KANDA DATA

If you’re analyzing data using OLS linear regression, there are certain assumptions you need to meet. The purpose of these assumption tests is to ensure that the estimation results are consistent and unbiased.

To meet these assumptions, it’s generally recommended that the variables you use are numeric, measured on an interval or ratio scale. But what if we want to include a categorical variable in an OLS linear regression model? Is it possible? That’s exactly what we’re going to discuss in this article, so stay tuned and read until the end.

Understanding Categorical Variables

Are you familiar with categorical variables? These are variables that are not numeric.

For example, let’s say we’re conducting research to find the factors that influence domestic production. In this case, domestic production is the dependent variable, while the independent variables are those suspected to affect it.

However, we might also want to examine the effect of import policy on domestic production. Import policy is a categorical variable.

For instance, if we have time series data on domestic production, we could compare the period before the import policy was implemented to the period after it. Does the import policy have a significant effect on domestic production?

To answer that question, we can add an “import policy” variable with two categories: before the import policy and after the import policy. Since this variable is not numeric but categorical, we need to create what is called a dummy variable. Let’s dive in.

Categorical Variables as Dummy Variables

A categorical variable, like the example above, can be converted into a dummy variable and included in the regression equation. Typically, the dummy variable is placed at the end, after the other independent variables.

In statistics, this technique is known as a binary dummy variable on a nominal scale.

Still remember what nominal scale data is? Let’s do a quick flashback to basic statistics: there are four data scales, namely nominal, ordinal, interval, and ratio.

Normally, to satisfy the assumptions of OLS linear regression, we use variables measured on an interval or ratio scale. But if we want to include a categorical variable on a nominal scale, we can transform it into a dummy variable.

After understanding this concept, I hope you now know what a dummy variable is. Next, let’s talk about the scoring technique for dummy variables.

Dummy Variable Scoring Technique

For dummy variables to be analyzed further, we need to apply a scoring technique. A dummy variable is given a score of 1 or 0.

So, when do we assign a score of 1 and when do we assign a score of 0?

Let’s go back to the previous example about the effect of import policy on domestic production. If we hypothesize that the import policy affects domestic production, the scoring technique would be:

• After the import policy → score 1
• Before the import policy → score 0

This way, the categories (before/after import policy) are transformed into numeric values of 1 and 0. Once all variables in the OLS linear regression model are numeric, we can proceed with the analysis as usual.

However, don’t forget to perform the required assumption tests after adding the dummy variable.

Conclusion

Dummy variables can be a useful solution for researchers who want to include categorical variables in an OLS linear regression model. Still, caution is needed.

It’s recommended to include only one or two dummy variables in the regression equation. Also, independent variables should still be dominated by those measured on an interval or ratio scale.

That’s it for this article. I hope it’s useful and adds insight for those who need it. Stay tuned for more articles from Kanda Data in the future.