Evaluating and Analyzing Life History Survey Data 1 |
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| Coordinator 1 | Dr Mengyao Hu (University of Michigan) |
| Coordinator 2 | Dr Brian Wells (University of California, Los Angeles) |
| Coordinator 3 | Professor Jacqui Smith (University of Michigan) |
There is an increasing multidisciplinary interest in understanding the long-term influences of early- and mid-life factors on late-life health and well-being outcomes. The knowledge gained about these time-varying complex relationships is critically important for designing policy intervention to reduce inequalities and foster healthy aging and well-being. One increasingly-popular method to obtain this information is to collect life history data using event history calendars. This approach has been applied in many large-scale surveys including the English Longitudinal Study of Ageing (ELSA) and the Survey of Health and Ageing in Europe (SHARE).
Recently, the U.S. Health and Retirement Study (HRS) collected life history data in their off-year mail surveys in 2015 and 2017. The U.S. Health and Retirement Study (HRS) is unique among large scale, high quality longitudinal surveys in representing the full range of the U.S. birth cohorts beginning with the first cohort born before 1923 through the late-Boomers born in 1960-1965. These cohorts have participated in the major demographic, economic and social transformation of the American people over much of the past century. The newly collected life history data fill in critical information about HRS respondents between birth and their entry into the HRS beginning in their 50s (70s for oldest cohort) and have great implications for conducting research and making effective policies that will promote healthy aging.
These aforementioned large-scale, representative life history surveys differ in their survey designs. For example, the HRS life history survey was conducted through mail surveys, and most other surveys were conducted through in-person interviews. Little research so far has been done to evaluate the pros and cons of different design conditions in life history surveys, the cognitive burden and usability of these surveys. There is also a growing need to evaluate the validity of such distantly recalled information, and the quality of life history data in the Total Survey Error framework.
We invite the submission of abstracts on 1) methodological evaluation on the quality of life history data such as examining the validity of life history measures, and evaluating nonresponse and recall errors; 2) statistical methods to identify, measure and control for errors in life history surveys; 3) developments in the statistical methods to analyze life history data; and 4) substantive research projects that links early- and mid-life factors to life course patterns and outcomes.