512-422 Advanced Design and Data Analysis

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.

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.

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.