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Can some1 please explain to me what is meant by sigma-restricted parameterization and over-parameterized parameterization?

the only information i could find on this is the following. But it didnt make any sense to me. Any help would be greatly appreciated. Thanks

Sigma Restricted Model. A

*sigma restricted model*uses the

*sigma-restricted*coding to represent effects for

*categorical predictor variables*in

*general linear models*and

*generalized linear models*. To illustrate the

*sigma-restricted*coding, suppose that a

*categorical predictor variable*called Gender has two levels (i.e., male and female). Cases in the two groups would be assigned values of 1 or -1, respectively, on the coded predictor variable, so that if the regression coefficient for the variable is positive, the group coded as 1 on the predictor variable will have a higher predicted value (i.e., a higher group mean) on the dependent variable, and if the regression coefficient is negative, the group coded as -1 on the predictor variable will have a higher predicted value on the dependent variable. This coding strategy is aptly called the sigma-restricted parameterization, because the values used to represent group membership (1 and -1) sum to zero.

Overparameterized Model. An

*overparameterized model*uses the indicator variable approach to represent effects for

*categorical predictor variables*in

*general linear models*and

*generalized linear models*. To illustrate indicator variable coding, suppose that a

*categorical predictor variable*called Gender has two levels (i.e., Male and Female). A separate continuous predictor variable would be coded for each group identified by the categorical predictor variable. Females might be assigned a value of 1 and males a value of 0 on a first predictor variable identifying membership in the female

*Gender*group, and males would then be assigned a value of 1 and females a value of 0 on a second predictor variable identifying membership in the male

*Gender*group.