One of the strengths of the structural equation modeling approach used here is the ability to detect and compensate for non-invariance of the measurement of criteria across subgroups of individuals in the population, thereby adjusting for distortion of the risk factor assessment. In addition to the demographic effects on the overall AUD factor, there were significant direct effects of gender, age, and race/ethnicity on individual AUD symptom criteria. The positive direct effects for tolerance, time spent, and hazardous use among the younger (12–17 and 18–25) compared to the older age group is consistent with the potential item bias reported in other studies (Langenbucher et al., 2004; Martin et al., 2006; Saha et al., 2006). In addition, the positive direct effects for legal problems among both younger age gender groups may result from perceived sanctions for hazardous behaviors, particularly traffic citations. The negative direct effects related to withdrawal, larger amounts, cut down, and continued drinking despite psychological/physical problems among younger compared to older current drinkers may be related to greater AUD severity. Overall, although the incorporation of direct effects in the
