1. Covariance. Significant relationships in longitudinal designs help establish covariance. When two variables are significantly correlated (as in the crosslag correlations in Figure 9.3), there is covariance.

2. Temporal precedence. A longitudinal design can help researchers make inferences about temporal precedence. Because each variable is measured in at least two different points in time, they know which one came first. By comparing the relative strength of the two cross-lag correlations, the researchers can see which path is stronger. If only one of them is statistically significant (as in the Brummelman overvaluation and narcissism study), the researchers move a little closer to determining which variable comes first, thereby causing the other.

3. Internal validity. When conducted simply—by measuring only the two key variables—longitudinal studies do not help rule out third variables. For example, the Brummelman results presented in Figure 9.3 cannot clearly rule out the possible third variable of socioeconomic status. It’s possible that parents in higher income brackets overpraise their children, and also that children in upper-income families are more likely to think they’re better than other kids.