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2003 IMTA Proceedings - International Military Testing Association

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In next- rather busy – graph the average payoff is given for the different entries. What is<br />

important to note for the moment is that the curvilinear relationship shown in figure 2<br />

does not seem to apply to all entries.<br />

Average payoff of assigned applicants<br />

1000<br />

900<br />

800<br />

700<br />

600<br />

500<br />

400<br />

Average Payoff of Enlisted Persons per Trade<br />

as a Function of Classification Frequency<br />

300<br />

1 6 12 20 30 60<br />

Number of subsets processed simultaneously<br />

Figure 5<br />

Discussion<br />

To begin with, it is clear that the conditions processing larger numbers of subsets<br />

simultaneously yield better average payoffs than the ones processing smaller numbers.<br />

The magnitude of the difference might seem rather small upon first sight. The difference<br />

between the first and last condition is about 45 points on a scale with an average of 500<br />

and a standard deviation of 200. Yet, given the fact that in all conditions the same<br />

applicant pool and the same vacancies were used along with the same eligibility rules and<br />

the same classification tool and considering that the difference pertains to the average of<br />

more than 1500 persons, one should realize that the effect is quite important. Put in other<br />

words, if you would think of measures that need to be taken to yield a similar increase of<br />

average recruit quality, such as increasing the selection ratio or improving the applicant<br />

pool quality through recruiting actions, you most probably would conclude that waiting<br />

some time before classifying the applicants is a very cheap and effective option.<br />

Secondly, it is quite interesting to notice the curvilinear relationship between average<br />

payoff and number of subsets processed simultaneously. The relationship approximates a<br />

logarithmic function. The steepest increase in payoff occurs at the left side of the abscissa<br />

and then flattens out.<br />

When looking at the second and third graph, we see very similar relationships. This<br />

means that both aptitude and preference benefit from classification in larger groups.<br />

J1<br />

J2<br />

J3<br />

J4<br />

J5<br />

J6<br />

J7<br />

J8<br />

J9<br />

J10<br />

J11<br />

J12<br />

J13<br />

J14<br />

J15<br />

J16<br />

J17<br />

J18<br />

J19<br />

J20<br />

J21<br />

J22<br />

J23<br />

J24<br />

J25<br />

J26<br />

J27<br />

373<br />

45 th Annual Conference of the <strong>International</strong> <strong>Military</strong> <strong>Testing</strong> <strong>Association</strong><br />

Pensacola, Florida, 3-6 November <strong>2003</strong>

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