Discriminant analysis builds a predictive model for group membership. The model is composed of a discriminant function (or, for more than two groups, a set of. Chapter 6 Discriminant Analyses. SPSS – Discriminant Analyses. Data file used: In this example the topic is criteria for acceptance into a graduate. Multivariate Data Analysis Using SPSS. Lesson 2. MULTIPLE DISCRIMINANT ANALYSIS (MDA). In multiple linear regression, the objective is to model one.

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Linear discriminant function analysis i. In addition, discriminant analysis is used to determine the minimum number of dimensions needed to describe these differences.

Discriminant Function Analysis | SPSS Data Analysis Examples

A distinction is sometimes made between descriptive discriminant analysis and predictive discriminant analysis. We will be illustrating predictive discriminant analysis on this page.

The purpose of this page is to show how to use various data analysis commands. It does not discriminane all aspects of the research process which researchers are expected to do.

Discriminant Analysis | SPSS Annotated Output

In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics or potential analyde analyses. A large international air carrier has collected data on employees in three different job classifications: The director of Human Resources wants to know if these three job classifications appeal to different personality types.


Each employee is administered a battery of psychological test which include measures of interest in outdoor activity, sociability and conservativeness. Fisher not only wanted to determine if the varieties differed significantly on the four continuous variables, but he was also interested in predicting variety classification for unknown individual plants.

We have included the data file, which can be obtained by clicking on discrim.

The dataset has observations on four variables. The psychological variables are outdoor interestssocial and conservative.

The categorical variable is job type with three levels; 1 customer service, 2 mechanic, and discrimknante dispatcher. It is always a good idea to start with descriptive statistics. Below is a list of some analysis methods you may have encountered.

Some of the methods listed are quite reasonable, while others have either fallen out of favor or have limitations. Note that the Standardized Canonical Discriminant Function Coefficients table and the Structure Matrix table are listed in different orders. The standardized discriminant coefficients function in a manner analogous to standardized regression coefficients in OLS regression.

For example, a one standard deviation increase on the outdoor variable will result in a. Next, we will plot a graph of individuals on the discriminant dimensions. Due to the large number of subjects we will shorten the labels for the job groups to make the s;ss more legible.

As you can see, the customer service employees tend to be at the more social negative end of dimension 1; the dispatchers tend to be discriminaante the opposite end, with the mechanics in the middle. On dimension 2 the results are not as clear; however, the mechanics tend to be higher on the outdoor dimension and customer service employees and dispatchers lower.


The territorial map is shown discrimiannte. Things to consider Multivariate normal distribution assumptions holds for the response variables.

This means that each of the dependent variables is normally distributed within anqlyse, that any linear combination of the dependent variables is normally distributed, and that all subsets of the zpss must be multivariate normal. Each group must have a sufficiently large number of cases. Different classification methods may be used depending on whether the variance-covariance matrices are equal or very similar across groups.

Dlscriminante discriminant function analysis, called k th nearest neighbor, can also be performed. See also SPSS annotated output: Discriminant analysis References Grimm, L. Reading and Understanding Multivariate Statistics. John Wiley and Sons, Inc. Lawrence Erlbaum Associates, Inc. Techniques for Educational and Psychological Research. John Wiley and Sons.