Statistical methods for analyzing multiple race response data

Collection of racial data is ubiquitous throughout research as an important measure of the demographic characteristics of the study population. However the validity of racial data has been a concern and as a result several agencies have modified their measurement of racial data by allowing an individual to identify with more than one racial category. These revisions can pose several analytic dilemmas for the researcher. This research aims to add to the current methodology for analyzing data collected from multiracial individuals by comparing different statistical methods for analyzing multiple race responses as well as single race categories for data generated from California Health Interview Survey (CHIS). Three distinct methods are explored to analyze outcomes indicating whether individual health behaviors are consistent with goals of the Healthy People 2010 program. One approach uses supplementary data from the Census Bureau and the California Department of Finance to rake multiple-race respondents into single-race categories consistent with the 1977 OMB standards. A second method, following Schenker and Parker (2003), imputes a single race category for multiple race respondents to produce population health estimates. The third method, which we call multiple covariate adjustment, simultaneously controls for indicators of all self-identified race categories (with one group treated as a reference group) in a regression analysis. The sensitivity and robustness of the three methods will be checked by fitting models to simulated populations, developed from the CHIS data set, that allow the proportion of multiple-race responders in the population to vary. Attention will focus on inference for the proportion of individuals who meet Healthy People 2010 goals, which has a common interpretation across methods, as well as inference about racial disparities in meeting Healthy People 2010 goals.