quantitative analysis
Chapter 25
Discriminant Analysis
Content list
Purposes of discriminant analysis
Discriminant analysis linear equation
Assumptions of discriminant analysis
SPSS activity – discriminant analysis
Stepwise discriminant analysis
589
590
590
593
604
When you have read this chapter you will understand:
1 The purposes of discriminant analysis.
2 How to use SPSS to perform discriminant analysis.
3 How to interpret the SPSS print out of discriminant analysis.
Introduction
This chapter introduces another extension of regression where the DV may have more than two conditions at a categorical level and IV’s are scale data.
The purposes of discriminant analysis (DA)
Discriminant Function Analysis (DA) undertakes the same task as multiple linear regression by predicting an outcome. However, multiple linear regression is limited to cases where the dependent variable on the Y axis is an interval variable so that the combination of predictors will, through the regression equation, produce estimated mean population numerical
Y values for given values of weighted combinations of X values. But many interesting variables are categorical, such as political party voting intention, migrant/non-migrant status, making a profit or not, holding a particular credit card, owning, renting or paying a mortgage for a house, employed/unemployed, satisfied versus dissatisfied employees, which customers are likely to buy a product or not buy, what distinguishes Stellar Bean clients from
Gloria Beans clients, whether a person is a credit risk or not, etc.
590
EXTENSION CHAPTERS ON ADVANCED TECHNIQUES
DA is used when:
•
•
the dependent is categorical with the predictor IV’s at interval level such as age, income, attitudes, perceptions, and years of education, although dummy variables can be used as predictors as in multiple regression. Logistic regression IV’s can be of any level of measurement. there are more than two DV categories, unlike
Discriminant Analysis
Content list
Purposes of discriminant analysis
Discriminant analysis linear equation
Assumptions of discriminant analysis
SPSS activity – discriminant analysis
Stepwise discriminant analysis
589
590
590
593
604
When you have read this chapter you will understand:
1 The purposes of discriminant analysis.
2 How to use SPSS to perform discriminant analysis.
3 How to interpret the SPSS print out of discriminant analysis.
Introduction
This chapter introduces another extension of regression where the DV may have more than two conditions at a categorical level and IV’s are scale data.
The purposes of discriminant analysis (DA)
Discriminant Function Analysis (DA) undertakes the same task as multiple linear regression by predicting an outcome. However, multiple linear regression is limited to cases where the dependent variable on the Y axis is an interval variable so that the combination of predictors will, through the regression equation, produce estimated mean population numerical
Y values for given values of weighted combinations of X values. But many interesting variables are categorical, such as political party voting intention, migrant/non-migrant status, making a profit or not, holding a particular credit card, owning, renting or paying a mortgage for a house, employed/unemployed, satisfied versus dissatisfied employees, which customers are likely to buy a product or not buy, what distinguishes Stellar Bean clients from
Gloria Beans clients, whether a person is a credit risk or not, etc.
590
EXTENSION CHAPTERS ON ADVANCED TECHNIQUES
DA is used when:
•
•
the dependent is categorical with the predictor IV’s at interval level such as age, income, attitudes, perceptions, and years of education, although dummy variables can be used as predictors as in multiple regression. Logistic regression IV’s can be of any level of measurement. there are more than two DV categories, unlike