I__. - International Military Testing Association

___r-.--r--...
.-.--.- --
I- _ - _--._ ..-. .~
exact point at which a job leaves a person's inventory must be
specified. Actually, however, the only information that is necessary
is the length of time that a person holds a specific task
in the job inventory. To meet this requirement, a small mental
transformation of the data is necessary. Occupational surveys
provide information on the percent members performing a task in
each time interval. The difference in percent member performing
over two intervals is in essence a measure of those who have
stopped doing a task. Therefore, occupational survey data meets
the two primary assumptions of event history analysis. However,
a problem is the inclusion of the censored data. While the Air
Force does have information regarding AFSC attrition rates,
whether the specific task is in an airman's inventory when he
leaves the service (attrites) is unknown.
For the purposes of this study, we generated a 1000 person
data base. This data base included actual data points for a task
leaving an airman's job inventory, as well as censored data,
which simulated those airmen who leave the Air Force prior to the
task leaving their inventory. While this model is not specific
to any career field, it does incorporate several facts which are
intrinsic to the job/career development in the Air Force. For
instance, many airmen spend up to 12 months in training before
actually being assigned to a work place. Thus, this model starts
simulating at the thirteenth month, which is actually the first
point in time that a task could leave an incumbent's inventory.
Another consideration is the large change in status at the 48th
month. At this point many airmen leave the service: of those who
do continue in the Air Force, some change career fields. This
change results in many censored data points at the 48th month.
In summary, single task performance data for an initial set
of 1000 airman was simulated over a 6 year (72 month) period.
Using the type of data available from Occupational Survey
Reports, percent members performing for each month interval were
created. Censoring was also generated for this simulation.
Although exact censoring data cannot be determined from current
Air Force data bases, historical attrition data are available.
The censored data values can then be estimated from the attrition
data using the information from current percent members performing
a single task. A total of 300 (30%) censored data points
were inserted into the data base using a random number procedure.
From this simulated data base, three functions were calculated:
the Survival function, the Hazard function, and the Mean
Life Residual function. All calculations were performed using
the Lifetest procedure in SAS. Examples of the survival and the
mean‘life residual functions are given in this paper.
Results
Figure 1 shows the survival function for the simulated data
base. It represents the probability of an airman at a specific
time period performing the task. For example, at the 36th month,
the probability of an airman still performing this task is .54.
Figure 2 represents the mean life residual function for the
simulated data base. This function can be interpreted as the
average length that an airman will be performing the task beyond
a specific time period. At the 36th month, on average, an airman
will be performing this task 13.8 more months.
140
.