What is a Standardized Beta Coefficient?
A standardized beta coefficient compares the strength of the effect of each individual independent variable to the dependent variable. The higher the absolute value of the beta coefficient, the stronger the effect. For example, a beta of -.9 has a stronger effect than a beta of +.8. Standardized beta coefficients have standard deviations as their units. This means the variables can be easily compared to each other. In other words, standardized beta coefficients are the coefficients that you would get if the variables in the regression were all converted to z-scores before running the analysis.In regression analysis, different units and different scales are often used. For example, one variable might use dollars and another might use percentages. Standardizing coefficients means that you can compare the relative importance of each coefficient in a regression model.
For example, let’s say your model involved how income of parents and their education level affected their offspring’s lifetime earnings. Income of parents is measured in dollars and education level is measured in years of school. Standardizing these variables means that they can be compared to each other in the model. Let’s say income has a standardized beta coefficient with a value of .2 and education level has a beta of .34. The model shows that with every increase of one standard deviation in parent’s income, an offspring’s income rises by .2 standard deviations. This assumes the other variable (education level) is held constant. With an increase of one standard deviation in education level, earnings rise .34 standard deviations — assuming parent’s income is held constant.
Despite the name, it isn’t actually the coefficients that get standardized, but the variables. Betas are calculated by subtracting the mean from the variable and dividing by its standard deviation. This results in standardized variables having a mean of zero and a standard deviation of 1.
Standardized beta coefficients are also called:
- Beta Coefficients.
- Beta Weights.
- Standardized Coefficients.
Freedman, Statistical models: Theory and practice, revised ed, Cambridge U Press, 2009, p 86.
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!