# How do you create dummy variables in SPSS?

## How do you create dummy variables in SPSS?

Procedure in SPSS Statistics to create dummy variables

1. Click Transform > Create Dummy Variables on the main menu, as shown below:
2. Transfer the categorical independent variable, favourite_sport, into the Create Dummy Variables for: box by selecting it (by clicking on it) and then clicking on the button.
3. Click on the.

## How do you Dichotomize a variable?

Dichotomizing is also called dummy coding. It means: Take a variable with multiple different values (>2), and transform it so that the output variable has 2 different values. Note that this “thing” can be understood as consisting of two different aspects: Recoding and cutting.

What happens when you Dichotomize a continuous variable?

Experts generally recommend avoiding dichotomization of continuous variables in data analysis, as it can lead to loss of statistical power, inappropriate effect size, and loss of explanatory information.

### Can dummy variables be 1 and 2?

Indeed, a dummy variable can take values either 1 or 0. It can express either a binary variable (for instance, man/woman, and it’s on you to decide which gender you encode to be 1 and which to be 0), or a categorical variables (for instance, level of education: basic/college/postgraduate).

### Why do we Dichotomize variables?

Re- searchers may dichotomize independent variables for many reasons—for example, because they believe there exist distinct groups of individuals or because they believe analyses or presentation of results will be simplified.

Why do we Dichotomize data?

Other researchers use dichotomization because they find a higher correlation and report those because it is tempting but they forget to look to the negative influences on the measurement. The most heard argument is that there are real different groups that underlie the data.

## How many dummy variables is too many?

The general rule is to use one fewer dummy variables than categories. So for quarterly data, use three dummy variables; for monthly data, use 11 dummy variables; and for daily data, use six dummy variables, and so on.

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