I__. - International Military Testing Association

A Suite of Advanced Interpretive Assistance Programs
A set of seven programs has evolved gradually over the last few years which are meant to
assist analysts in interpreting job and task clusters; some of these were completed in time to
be released with the initial version of ASCII CODAP. Others are still being refined and thus
are not yet ready for operational use. It is helpful to have an overview of the entire set of
programs, so everyone can see how the programs relate to one another and to their ultimate
objective. These programs are shown in Figure 1 below.
Identify Appropriate Clusters
IdenNy/Display Core Tasks
IdenIify/I>isplay Core C&es
Relationship of Task Clusters
lo Job Clusters
Case Cl uslers Task Cl usfers
(Job Types) (Task Modules)
JOBTYP MODT YP
I I
CORTAS TASSET
I
CASSE T
I
CORCAS
JOBMOD
Figure 1. The Set of Advanced Interpretive Assistance Programs
(Boldface = operational program in ASCII CODAP; Ifafic = experimental, not yet released).
The operational programs are briefly described as follows:
JOBTYP automatically identifies stages in most branches of a hierarchical clustering
DIAGRM which represent the “best” candidates for job types. First, core task homogeneity,
task discrimination, a group size weight, and a loss in “between” overlap for merging stages
are calculated for all stages and these values are used to compute an initial evaluation value
(for JOBTYP equations, see Haynes, 1989). This value is used to pick three sets of initial
stages; these are then inserted into a super/subgroup matrix for additional pairwise
evaluation, in order to further refine the selection of candidate job type groups Three final
sets of stages (primary, secondary, and tertiary groups) are then reported for the analyst to
use as starting points for selecting final job types.
CORTAS compares a set of group job descriptions (“contextual” groups) in terms of
number of core tasks performed, percent members performing and time spent on each core
case, and the ability of each core task to discriminate each group from all other groups in
the set. It also computes for each group an overall measure of within-group overlap called
the “core task homogeneity index”, an overall measure of between-group difference called the
“index of average core task discrimination per unit of core task homogeneity”, and an
asymmetric measure of the extent to which each group in the set qualifies as a subgroup or
supergroup of every other group in the set.
TASSET compares clusters of tasks (modules) in terms of the degree to which each cluster
of tasks is co-performed with every other task cluster (supergroup/subgroup matrix). Within
each cluster, TASSET computes the average co-performance of each task with every other
task in the cluster (representativeness index) and the difference in average co-performance
of the same tasks with all other task clusters (discrimination index). TASSET also identifies
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