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Presented By: Michigan Program in Survey and Data Science

MPSDS JPSM Seminar Series - Utility of Commercial Data for Sampling Population Subgroups: A Case of Health and Retirement Study

Sunghee Lee, Chendi Zhao & Anqi Liu - Survey Research Center, Michigan Program in Survey and Data Science

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MPSDS JPSM Seminar Series
November 16, 2022
12:00 - 1:00 EST

Sunghee Lee is a Research Associate Professor at Survey Research Center, University of Michigan. Her research focuses on sampling and measurement issues with hard-to-survey population subgroups as well as racial, ethnic, and linguistic minorities.

Chendi Zhao is a Research Assistant and first-year Ph.D. student in the Program in Survey and Data Science

Anqi Liu is a master’s student in MPSDS at the University of Michigan. She works closely with Dr. Sunghee Lee on the Health and Retirement Study sampling.

A standard approach for targeting population subgroups in household surveys is to sample general population and then to screen for eligible households. This becomes increasingly costly as the subgroup accounts for a small proportion of the population, which is the case for the Health and Retirement Study (HRS). HRS is a population-based longitudinal study of adults ages 50 and older in the U.S. and maintains its representativeness by adding a new age cohort every 6 years. In 2016, HRS targeted those born between 1960 and 1965 with an additional goal of oversampling racial/ethnic minorities. This group is less than 10% of the population. In order to increase the efficiency of screening, HRS had traditionally used probability proportionate size sampling in its area-probability sample with the age-eligible population size as a measure of size as well as stratification based on the race/ethnicity distribution of area sampling units. For 2016, HRS sampling additionally used stratification at the address level by enhancing the population of addresses in the sample areas with commercial data. This study examines the utility of commercial data for increasing efficiency with a focus on its availability and accuracy by analyzing a dataset that combines sampling frame data, screening data, main survey data as well as external data from the American Community Survey.

The University of Michigan Program in Survey Methodology was established in 2001 seeking to train future generations of survey and data scientists. In 2021, we changed our name to the Michigan Program in Survey and Data Science. Our curriculum is concerned with a broad set of data sources including survey data, but also including social media posts, sensor data, and administrative records, as well as analytic methods for working with these new data sources. And we bring to data science a focus on data quality — which is not at the center of traditional data science. The new name speaks to what we teach and work on at the intersection of social research and data. The program offers doctorate and master of science degrees and a certificate through the University of Michigan. The program's home is the Institute for Social Research, the world's largest academically-based social science research institute.

The Annual Summer Institute in Survey Research Techniques
The mission of the Summer Institute is to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys. The Summer Institute in Survey Research Techniques has presented courses on the sample survey since the summer of 1948, and has offered such courses every summer since. Graduate-level courses through the Program in Survey and Data Science are offered from June 5 through July 28 and available to enroll in as a Summer Scholar.

The Summer Institute uses the sample survey as the basic instrument for the scientific measurement of human activity. It presents sample survey methods in courses designed to meet the educational needs of those specializing in social and behavioral research such as professionals in business, public health, natural resources, law, medicine, nursing, social work, and many other domains of study.

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