Assessing Cross-Cultural Comparability of Self-Rated Health and Its Conceptualization through Web Probing

Self-rated health (SRH) is a widely used question across different fields, as it is simple to administer yet has been shown to predict mortality. SRH asks respondents to rate their overall health typically using Likert-type response scales (i.e., excellent, very good, good, fair, poor). Although SRH is commonly used, few studies have examined its conceptualization from the respondents’ point of view and even less so for differences in its conceptualization across diverse populations.

We aim to assess the comparability of SRH across different cultural groups by investigating the factors that respondents consider when responding to the SRH question. We included an open-ended probe asking what respondents thought when responding to SRH in web surveys conducted in five countries: Great Britain, Germany, the U.S., Spain, and Mexico. In the U.S., we targeted six racial/ethnic and linguistic groups: English-dominant Koreans, Korean-dominant Koreans, English-dominant Latinos, Spanish-dominant Latinos, non-Latino Black Americans, and non-Latino White Americans. One novelty of our study is allowing multiple attribute codes (e.g., health behaviors, illness) per respondent and tone (e.g., in the direction of positive or negative health or neutral) of the probing responses for each attribute, allowing us 1) to assess respondents’ thinking process holistically and 2) to examine whether and how respondents mix attributes. Our study compares the number of reported attributes and tone by cultural groups and integrates SRH responses in the analysis. This study aims to provide a deeper understanding of SRH by revealing the cognitive processes among diverse populations and is expected to shed light on its cross-cultural comparability. 

Stephanie Morales is a second-year Ph.D. student at the University of Michigan's Program in Survey and Data Science. She holds a BA in Psychology and an MA in Sociology. She is interested in cross-cultural surveys, measurement error in data collection with racial/ethnic minorities, and adaptive survey design.