Session17&18_Group D

Discriminant Analysis

The aim of the statistical analysis in DA is to combine (weight) the variable scores in some way so that a single new composite variable, the discriminant score, is produced. One way of thinking about this is in terms of a food recipe, where changing the proportions (weights) of the ingredients will change the characteristics of the finished cakes. Hopefully the weighted combinations of ingredients will produce two different types of cake. Similarly, at the end of the DA process, it is hoped that each group will have a normal distribution of discriminant scores. The degree of overlap between the discriminant score distributions can then be used as a measure of the success of the technique, so that, like the different types of cake mix.

Discriminant Analysis is used primarily to predict membership in two or more mutually exclusive groups.

This menu selection opens the following dialog box:

First enter the grouping variable (here: variable category). Then, define the lowest and highest coded value for the grouping variable by clicking on Button _ Define Range. As the variable category has only two levels you enter 1 and 2 in the boxes. See the second figure standing above.

Then, select the independent variables (choose gender, motive and stable) in the ‘Independents:’ box. There are several methods for discriminant analyses, but here we will only use ‘Enter independents together’, which is standard selected.

Button _ Statistics : Here you can indicate those statistics that are desired in discriminant analysis. Often these include:

Means: The means and standard deviations for each variable for each group (the two levels of category in this case)

Univariate ANOVAs: This compares the mean values for each group for each variable to see if there are significant univariate differences between means.

Box’s M: A test for the equality of the group covariance matrices. For sufficiently large samples, a       non-significant p value means there is insufficient evidence that the matrices differ. The test is sensitive to departures from multivariate normality.

Unstandardized Function Coefficients: The unstandardized coefficients of the discriminant equation

based on the raw scores of discriminating variables.

Button _ Classify : Many classification options can be selected here, such as prior probabilities and plots. Also, a summary table can be requested.

Button _ Save :  This option allows to save as new variables: Predicted group membership, Discriminant Scores and Probabilities of group membership.

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Rohit Jayant

Operation 

14160

Group D