Classification results and coefficients for the eight-variable classification function for the logit and multiple discriminant analysis models one year prior to insolvency are presented in Table 1.
As in BarNiv and Hershbarger (1990), the sign and the magnitude of the various coefficients differ across the logit and multiple discriminant analysis models.
Table 2 Multiple Discriminant Analysis
Summary Equivalent Step Variable Entered Wilk's Lambda F P 1 Attitude Toward Employment .
The traditional approach and present standard for predicting financial distress uses multiple discriminant analysis
(MDA) to weight the relative value of information provided by a combination of financial ratios.
A multiple discriminant analysis
is a statistical procedure that is frequently used to distinguish (discriminate) between two or more populations on the basis of observations on several variables.
For example, Trieschmann and Pinches (1973) report that their multiple discriminant analysis
(MDA) model correctly classifies 92 percent of insolvent insurers and 96 percent of solvent firms two years prior to the determination of insolvency or solvency; later studies report correct classifications ranging from 62 to 100 percent.
The data were analyzed by multiple discriminant analysis
using GATB and WAIS scores as predictors and actual vocational placement levels as criterion.
Discriminant Analysis: A multiple discriminant analysis
and a paired case-control methodology was used to assess the differences in the financial characteristics of restructuring and non-restructuring firms as a further test of economic performance.