Data collection procedures can significantly impact the quality of demographic and SES information obtained from patients. Studies have shown that preferred response options for self-identification impact racial/ethnic coding (Williams, 1999); for example an individual who self-identifies as Latino, but must choose either “Hispanic” or “white” on a survey must self-identify incorrectly, select an unknown category, or skip the question entirely. The limited number of race/ethnicity groups that patients are able to choose from represents a fraction of the race/ethnicity groups that exist. The fact that data collection policies and procedures across public and private efforts lack coordination and standardization also complicates our ability to examine disparities. One group may use three race/ethnic classes, while another collects four. The lack of standardized and reliable methods for collecting race/ethnicity data is the most commonly cited concern by health plans that choose not to collect this type of data (AHIP-RWJF 2006). Despite efforts to improve and expand racial/ethnic groups there is general consensus in the literature that current categories are more limiting than they are illustrative. Some believe there is more variation within race/ethnicity groups than between groups (Williams 1999). For example, the NHIS Hispanic code contains more than 25 different national origin populations that vary significantly in terms of health status (Sandefur et al., 2004).