I__. - International Military Testing Association

where:
A = (A,, . . . . A,) , B = (B,, . . . . B,) , l- = (l-,, . . . . I?,) , and A = (A,, . . . . A)
are vectors of regression coefficients. The RZ for this model is .350. This is considerably smaller than the R* z
.59 achieved when intercepts were completely unconstrained. This suggests that the job characteristic factor scores
explain a portion, but by no means all, of the variability in the job specilic intercepts.
The Neural Network Approach
Having witnessed the rather large degradation in variance explained between Model I and Model II, i.e.,
R2=.595 to R*=.350, a neural network paradigm was investigated. Once the candidate explanatory variables were
determined from the second model, the next step was to construct a neural network capable of analyzing the problem.
Actual construction involved the following five steps which specified:
o network type
o number of nerodes in the output and hidden layers
o training and cross validation gamples
o transfer function at each layer and global error function
o scaling, learning, momentum, epoch size parameters
Network Architecture
Since the problem involved a (hetero-associative) mapping of continuous, dichotomous, and polytomous
explanatory variables to a bounded continuous criterion measure of hand-on-performance, a forward-feed backward
error propogation network was chosen.
Ostensibly, the single output “neurode” was hand-on-performance test score. Because the number of neurodes
in the hidden-layer of the feedforward network determines the complexity of the function the network is capable of
mapping, 26 was determined to yield a sufficiently complex network. Notably, it has been shown that any
continuous function or ”.,.mapping can be approximately realized by Rumelhart-Hinton-Williams’ multilayer neural
network with at least one hidden layer whose output functions are sigmoid functions (Funahashi, 1989; Homik,
1989).”
The data were randomly split 60140 into two sets. The first (N=5,078) was used to train the network and the
second (N=3,386) was used to validate the network. The transfer function for the output neurode was logistic, while
the transfer functions for the hidden neurodes were hyperbolic. Although any error function which is continuously
differentiable could have been used, we selected the squared deviation between the observed and prcdictcd output
values. Graphically, the network is shown in Figure 1 below.
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