CHAPTER ONE

INTRODUCTION

1.1     DISCRIMINANT ANALYSIS

 Discriminant Analysis or D.A is a multivariate technique used to classify cases into distinct groups. It separates distinct sets of objects (or observations) and allocates new objects (or observations) to previously defined groups. Discriminant analysis is concerned with the problem of classification, which arises when a researcher having made a number of measurements on an individual, wishes to classify the individual into one of several categories on the basis of these multivariate measurements (Onyeagu, 2003).

 Discriminant analysis will help us analyze the differences between groups and provide us with a means to assign or classify any case into the groups which it most closely resembles. 

          There are two aspects of discriminant analysis, 

1.                 Predictive Discriminant Analysis (PDA) or Classification, which is concerned with classifying objects into one of several groups and

2.                 Descriptive Discriminant Analysis (DDA) which focused on

revealing major differences among the groups (Stevens 1996). 

According to Huberty (1994), Descriptive discriminant analysis includes the collection of techniques involving two or more criterion variables and a set of one or more grouping variables, each with two or more levels. “Whereas in predictive discriminant analysis (PDA) the multiple response variables play the role of predictor variables. In descriptive discriminant analysis (DDA) they are viewed as outcome variables and the grouping variable(s) as the explanatory variable(s). That is, the roles of the two types of variables involved in a multivariate multigroup setting in DDA are reversed from the role in PDA.

1.2     STEPWISE DISCRIMINANT ANALYSIS      

 A researcher may wish to discard variables that are redundant (in the presence of other variables) when a large number of variables are available for groups separation. Here (in discriminant analysis), variables (say y’s) are selected and, the basic model does not change. Unlike regression, where independent variables are selected and consequently, the model is altered.  Stepwise selection is a combination of forward and backward

variables selection methods. In forward selection, the variable entered at each step is the one that maximizes the partial F-Statistic based on Wilks’Ù.

The maximal additional separation of groups above and beyond the separation already attained by the other variables is thus obtained. The proportion of these F’s that exceed Fα is greater than α. While in backward selection (elimination), the variable that contributes least is deleted at each step as shown by the partial F.

 The variables which are selected one at a time, and at each step, are re-examined to see if any variable that entered earlier has become redundant in the presence of recently added variables. When the largest partial F among the variables available for entry fails to exceed a preset threshold value, the procedure stops.

 Stepwise discriminant Analysis is a form of discriminant analysis. During the selection process no discriminant functions are calculated. However, after the completion of the subset selection, discriminant function is calculated for the selected variables. These variables can also be used in the construction of classification functions.   

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