Episode 18: What is Multiple Linear Regression?
Earlier in this season, we discussed a commonly used technique called simple linear regression. In this technique, we used one variable to predict an outcome. But, let's face it – life is a little bit more complex than just having one predictor and many times, organizations have lots of data that can be used to predict an outcome. In another technically focused episode, co-hosts Ron Landis and Jennifer Miller deconstruct multiple linear regression. They focus on using multiple predictors to predict a single criterion variable.
In this podcast episode, we had conversations around these multiple regression questions:
What is multiple linear regression?
What are some applications of multiple linear regression?
What are some of the ways in which models can be built using multiple linear regression?
What is mediation and moderation?
Link to Measurement Podcast Episode
2 Key Takeaways on Multiple Linear Regression
Multiple linear regression uses multiple variables to predict an outcome (I.e., criterion) variable. The ultimate goal is to explain the variation in the criterion variable. One aspect to consider in this analysis is the relation between variables; that is, to what degree do the predictor variables correlate and how does that relation predict the outcome variable. Depending on the relation between predictors, either partial or full redundancy might be present.
Ron and Jennifer discussed three questions that can be asked using multiple linear regression. First, you can assess the effects of particular predictors while controlling for others. Second, you can compare different sets of variables to find the most efficient model. Third, you can test for moderation and mediation.
