a procedure in REGRESSION NALYSIS WHERE PREDICTOR OR INDEPENDENT VARIABLES ARE CODED AS 1 OR 0 (REPRESENTING CATEGORIES) AND CAN BE USED TO PRODUCE ANOVA TABLES UNDER THE GENERAL LINEAR MODEL IS KNOWN AS?
The procedure you are referring to is called "dummy coding" or "indicator coding." Dummy coding is a method used in regression analysis when the predictor or independent variables are categorical and have more than two categories. It involves creating binary (0 or 1) variables, known as "dummy variables," to represent each category of the predictor variable.
To perform dummy coding and use it to produce ANOVA (Analysis of Variance) tables under the general linear model, you need to follow these steps:
1. Identify the categorical predictor variable for which you want to perform the dummy coding. Let's assume it has "k" categories.
2. Create "k-1" dummy variables, each representing one category of the predictor variable. For example, if your predictor variable has three categories (A, B, and C), you would create two dummy variables (D1 and D2).
3. Assign a value of 1 to each observation that belongs to the corresponding category and 0 to observations that do not belong to that category. For instance, if category A is represented by D1, all observations that belong to category A would have D1 = 1, while observations from other categories (B and C) would have D1 = 0.
4. Repeat this process for each additional category of the predictor variable, creating new dummy variables for each.
5. Once you have created the dummy variables, include them in the regression or analysis of variance (ANOVA) model as independent variables, along with any other predictor variables you may have.
6. Use the general linear model framework to carry out the analysis. The dummy variables will allow you to compare the effect of each category relative to a reference category (usually the one not represented by any dummy variable).
By following these steps, you can perform dummy coding and utilize it to generate ANOVA tables under the general linear model.