The “impact” of major factors (labor market ,type of disability, type of economic/ medical assistance,

and type of DVR services received) and other client characteristics (for example, education, distance

from office) were estimated in terms of their ability to explain

**of successful**

*the likelihood*rehabilitation.

Statistically, these likelihoods are derived from

**‘**often referred to as

*odd ratios*’**‘odds’ –**higher or

lower chances of an event (rehabilitation).Likelihoods are calculated by taking the odds minus 1.00.

Odds are calculated by taking the exponential value of the relevant

**‘log-odd’**coefficientor sum of

relevant log-odd coefficients – referred toas log-odds. They are estimated by a logistical regression

The formulas are:

Likelihood = (Odds – 1.00) Odds = Exp [Sum of Log-odds]

So: Likelihood = (Exp [Sum of Log-odds] –1.00)

For example: A post secondary degree is associated with a log-odd of 0.3223.

*See model*. The odds

ratio is the exponential value of 0.3223:1.38. The likelihood is 0.38 (1.38 minus 1.0); it is interpreted

as a 38 percent increase in the chances of being successfully rehabilitated if a DVR client has such a

degree.

This description seems like relative risk to me, but I did not think that was calculated this way.