Geocoding to Create Survey Frames

Eckman, Stephanie; English, Ned

doi:10.29115/sp-2012-0026

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…For example, segments were sampled within two months from being fielded, minimizing the risk of new construction. Census block groups were also used as the segment unit to minimize the risk of geocoding error (Eckman and English 2012; Shook-Sa et al 2010). Despite our best efforts, our analyses may slightly underestimate the risk of bias.…”

Section: Methodsmentioning

confidence: 99%

“…Several features may affect geocoding accuracy. For example, the smaller the sampling geography, the higher the required geocoding precision, and the higher the risk of geocoding error (Eckman and English 2012; Shook-Sa et al 2010). Moreover, geocoding error in rural areas has been found to be as high as 15 percent compared to an average of 2 percent in urban and suburban areas (Dohrmann et al 2006).…”

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confidence: 99%

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Does Undercoverage on the U.S. Address-based Sampling Frame Translate to Coverage Bias?

Amaya

Zimmer

Morton

et al. 2018

Sociological Methods & Research

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Address-based sampling (ABS) refers to the use a list of addresses derived from the U.S. Postal Service’s Computerized Delivery Sequence File as a sampling frame. While most residential addresses are included on an ABS frame, it still suffers from undercoverage. Undercoverage is problematic only if the uncovered units have different attributes than covered units. In this article, we assess the risk of observing coverage bias on a variety of univariate point estimates in an in-person survey. We accomplish this task using a combination of survey data and Monte Carlo simulation models to vary the coverage rate and the target geography. For some variables, we found low risk of observing coverage bias. But, for others, the risk of observing bias was high for nearly all levels of coverage. Finally, we did not observe any trends by variable type that would give us the ability to predict a priori which variables may suffer bias.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%