I__. - International Military Testing Association

anged from 62% (908X0) to 89% (112X0); however, the second
smallest CA was 71% (321XlE). In comparison, the CA's at the 26group
stage ranged from 71% (908X0) to 90% (112X0), with the
second smallest CA being 72% (231X0). Therefore, the range of
the CA's doesn't change much between the two extremes of the
clustering process. At the 26-group stage, only 2 "A" tasks were
classified as "Dw tasks and only 2 "D" tasks were classified as
ltA1l tasks. With the exception of one AFS (908X0), where six 'ID"
tasks were classified as IlA" tasks and one "A" task was classfied
as a "D" task, there were only three "A" tasks classified as "D"
tasks and two "DM tasks classified as "A" tasks over all AFSs at
the l-group stage.
A Wilcoxon matched-pairs signed-ranks test (Siegel, 1956)
was used to compare the differences in CA's between the the 26group
stage and the 5-group stage and between the 5-group stage
and the l-group stage. There was a statistically significant
difference (d=. 05) between the 26-group and 5-group stages, but
not between the 5-group and l-group stages. Although
significantly better classifications result from the use of 26
eqUatiOnS, 20 Of 26 AFSs had differences Of 5% or 1eSS (maX=13%).
If generalized equations are to be used to classify tasks in
other AFSs where TI data are not available, it appears promising
that a single prediction equation could generate adequate testing
importance values. Further analyses are being conducted to
identify the highest and lowest stages that are significantly
different from the 26-group and l-group stages, respectively.
CONCLUSIONS
A large amount of the variance in TI was accounted for by
linear combinations of the task-level predictors. The stability
of least squares weights within each of the 26 AFSs was
demonstrated. Prediction equations adequately classified tasks
according to testing importance with very few A to D or D to A
misclassifications. Use of squared and interactive predictor
terms added little to predictive efficiency. A hierarchical
clustering of the regression equations developed for each AFS
showed small decreases in predictive efficiency throughout most
of the clustering process. Preliminary results indicate that a
single prediction equation may do an adequate job of classifying
tasks on testing importance across all AFSs.
REFERENCES
Lindguist, E. F. (1953). Design and analysis of exneriments in
psvcholoav and education. Boston: Houghton Mifflin Company.
Walker, H. M. & LeV, J. (1953). Statistical inference. New York:
Henry Halt and Company.
Siegel, S. (1956). Nonnarametric statistics for the behavioral
sciences. New York: McGraw-Hill.
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