Example
An example may clarify how unit weights can be useful in practice.
Brenna Bry and colleagues (1982) addressed the question of what causes drug use in adolescents. Previous research had made use of multiple regression; with this method, it is natural to look for the best predictor, the one with the highest beta weight. One previous study had found that early use of alcohol was the best predictor. Another study had found that alienation from parents was the best predictor. Still another study had found that a low grades in school was the best predictor. The failure to replicate was clearly a problem, a problem that could be caused by bouncing betas.
Bry and colleagues suggested a different approach. Instead of looking for the best predictor, they looked at the number of predictors. In other words, they gave a unit weight to each predictor. Their study had six predictors: 1) grades in school, 2) affiliation with religion, 3) age of alcohol use, 4) psychological distress, 5) self-esteem, and 6) alienation from parents. Each risk factor was scored as one (present) or zero (absent). For example, grades in school were scored as one when the grades were Ds or Fs. The results showed that the number of risk factors was a good predictor of drug use: adolescents with more risk factors were more likely to use drugs.
The model used by Bry and colleagues was that drug users do not differ in any special way from non-drug users. Rather, they differ in the number of problems they must face. "The number of factors an individual must cope with is more important than exactly what those factors are" (p. 277). Given this model, unit-weighted regression is an appropriate method of analysis.
Read more about this topic: Unit-weighted Regression
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