Enhancing Social Surveys Through the Postal Collection of Shed Milk Teeth: an Example of a Large-Scale Cost-Effective Collection on a Longitudinal Study
Keywords: milk teeth, biomarkers, biosocial survey, postal data collection, non-response
AbstractSocial scientists and health researchers often need valid and reliable health measures from survey respondents to address key research questions, whether on environmental risks, weight and nutrition, physical activity or health / risky behaviours. There are long-standing debates on the validity of self-reported measures of health status and health behaviours in representative sample surveys. Such problems are particularly acute when the health status or behaviour occurred in the past and depends on retrospective recall. Increasingly social surveys are collecting direct biomarkers to provide more precise information on health status and behaviours. While much of this biomarker collection requires clinic visits or in-home nurse visits, some biomarkers are amenable to less costly and intrusive collection. Shed milk teeth are a good example of a stable biomarker that can provide extensive information on early (including in utero) child environmental and family contexts that may shed valuable light on childhood and adult health and social outcomes. Shed milk teeth are also potentially cheap (and non-intrusive) to collect as well as to store. In this paper we report on the collection of shed milk teeth in a nationally representative sample in the UK using postal methods. We conclude that for surveys involving children and with broad geographical coverage, incorporating the collection of shed milk teeth could prove a cost-effective enhancement, providing valuable environmental, nutritional and health information.
How to Cite
Parsons, S. J., & Platt, L. (2016). Enhancing Social Surveys Through the Postal Collection of Shed Milk Teeth: an Example of a Large-Scale Cost-Effective Collection on a Longitudinal Study. Survey Research Methods, 10(1), 1-14. https://doi.org/10.18148/srm/2016.v10i1.6233
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