Utilising your categorical data

Target Audience:
Ideal for those wishing to effectively analyse the categorical information available within their data.

Prerequisites:
PC Literate. Familiarity with SPSS, including variable definition, opening and saving data files, generation of basic exploratory statistics. The understanding of Central Tendency, Dispersion and Hypothesis Testing (including the t-test) is an essential prerequisite. Familiarity with Factor Analysis and the principles and methodology behind Linear Regression is preferred but not essential.

Overview:
The course is designed for those who wish to examine further and make predictions about new or future cases from their categorical data. To this end, the related techniques of Conjoint and Correspondence Analysis, CHAID and Logistic Regression will be assessed and their different applications examined.

Objectives:
By the end of the course you will have learnt:
• The underlying assumptions and types of data required for each technique.
• The similarities and differences between these techniques
• When to use each technique and how to apply them using SPSS products
• How to interpret the results
• Know how well the technique is doing with your data
• Understand how to apply predictive models to new data

Duration:
1 day

Course Content:
Following an introduction to the principles of Categorical Analysis, you will proceed logically through the following topics:

Correspondence Analysis:
• Introduction to Correspondence Analysis
• Production of Perceptual Maps
Conjoint Analysis:
• Introduction to Conjoint Analysis
• Generating scorecards
• Interpreting results
Regression Analysis:
• Principles of Logistic Regression
• Principles of Ordinal Regression
• Principles of Multinominal Logistic Regression
Discriminant Analysis:
• Introduction to Discriminant Analysis
• Understanding fit and predictive accuracy
Tree Based Analysis:
• Introduction to Tree based Analysis
• Running CHAID analysis in AnswerTree