Table 2.
Parameter estimates for the natural log of the ratio of drinking to nondrinking drivers younger than age 21 in fatal crashes. In this model, “region” and data from the 14 states that had the possession/purchase laws in place in 1982 serve as a covariate (R2 = 0.49)
| Parameter | B | SE (B) | Effect Sizea (%) | P-Value | 95% CI for B | Variance explained (partial Eta2) | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| Possession & Purchasing Laws | -0.12 | 0.06 | -11.2 | 0.041 | -0.23 | 0.00 | 0.01 |
| .08 Law | -0.80 | 0.24 | -55.1 | 0.001 | -1.27 | -0.33 | 0.03 |
| ALR Law | -0.26 | 0.05 | -22.6 | <0.001 | -0.35 | -0.17 | 0.09 |
| Under 21 ratio in comparison States (in log transformed metric) | 0.46 | 0.11 | <0.001 | 0.24 | 0.68 | 0.05 | |
| % Urbanization | -0.32 | 0.15 | 0.038 | -0.62 | -0.02 | 0.01 | |
| Unemployment | 0.03 | 0.01 | 0.022 | 0.00 | 0.05 | 0.02 | |
| VMT per licensed driver | 0.03 | 0.02 | 0.048 | 0.00 | 0.06 | 0.01 | |
| Categorical Factors | F-statistic | df | P-Value | partial Eta2 | |||
| Region (Region 10 = Ref cat ) | 16.68 | 9 | <0.001 | 0.32 | |||
a
Effect size is the percentage change in the outcome variable’s metric, per unit change in the predictor variable. For binary variables representing presence/absence of a law, it can be interpreted as the proportional amount of change in the outcome associated with the presence of a law.