural equation modelling
  • develop a critical understanding of multivariate methods for data analysis, particularly in relation to applicability, interpretation and inference
  • develop skill in the use of the statistical software program SPSS/PASW for multivariate analysis
Assessment:

A written report of no more than 1500 words (40%), and an examination of no more than two hours (60%).

Each piece of assessment must be completed (hurdle requirement).

Attendance at 80% or more of the laboratory classes is a hurdle requirement. In case of failure to meet the hurdle requirement, additional work will be required before a passing grade can be awarded.

Prescribed Texts: There are no prescribed texts
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

On completion of this subject, students should have a greater ability to: design research studies requiring complex quantitative observations; present and analyse complex quantitative information; and critically evaluate and interpret complex quantitative information.

Related Course(s): Bachelor of Arts (Honours) in Psychology

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