The NLAAS story: Some reflections, some insights.

Abstract

The General Social Survey (GSS), a national survey that queries U.S. residents about their behaviors and attitudes on social issues, shows that many Americans do not have as much familiarity with Asian Americans as they do with other racial and ethnic groups (Gee, Ro, Shariff-Marco & Chae, 2009). Thirty-four percent of the GSS sample stated that they had less in common with Asian Americans compared to African Americans, Latinos, Whites, and Jews. African Americans received the next highest percentage (17%). When the question was reversed, “Which group do you have the most in common with?”, a small percentage of the GSS respondents cited Asian Americans (7.5%) and African Americans (7.7%). What accounts for this lack of connection with Asian Americans? There are several likely reasons, including past and current stereotypes of Asian Americans, the geographic and residential distribution of racial groups across the country, and the high proportion of immigrants among Asian Americans that make them seemingly dissimilar to U.S.-born residents. It is also likely that there is relatively limited scientific data available about Asian Americans which make it difficult to adequately inform the general public about Asian American experiences on a wide range of dimensions. It is within this context that the National Latino and Asian American Study (NLAAS) was conceived, designed, implemented, and analyzed. NLAAS is the first national epidemiological study of Asian Americans that used state-of-the-art sampling and survey design strategies, an interview guide that was translated into multiple languages and dialects, and includes data that can be compared across Black American, Latino and White samples see Pennel). Its findings have been disseminated to a broad range of public and scientific audiences and the data are still being analyzed. NLAAS has received special recognition for its contribution to the scientific enterprise and it became one of the most downloaded datasets when it became publically-available. We are gratified and honored that this special issue of the journal has focused on analyses derived from the NLAAS. This paper serves to provide some of the initial thinking behind NLAAS and discuss some of the themes that we find in the papers included in the two special issues.