I__. - International Military Testing Association

year career. Instead, CODAP provides information about what all
airmen are doing in groups such as first term, second term , or
career enlistees. Also, the survey is administered.to essentially
100 percent of the career field and produces a response
rate of over 80 percent.
Event History Analysis
Event history analysis enables the researcher to determine
probabilities associated with the length of time for a binary,
dependent variable to change states. Another requirement is
knowledge of the time from the start of the experiment to the
change in state of the dependent variable. Both the origin time
and the exact point at which the dependent variable changes must
be precisely defined. Also, the length of time must always be a
positive value. The last assumption is that the sample should be
homogeneous (Cox & Oakes, 1984).
One of the strengths of event history analysis is the ability
to include some information concerning censored data. An item is
considered to be censored if it is removed from the sample before
the experiment is terminated and the dependent variable has not
changed states. A second type of censoring occurs if the experiment
ends before the dependent variable changes. In most parametric
statistical analyses, such data would have to be omitted
from the sample. However, the fact that the item had not changed
at the point of leaving or ending the experiment can provide some
relevant information that should be incorporated into probabilities
associated with the time at which the dependent variable
changes states.
Several probabilities are associated with event history analysis.
The failure and the survival functions represent cumulative
distributions about when the dependent variable changes
states. Failure is defined as the change in the dependent variable;
survival is the lack of change. The hazard function represents
the conditional probability that the dependent variable
will change states in a specific time period, given that it had
not changed states in the previous period (Kalbfleisch & Prentice,
1980). The mean life residual function represents the
average length that the dependent variable will survive beyond
the specified time period (Oakes & Desu, 1990).
All of these functions are related mathematically. E.g.,
once the survival curve is estimated, the mean life residual can
be determined. The most widely used method for computing the
survival function is the product limit estimator proposed by
Kaplan and Meier (1958).
Method
At first glance, event history analysis does not seem appropriate
for examining Air Force occupational data. One problem is
that the Air Force maintains little data on persons who leave the
service. Also, the actual time that a person stops doing a specific
task is not recorded. However, data gathered by occupational
surveys do meet the required assumptions.
Event history requires that the dependent variable be
binary. For task perishability, this translates to whether or
not a task is being performed. In an occupational survey,
respondents check if they are performing a task; thus, task performance
is known.
The second assumption of event history is that the origin and
139