Model Specification
Standard multiple regression has a major assumption: it assumes that all the important predictors are in the equation. This assumption is called model specification. A model is specified when all the predictors are in the equation, and no irrelevant predictors are in the equation.
However, in the social sciences, it is rare for a study to be able to know all the important predictors of a behavioral outcome. Therefore, most models are not specified. When the model is not specified, the estimates for the beta weights are not accurate. Because the inclusion of one variable can cause the beta weights to fluctuate wildly, this fluctuation is sometimes called the problem of the bouncing betas. It is this problem with bouncing betas that makes unit-weighted regression a useful method.
Read more about this topic: Unit-weighted Regression
Famous quotes containing the word model:
“The best way to teach a child restraint and generosity is to be a model of those qualities yourself. If your child sees that you want a particular item but refrain from buying it, either because it isn’t practical or because you can’t afford it, he will begin to understand restraint. Likewise, if you donate books or clothing to charity, take him with you to distribute the items to teach him about generosity.”
—Lawrence Balter (20th century)