Discriminant Analysis
During a study, researchers often have questions that they must answer. These questions include questions like ‘are the groups different?’, ‘on what variables, are the groups most different?’, ‘can one predict which group a person belongs to using such variables?’ etc. In answering such questions, discriminant analysis is quite helpful.
Researchers use discriminant analysis to analyze research data when the dependent variable is categorical and the independent variable is interval in nature. The term categorical variable means that researchers divide the dependent variable into several categories. For example, three brands of computers, Computer A, Computer B and Computer C can be the categorical dependent variable.
Discriminant analysis aims to create functions that use independent variables to distinguish between categories of the dependent variable. It enables the researcher to examine whether significant differences exist among the groups, in terms of the predictor variables. It also evaluates the accuracy of the classification.
Discriminant analysis describes the number of categories that the dependent variable possesses.
In statistics, we assume everything extends to infinity. So, when the dependent variable has two categories, we use two-group discriminant analysis. If the dependent variable has three or more than three categories, then the type used is multiple discriminant analysis. The major distinction to the types of discriminant analysis is that for a two group, it is possible to derive only one discriminant function. In multiple discriminant analysis, you can compute more than one discriminant function. For a researcher, it is important to understand the relationship of discriminant analysis with Regression and Analysis of Variance (ANOVA) which has many similarities and differences.
Often we can find similarities and differences with the people we come across. Similarly, there are some similarities and differences with discriminant analysis along with two other procedures. The similarity is that the number of dependent variables is one in discriminant analysis and in the other two procedures, the number of independent variables are multiple in discriminant analysis. The difference is categorical or binary in discriminant analysis, but metric in the other two procedures. The nature of the independent variables is categorical in Analysis of Variance (ANOVA), but metric in regression and discriminant analysis.
The steps involved in conducting discriminant analysis are as follows:
1. Researchers formulate the problem before conducting the analysis.
2. They estimate the discriminant function coefficients.
- 3. Next, they determine the significance of these discriminant functions.
- 4. They interpret the results obtained.
- 5. The final and most important step is assessing the validity.