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provide sources of variation that are necessary to successfully discriminate among hypotheses.


The research in this volume reflects the current state of knowledge and the chapters in Part I also suggest several challenges that are likely to be faced in the future. Studies have clearly demonstrated the feasibility of including biomarkers in large-scale population surveys. Important questions remain about how to select biomarkers for inclusion in particular studies as well as questions about the trade-offs between resources allocated to biomarker measurement relative to other dimensions of a survey. The latter includes resources for the collection of other survey items, sample composition and sample size, including the age range of the sample, whether to include other family members in the study, whether to purposively sample particular subpopulations, and whether to follow subjects over time in order to collect longitudinal information on health and social status. These are not easy choices, and one size does not fit all.

Questions about the selection of biomarkers arise, in part, because there is little clarity on which biological, nutritional, and genetic markers should have the highest priority for inclusion in broad-purpose demographic and socioeconomic surveys. The typical population survey is large in scale and multipurpose in scope and designed to support the testing of an array of different hypotheses. Because health is multidimensional, it is tempting to collect a wide array of different biometric indicators to parallel both the broad-purpose nature of population surveys and the broad-based nature of most self-assessed indicators of health status reported in these surveys. Succumbing to this temptation quickly becomes prohibitively expensive. It is difficult to overstate the contributions of recent developments of simple, low-cost methods for the collection, storage, and analysis of biological samples, and future innovations promise to revolutionize the field. However, progress will be limited if we rely on innovation driven by measurement alone. It is important that theory-driven hypotheses also influence the development of technologies for measurement of biomarkers so they can be included in population surveys.

Some of the most influential social science surveys in the last 40 years have been guided by the integration of theory with measurement across multiple disciplines. For example, in economics, ideas that were pioneered by T.W. Schultz, Gary Becker, and their colleagues have influenced the design of many surveys across the globe. These include highlighting that decisions in any single domain of an individual’s life (such as health) affect decisions in other domains (such as work); that choices today affect

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