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https://doi.org/10.13094/SMIF-2020-00012
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Do supplemental list frames for subpopulations increase subpopulation sampling efficiency? Evidence from the National Household Food Acquisition and Purchase Survey
[journal article]
Abstract Multiple-frame sampling has been regarded as a device for increasing efficiency in identifying small subpopulations. However, there has
been a lack of empirical evidence in supporting the efficiency of the multiple-frame approach and in guiding best practices. The current
study focuses on a specia... view more
Multiple-frame sampling has been regarded as a device for increasing efficiency in identifying small subpopulations. However, there has
been a lack of empirical evidence in supporting the efficiency of the multiple-frame approach and in guiding best practices. The current
study focuses on a special scenario in which two frames were used to recruit sample members. Using paradata from the U.S. National
Household Food Acquisition and Purchase Survey (FoodAPS), the current analysis focuses on recruiting households that received
Supplementary Nutrition Assistance Program (SNAP) as a sub-goal of the survey sampling. SNAP households account for around one-fifth
of the general U.S. population, compared to a survey goal of 30 percent of responding households. Our findings were consistent with
theoretical expectations. Having and using additional SNAP list frames improved the efficiency of identifying SNAP households as opposed
to screening a general address-based sample frame. This efficiency remained even as the SNAP list frames aged.... view less
Keywords
sampling theory; sample; survey research; United States of America; survey; private household
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords
Dual-frame sampling; recruitment efficiency; sampling efficiency; small subpopulations; Supplementary Nutrition Assistance Program; SNAP
Document language
English
Publication Year
2020
Page/Pages
p. 1-11
Journal
Survey Methods: Insights from the Field (2020)
ISSN
2296-4754
Status
Published Version; peer reviewed