Why do we create dummy variables

You will notice that when you click into the cells under the column, SPSS Statistics will give you a drop-down option with your categories already populated. Now that you have set up your data in the Variable View and Data View windows of SPSS Statistics, we recommend reading next section: Understanding dummy variables and dummy coding , where we explain the basic principles of dummy variables and dummy coding.

However, if you already familiar with the fundamentals of dummy variables and dummy coding, you can skip this section and go straight to the Procedure section where we set out the Create Dummy Variables procedure in SPSS Statistics that is used to create dummy variables.

As we mentioned in the Introduction , if you are analysing your data using multiple regression and any of your independent variables were measured on a nominal or ordinal scale, you need to know how to create dummy variables and interpret their results. This is because categorical independent variables i. In the sections below, we explain: a the number of dummy variables you need to create ; and b how to create dummy variables and dummy coding.

The number of dummy variables you need to create will depend on how many categories your categorical independent variable has. As a general rule, you will create one less dummy variable than the number of categories in your categorical independent variable.

For example, if you have a categorical independent variable with three categories e. We explain more about reference categories after the following table, which provides some examples of categorical independent variables and the number of dummy variables that need to be created:. As shown in the table above, you only need to create one less dummy variable than the number of categories in your categorical independent variable.

This is because you only need to and should transfer this number of dummy variables into a multiple regression when you have a categorical independent variable. However, there are good reasons to create a dummy variable for every category of the categorical independent variable : a it is more flexible and b it allows multiple comparisons to be made see the note below. In other words, if your categorical independent variable has three categories you would create three dummy variables , not just two.

Therefore, under normal circumstances, you will have created the following setup in SPSS Statistics, depending on whether you have version 21 or earlier or version 22 and above :. Note: As mentioned above, creating a dummy variable for every category of the categorical independent variable is beneficial for two reasons: a it is more flexible and b it allows multiple comparisons to be made. We briefly touch on these benefits below: It is more flexible: When you have created a dummy variable for every category of your categorical independent variable, you can then consider any category as a reference category.

In our example, we considered the "running" category as the reference category, which means we would have transferred "swimming" and "cycling" into the multiple regression equation. However, if we later changed our mind about our choice of reference category, we would have to run the dummy variable procedure again unless you have SPSS Statistics version 22 or above.

For example, let's assume we now wanted to consider the "cycling" category as the reference category. We could now transfer the "swimming" and "running" dummy variables into the multiple regression equation because we also have the "running" dummy variable.

It allows multiple comparisons to be made: The coefficient of a dummy variable represents the difference between the category that dummy variable represents and the reference category. For example, with "running" as the reference category, the coefficient of the "swimming" dummy variable represents the difference in the dependent variable between the "swimming" and "running" categories. Using this method, not all combinations of categories will be possible.

This problem can be solved by using different reference categories. This is possible if all categories of the categorical variable have a dummy variable.

There are two steps to successfully set up dummy variables in a multiple regression: 1 create dummy variables that represent the categories of your categorical independent variable; and 2 enter values into these dummy variables — known as dummy coding — to represent the categories of the categorical independent variable. We explain this process below using the example we set out above. Explanation: Dummy variables are simply new variables that act as " placeholders " for a particular coding scheme.

They do not contain any data at all, per se. There are many different types of coding scheme that will dictate the values that are entered into dummy variables, but we use a very common coding scheme called dummy coding or, alternatively, indicator coding N. Dummy coding works by using each dummy variable to identify a specific category of a categorical independent variable with the exception of a reference category, which we explain below. Since there are three categories , there needs to be two dummy variables representing two of the categories , and a reference category representing the third category.

Do you need support in running a pricing or product study? We can help you with agile consumer research and conjoint analysis. Apart from product and pricing research, Conjoint. Fully-functional online survey tool with various question types, logic, randomisation, and reporting for unlimited number of responses and surveys. A dummy variable is a numerical variable used in regression analysis to represent subgroups of the sample in your study.

In research design, a dummy variable is often used to distinguish different treatment groups. In the simplest case, we would use a 0,1 dummy variable where a person is given a value of 0 if they are in the control group or a 1 if they are in the treated group. Dummy variables are useful because they enable us to use a single regression equation to represent multiple groups. One subject is how we deal with categorical variables and the answer I found so far on the web was turn it into dummy variables.

However, I'm struggling to understand why we do that. What's the reason hidden behind this method? Is that something we do automatically as soon we see a categorical variable in our dataset? Introduction of dummy variables, or One-hot encoding, is a way to include nominal variables in a regression model.

Say Z is a nominal variable representing occupation, with 3 levels: Doctor, Engineer and Writer, and you wish to include this variable in your regression model to predict income. To include it in a regression model, you need to somehow convert it into a number.

Being a nominal variable, there is no natural ordering that you can use to assign a number to each category.

Then, say you use some arbitrary mapping, assigning 1 for Doctor, 2 for Engineer and 3 for Writer. Now after you fit the model, no matter what coefficient you get, the income for Engineer will always be between that of Doctor and Writer.

That is not how you would want your regression model to work, hence this arbitrary mapping is not a suitable way of including nominal variables. The proper way to include nominal variables is One-Hot encoding. In One-Hot encoding, if your variable has n levels, you add n-1 columns to your design matrix. In the above example, you would add 2 columns, because there are 3 occupations.

The first column, say ZEngineer you add would be an indicator variable corresponding to Engineer. Nominal variables with multiple levels If you have a nominal variable that has more than two levels, you need to create multiple dummy variables to "take the place of" the original nominal variable. For example, imagine that you wanted to predict depression from year in school: freshman, sophomore, junior, or senior.

Obviously, "year in school" has more than two levels. What you need to do is to recode "year in school" into a set of dummy variables, each of which has two levels. The first step in this process is to decide the number of dummy variables. This is easy; it's simply k-1, where k is the number of levels of the original variable. You could also create dummy variables for all levels in the original variable, and simply drop one from each analysis.