![spss code categorical variables spss code categorical variables](http://dwstockburger.com/Multibook/images/mlt0885.gif)
This can really lead to confusion when interpreting coefficients. Use a contingency table to compare a categorical variable (e.g., pass vs. Because 1 comes last alphabetically, SPSS GLM will make that group the reference group and internally code it as 0. It really can get confusing, though, if the variable was already dummy coded–if it already had values of 0 and 1. (But create a new variable–never overwrite original data). This is because independent variables in linear regression must be either scale or dichotomous. If you want to use a nominal or ordinal variable with 3 or more categories in linear regression you first need to dummy code the variable. I’ve been known to do things like change my data so that the control group becomes something like ZControl. Create dummy variables from an existing categorical variable in SPSS. If you want that to be the reference group in SPSS GLM, make it come last alphabetically. In some studies it really doesn’t matter which is the reference group.īut in others, interpreting regression coefficients will be a whole lot easier if you choose a group that makes a good comparison such as a control group or the most common group in the data. Some procedures let you change it the default, but SPSS GLM doesn’t). (Note: Not all procedures in SPSS use this default so double check the default if you’re using something else. If those values are numbers, it will be the highest one. So if the values you input are strings, it will be the one that comes last. SPSS GLM always makes the reference group the one that comes last alphabetically. However, there are some dummy coding defaults you need to be aware of that may or may not make this a good choice. SPSS GLM will dummy code those variables for you, which is quite convenient if your categorical variable has more than two categories. The easiest is to put categorical variables in Fixed Factors. You have two choices, and each has advantages and disadvantages. So the question is what to do with your categorical variables. To do so on all categorical variables, you can use sapply(): mustconvert<-sapply(M,is.factor) logical vector telling if a variable needs to be displayed as numeric M2<-sapply(M,mustconvert,unclass) ame of all categorical variables now displayed as numeric out<-cbind(M,mustconvert,M2) complete ame with all variables. And don’t use random factors at all unless you really know what you’re doing.
![spss code categorical variables spss code categorical variables](https://1.bp.blogspot.com/-Sj8Rh4J0mLk/X2A-zZEyaFI/AAAAAAACJ9M/HGId84ii9-kgsXfdi3KEOV5CmPtw5KjagCLcBGAsYHQ/s2380/age1.jpg)
As I’ve detailed in another post, any continuous independent variable goes into covariates. The big question in SPSS GLM is what goes where. Or you can use SPSS GLM, which I discuss here and in a follow-up post. You can use the SPSS Regression procedure.
![spss code categorical variables spss code categorical variables](https://i.ytimg.com/vi/wjKuHWMhLxQ/maxresdefault.jpg)
If you have a categorical predictor variable that you plan to use in a regression analysis in SPSS, there are a couple ways to do it.