We do this by looking back at the raw data and figuring out how many of the 25 people in the A (E+, D+) cell above were men and how many were women. Again, we draw 2 × 2 tables with the same exposure (sleep) and outcome (GPA) but draw separate tables for men and women (gender is the covariable). As with confounding, we would conduct a stratified analysis to check for effect modification. However, from talking to students, we wonder whether or not gender might be an important covariable. Just as for confounding, we refer to this as the unadjusted or crude RR. This is a risk ratio from a cohort study, so we need to include the time frame-which I did by saying “to end the term”. Students who averaged fewer than 8 hours of sleep per night were 1.0 times as likely to end the term with a GPA below 3.0, compared to students who got at least 8 hours per night. Since this was a cohort study, we calculate the risk ratio (RR): For example, if we do a cohort study on amount of sleep and GPA among Oregon State University (OSU) students over the course of one term, we might collect these data: Table 8-1 When effect modification (also called interaction) is present, there will be different results for different levels of the third variable (also called a covariable). Rather, presence of effect modification is itself an interesting finding, and we highlight it. Effect modificationalso involves a third variable (not the exposure and not the outcome)-but in this case, we absolutely do not want to control for it. A confounder, you will recall, is a third variable that if not controlled appropriately, leads to a biased estimate of association. In the prior chapter, we discussed confounding.