KANDA DATA

For those of you conducting multiple linear regression analysis, have you ever used dummy variables? These variables are very useful when we want to include categorical variables in a multiple linear regression equation.

Fortunately, in this article, I will talk about dummy variables. I will explain what dummy variables are, how to create them, and how to include them in a multiple linear regression model.

Before going further, we need to understand that dummy variables are categorical variables measured on a nominal scale, so they cannot be directly included in a regression equation.

This is because regression equations only accept numeric input. Therefore, we need a scoring technique to convert nominal categorical variables into dummy variables that can be used in multiple linear regression.

Understanding the Definition of Dummy Variables

By definition, a dummy variable is a nominal-scale categorical variable that is represented as a numeric score and used as an independent variable in a linear regression equation.

Dummy variables are usually expressed as 0 and 1, where 1 indicates the presence of a particular category, and 0 indicates its absence.

For example, let’s say we want to examine the effect of household income in urban and rural areas on food consumption. We can treat urban and rural household income as a dummy variable, where a score of 1 is assigned to urban households, and 0 to rural households.

This structure is based on a hypothesis that urban respondents are expected to have a higher average income than rural respondents. Therefore, a score of 1 is given to urban respondents and 0 to rural respondents.

Steps to Create Dummy Variables

In multiple linear regression, we estimate the effect of independent variables on the dependent variable, and all variables must be in numeric form.

If we enter categorical variables in text form, such as “urban” and “rural,” the regression software won’t be able to compute the regression slope.

That’s why, by scoring the categorical variable into a dummy variable, we can include it alongside other numeric variables in the regression model.

The first step in creating a dummy variable is to identify the categorical variable in your dataset. In multiple linear regression, categorical variables can be included as independent variables.

However, in OLS-based multiple regression models, it is recommended not to overload the model with dummy variables. Including just one or two dummy variables is enough, especially if your regression has about 5 or 6 independent variables.

This is to ensure that the regression model still satisfies the necessary assumptions and produces consistent and unbiased estimates.

Interpreting Dummy Variables

Once we perform the multiple linear regression analysis, the dummy variable will have a coefficient, just like any other independent variable.

However, we need to be careful when interpreting the coefficient of a dummy variable, because it’s different from interpreting numerical predictors.

To make it easier to understand, here’s a simple example. Suppose income levels of urban and rural respondents are measured in US Dollars.

If the regression result shows that the coefficient for the dummy variable is 153.20, then the interpretation is that urban respondents have an income that is 153.20 US Dollars higher than rural respondents, assuming all other variables are held constant.

This is just an example of interpretation. In your own study, make sure to adjust the explanation based on your specific context and variables.

Closing Remarks

After reading this article, I hope readers now have a better understanding of what dummy variables are and how to create dummy scores in a multiple linear regression analysis.

Dummy variables are an essential component of multiple regression analysis, especially when we want to include categorical variables.

Alright, that’s all for this article. I hope it’s useful and provides new insights for those who need it. Thank you for reading, and stay tuned for more articles from Kanda Data in the future.