Health-related quality of life (HRQoL) measures are increasingly important for evaluating health programs and health outcomes. Understanding the clinical and socioeconomic determinants of HRQoL is a related, but relatively little studied topic. It is often difficult to study such relationships for large samples of individuals, because large health survey data sets rarely include HRQoL measures. This paper attempts to address this problem by demonstrating the feasibility and utility of linking algorithms for well-known quality of life indicators to large nationally representative databases. This process allows the advantages of health related quality of life (HRQoL) indicators to be combined with the benefits of large, nationally representative data sets. We illustrate this approach using the National Medical Expenditure Survey (NMES) and the EuroQol and drawing on a previous match of the Health Utility Index (HUI) with NMES. Although the NMES data allow a good match to the EuroQol and the HUI, the matches are not perfect. Thus we conduct sensitivity analysis using several related specifications of each indicator. We analyze the correlation of the several specifications within each set of specifications of HUI and EuroQol and across the two, HUI and EuroQol. By examining the within domain correlations and the between correlations, we find that the alternate specifications within domain are very similar and that the two HRQoL indicators are similar in many but not all ways. We analyze construct validity by examining the performance of the HUI and EuroQols in empirical situations in which we have knowledge about the relationships (e.g. health decreases with aging). The results suggest good construct validity.