If we switch the vector (column) from zeros to ones (status to status 2), we just change the sign of the deviations from the mean.To test for the difference we find the ratio of the two mean squares:Īnd if we square this result, we get 10.49, which is our value for F (recall that F = t 2). The sum of squared deviations from the grand mean is 128.5 (SStot) the sum of squared deviations from the cell means is 55.60 (SSwithin), and difference must be SSbetween = 128.5-55.60 = 72.90. For fun, let's see what happens if we code it the other way (Status 2 0=single 1=married) Our data: They also fill out some demographic information, an item of which is marital status (Status), which we code 1=single 0=married. So we take a small sample of people shopping at University Square Mall and promise them some ice cream if they fill out our life satisfaction survey, which some do. Let's suppose we want to know whether people in general are happier if they are married or single. But we can do the same thing with regression. Ordinarily if we wanted to test for group differences, we would use a t-test or an F-test. Or we could code for marital status as 1=single 0=married or 1=married 0=single. If we did, we would have a column variable indicating status as male or female. For example, we could code sex as 1=female 0=male or 1=male 0=female. Category membership is indicated in one or more columns of zeros and ones. With this kind of coding, we put a '1' to indicate that a person is a member of a category, and a '0' otherwise. Both methods are specific cases of a larger family called the general linear model. This is designed merely to familiarize you with the correspondence between regression and analysis of variance. We are going to cover lots of ground quickly here. You are already familiar with ANOVA, so I am not going to discuss it. However, I want to show you what happens with the 3 kinds of coding so you will understand it. The computer will be doing the work for you. There are some advantages to doing this, especially if you have unequal cell sizes. What we are doing here is ANOVA with regression techniques that is, we are analyzing categorical (nominal) variables rather than continuous variables. Is there any advantage to using regression rather than some ANOVA program to analyze designs with categorical IVs? What effect does unbalanced (unequal) cell size have on the interpretation of dummy coded regression slopes and intercepts? Why might I choose one model rather than another (that is, choose either dummy, effect or orthogonal coding) to analyze my data? What do the intercept and b weights mean for these models? What is the difference between a continuous variable and a categorical variable? Give a concrete example of each. Categorical IVs: Dummy, Effect, & Orthogonal Coding

Spss code categorical variables