Table 2.
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 |
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.