000234387 001__ 234387
000234387 005__ 20180123004025.0
000234387 037__ $$a1496-2016-130638
000234387 041__ $$aen_US
000234387 245__ $$aCan Calibration Be Used to Adjust for “Nonignorable” Nonresponse?
000234387 260__ $$c2008-07
000234387 269__ $$a2008-07
000234387 300__ $$a22
000234387 336__ $$aReport
000234387 520__ $$aCalibration can be used to adjust for unit nonresponse when the model
variables on which the response/nonresponse mechanism depends do
not coincide with the benchmark variables in the calibration equation.
As a result, model-variable values need only known for the
respondents. This allows the treatment of what is usually considered
nonignorable nonresponse. Although one can invoke either quasirandomization
or prediction-model-based theory to justify the
calibration, both frameworks rely on unverifiable model assumptions,
and both require large samples to produce nearly unbiased estimators
even when those assumptions hold. We will explore these issues
theoretically and with an empirical study.
000234387 542__ $$fLicense granted by Mallory Pagel (pagel107@umn.edu) on 2016-04-15T21:32:15Z (GMT):

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000234387 650__ $$aResearch Methods/ Statistical Methods
000234387 6531_ $$aPrediction model
000234387 6531_ $$aQuasi-randomization
000234387 6531_ $$aBenchmark
000234387 6531_ $$aModel variable
000234387 6531_ $$aBias
000234387 6531_ $$aResponse-drive response group.
000234387 700__ $$aKott, Phillip S.
000234387 700__ $$aChang, Ted
000234387 8564_ $$s220705$$uhttp://ageconsearch.umn.edu/record/234387/files/jsm_for%20web.pdf
000234387 887__ $$ahttp://purl.umn.edu/234387
000234387 909CO $$ooai:ageconsearch.umn.edu:234387$$pGLOBAL_SET
000234387 912__ $$nSubmitted by Mallory Pagel (pagel107@umn.edu) on 2016-04-15T21:42:20Z
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  Previous issue date: 2008-07
000234387 982__ $$gUnited States Department of Agriculture>National Agricultural Statistics Service>NASS Research Reports
000234387 980__ $$a1496