As a consumer of research, you know that relationships are of critical importance. You must first know if a relationship exists between two variables before you can determine if one variable may account for another. In this week’s readings, you focused on correlations that are used to tell you if two variables are related to one another, but you also now know that you cannot infer causation from a significant correlation alone. That is, you might find that years of education and salary are related, but that does not tell you if more education causes your salary to increase. Correlations also do not allow you to predict a participant’s score on one variable, based on his or her score on another variable. One way to predict one score from another is by using regression. For example, if you wanted to know what salary you could expect in your field if you went back to school for another 2 years, regression could help you make that prediction.
In this Discussion you will apply regression to a research scenario of your choosing.
To prepare: Imagine a situation in which you would like to predict an outcome. Think about why you would choose to use regression rather than correlation. Why is prediction more important than simply describing a relationship?
Post by Day 3 a description of a scenario where you would like to predict an outcome based on a predictor variable. Describe how regression would help you make your prediction. Apply the following terms to your scenario (making sure to fully explain each concept and its relation to your example): criterion, predictor, least-squares regression line, residual scores, correlation, and R2.
