+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Handling missing data in coverage estimation, with application to the 1986 Test of Adjustment Related Operations

Handling missing data in coverage estimation, with application to the 1986 Test of Adjustment Related Operations

Survey Methodology 14(1): 87-97, 93-104

"This paper discusses methods used to handle missing data in post-enumeration surveys for estimating census coverage error, as illustrated for the 1986 Test of Adjustment Related Operations (Diffendal 1988). The methods include imputation schemes based on hot-deck and logistic regression models as well as weighting adjustments. The sensivity of undercount estimates from the 1986 test to variations in the imputation models is also explored." The test was carried out in Central Los Angeles County, California.

(PDF emailed within 1 workday: $29.90)

Accession: 046211454

Download citation: RISBibTeXText

PMID: 12315619

Related references

The 1986 Test of Adjustment Related Operations in Central Los Angeles County. Survey Methodology 14(1): 71-86, 75-92, 1988

Adjustment for missing data in complex surveys using doubly robust estimation: application to commercial sexual contact among Indian men. Epidemiology 21(6): 863-871, 2011

An expectation-maximization-likelihood-ratio test for handling missing data: application in experimental crosses. Genetics 169(2): 1021-1031, 2005

Handling missing data in propensity score estimation in comparative effectiveness evaluations: a systematic review. Journal of Comparative Effectiveness Research 7(3): 271-279, 2017

Handling missing values in population data: consequences for maximum likelihood estimation of haplotype frequencies. European Journal of Human Genetics 12(10): 805-812, 2004

Multiple Imputation Methods for Handling Missing Data in Cost-effectiveness Analyses That Use Data from Hierarchical Studies An Application to Cluster Randomized Trials. 2013

Multiple imputation methods for handling missing data in cost-effectiveness analyses that use data from hierarchical studies: an application to cluster randomized trials. Medical Decision Making 33(8): 1051-1063, 2014

Handling missing values in kernel methods with application to microbiology data. Neurocomputing 141: 110-116, 2014

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies. Metabolomics 14(10): 128, 2019

Handling missing data in transmission disequilibrium test in nuclear families with one affected offspring. Plos One 7(10): E46100, 2013

Time related coverage errors and the data adjustment factor (DAF). SRB research report (93-10) 54-59, 1993

Test the reliability of doubly robust estimation with missing response data. Biometrics 70(2): 289-298, 2015

Missing Data and the Rasch Model: The Effects of Missing Data Mechanisms on Item Parameter Estimation. Journal of Applied Measurement 20(2): 154-166, 2019

Variance Estimation Under Two-Phase Sampling with Application to Imputation for Missing Data. Biometrika 82(2): 453-460, 1995

Group-based estimation of missing hydrological data: II. application to streamflows. Hydrological Sciences Journal 45(6): 867-880, 2000