Advanced Design and Data Analysis

This subject has the following teaching availabilities in 2015:

Estimated total time commitment of 170 hours.

No prerequisites are required for this subject

No corequisites are required for this subject

An accredited psychology major sequence

There are no non allowed subjects

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/

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 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.

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, multilevel models, 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 for multivariate analysis

A written report of no more than 1000 words (20%) to be submitted early in the semester.

A written report of no more than 1500 words (30%) to be submitted in mid-semester.

And an examination of no more than two hours (50%) during the examination period.

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.

There are no prescribed texts

This subject is not available as a breadth subject.

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.