Online Program Home
  My Program

Abstract Details

Activity Number: 462 - SPEED: Survey Research Methods
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #323247 View Presentation
Title: Investigating the Performance of Inverse Sampling for Model Estimation
Author(s): Zachary Haskell Seeskin* and Josiane Bechara and Qiao Ma and Edward Mulrow
Companies: NORC at the University of Chicago and NORC at the University of Chicago and NORC at the University of Chicago and NORC
Keywords: Analysis of complex survey data ; Resampling ; Public use data
Abstract:

Hinkins et al. (1997) introduced inverse sampling as a way to aid analysts navigating complex sample designs. One intent was to provide users a set of inverse samples that could each be analyzed using methods designed for simple random samples and then combined for inference. These techniques assume one has knowledge of the complex sample design and can properly invert the sample. For public use data, unless inverse samples are provided, a data user would be hard-pressed to create inverse samples based on the complex sample design. Our current research empirically investigates the performance of inverse sampling by comparing the resulting estimates to estimates that use the original sample and incorporate the survey design properly. We study the performance of inverse sampling both when the sampling design is known and when only the survey weights are known and thus only approximate inverse samples can be obtained. The results show that inverse sampling performs well for producing unbiased estimates of model coefficients, but caution is needed for estimating standard errors.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association