Discriminant analysis: An illustrated example

One of the challenging tasks facing a researcher is the data analysis section where the researcher needs to identify the correct analysis technique and interpret the output that he gets. The analysis wise is very simple, just by the click of a mouse the analysis can be done. The more demanding part is the interpretation of the output that the researcher gets. Many researchers are very familiar and well exposed to the regression analysis technique whereby the dependent variable is a continuous variable. But what happens if the dependent variable is a nominal variable? Then the researcher has 2 choices: either to use a discriminant analysis or a logistic regression. Discriminant analysis is used when the data are normally distributed whereas the logistic regression is used when the data are not normally distributed. This paper demonstrates an illustrated approach in presenting how the discriminant analysis can be carried out and how the output can be interpreted using knowledge sharing in an organizational context. The paper will also present the 3 criteria that can be used to test whether the model developed has good predictive accuracy. The purpose of this paper is to help novice researchers as well as seasoned researchers on how best the output from the SPSS can be interpreted and presented in standard table forms.