For those of you conducting regression analysis who wish to incorporate dummy variables, there is a fundamental technique you need to consider: the scoring technique. In this context, appropriately assigning scores to dummy variables is crucial because it dictates how we interpret the results of the regression analysis.
In this article, I will share how the technique of scoring dummy variables serves as the foundation for interpreting our analytical results.
An Example of Using Dummy Variables
Before we delve further into the scoring technique, we must first understand the context of the dummy variables we are discussing.
A dummy variable is a categorical variable that we include in a linear regression model using the Ordinary Least Squares (OLS) method. As you may already know, when utilizing OLS linear regression, several assumptions must be met. One of the primary assumptions is that the data scale used should ideally be numerical—specifically, an interval or ratio scale.
However, in many research scenarios, we inevitably need to include categorical variables because we want to determine their specific effect on the dependent variable.
Theoretically, nominal-scale categorical variables are more suitable for analysis using non-parametric statistics. Yet, because we want to observe their effects within a linear regression model, we can transform them into dummy variables, despite their inherently categorical nature.
For example, suppose a researcher wants to determine the effect of import tariffs on domestic production. The researcher hypothesizes that the implementation of an import tariff will increase domestic production. Therefore, they want to empirically verify whether the tariff policy truly drives this increase. We will use this case as our basis for understanding the dummy variable scoring technique.
How to Assign Scores to Dummy Variables
You likely already know that dummy variables are typically assigned binary scores of 0 and 1. However, the most frequent question is: when do we assign a value of 0, and when do we assign a value of 1?
If we refer to our previous case example using time-series data, we can assign the scores as follows:
- A value of 0 for the years before the import tariff was implemented.
- A value of 1 for the years after the import tariff was implemented.
By structuring it this way, we can interpret the regression results with crystal clarity. For instance, suppose the regression estimation yields a dummy variable coefficient of 2.5. This means that after the implementation of the import tariff, domestic production increased by 2.5 units (or 2.5%, depending on the unit of measurement used) compared to the baseline condition before the tariff existed.
Conversely, if the coefficient is negative—for example, -1.2—it can be interpreted that after the import tariff policy was implemented, domestic production actually decreased by 1.2 units compared to the prior period.
Therefore, the core of this scoring technique is not merely about plugging in the numbers 0 and 1. It is about how clearly we define the “before condition” (the reference category) and the “after condition” to ensure that our interpretation of the regression results is perfectly accurate.
That concludes the insights I can share on this occasion. Hopefully, this helps those of you who are currently studying or conducting regression analysis with dummy variables. See you in the next educational article!
