If we only include the interaction term without the main effects, then the observed effect of the interaction term might be masking the true effect from one of the main predictors. Running a model like this however, is generally ill-advised. Although the coding for this output is relatively painless, Stata offer a quicker way to run models with interaction terms using hashtags:ДЄs the figure shows, if one hashtag is used, Stata runs a model only with the interaction term. The output suggests that minorities gain 15 cents more per hour than whites for every additional year of education they receive, ceteris paribus, even though minorities make $2.47 less per hour than whites overall. The most intuitive way to do so is to generate the interaction term as a new variable: This doesnвЂ™t mean that minorities have higher wages than whites (ОІ 2 tells us that), but that minorities derive more wage-generating value from education than whites.Д¬onducting analysis with interaction terms is straightforward in Stata. If ОІ 3 > 0, then minorities earn more per hour than Caucasians for every additional unit of education they receive, controlling for the other predictors. О’ 3 tells us the effect of education on hourly wage by race. Wage = ОІ 0 + ОІ 1Education + ОІ 2Minority + ОІ 3Education*Minority + Оµ To consider an interaction term, we simply create a new variable with the two terms multiplied together: ItвЂ™s possible that minority wages rises higher for every additional вЂњunitвЂќ of education than it does for whites. For instance, when testing how education and race affect wage, we might want to know if educating minorities leads to a better wage boost than educating Caucasians. In regression analysis, it is often useful to include an interaction term between different variables.

Or in stata