Purpose

    The general linear model can be seen as an extension of linear multiple regression for a single dependent variable, and understanding the multiple regression model is fundamental to understanding the general linear model. The general purpose of multiple regression (the term was first used by Pearson, 1908) is to quantify the relationship between several independent or predictor variables and a dependent or criterion variable. For a detailed introduction to multiple regression, also refer to the Multiple Regression chapter. For example, a real estate agent might record for each listing the size of the house (in square feet), the number of bedrooms, the average income in the respective neighborhood according to census data, and a subjective rating of appeal of the house. Once this information has been compiled for various houses it would be interesting to see whether and how these measures relate to the price for which a house is sold. For example, one might learn that the number of bedrooms is a better predictor of the price for which a house sells in a particular neighborhood than how "pretty" the house is (subjective rating). One may also detect "outliers," for example, houses that should really sell for more, given their location and characteristics.

    In the social and natural sciences multiple regression procedures are very widely used in research. In general, multiple regression allows the researcher to ask (and hopefully answer) the general question "what is the best predictor of ...". For example, educational researchers might want to learn what are the best predictors of success in high-school. Psychologists may want to determine which personality variable best predicts social adjustment. Sociologists may want to find out which of the multiple social indicators best predict whether or not a new immigrant group will adapt and be absorbed into society.

Preparations   

    1. Run Statistics→Analysis of Variance(ANOVA)→GLM ANOVA.

    2. Add all necessary interactions in the GLM ANOVA Settings window. Model label reflects current model.

    3. If necessary, change coding type:

• Dummy coding - with this kind of coding, we put a '1' to indicate that a person is a member of a category, and a '0' otherwise. Category membership is indicated in one or more columns of zeros and ones. We can apply dummy coding to categorical variables with more than two levels. We can keep the use of zeros and ones as well. However, we will always need as many columns as there are degrees of freedom.

• Effect coding is similar to dummy coding. The difference in coding is that, in effect coding, the comparison group is identified by the symbol -1.

• Orthogonal coding is used to compute contrasts. You can use it if you have specific planned comparisons going into the analysis.

      Click OK to run GLM ANOVA.