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EVENT HISTORY ANALYSIS 225
may be possible to predict the survival rate of
that population at time t + 1. In a sense it
is akin to a prediction study. Life-table studies
are straightforward in that they are concerned
with specific, non-repeatable events (e.g. death);
in this case the calculation of life expectancy
does not rely on distinguishing various causes of
death (Rose and Sullivan 1993: 189). However,
in event history analysis the parameters become
much more complex as multiple factors come into
the equation, requiring some form of multivariate
analysis to be undertaken.
In event history analysis the task is to
calculate the ‘hazard rate’ – the probability of a
dependent variable occurring to an individual
within a specified time frame. The approach
is mathematical, using log-linear analysis to
compute the relative size of each of several
factors (independent variables), e.g. by calculating
coefficients in cross-tabulations, that will have an
effect on the hazard rate, the likelihood of an event
occurring to an individual within a specific time
frame (Rose and Sullivan 1993: 190). 3
Event history analysis also addresses the problem
of attrition, as members leave a study over
time. Plewis (1997: 117) suggests that many
longitudinal studies suffer from sample loss over
time, and attempts to address the issue of
censoring –the adjustments necessary in a study
in order to take account of the accretion of
missing data. Right censoring occurs when we
know when a particular event commences but
not when it finishes; left censoring occurs when
we know of the existence of a particular event
or situation, but not when it began. Plewis
(1997: 118) suggests that censored events and
episodes (where attrition has taken place) last
longer than uncensored events and episodes, and,
hence, hazard rates that are based on uncensored
observations will usually be too high. Event history
is a valuable and increasingly used technique for
research.
Chapter 9