Blending domain estimates from two victimization surveys with possible bias

Lohr, Sharon L.; Brick, J. Michael

doi:10.1002/cjs.11153

Cited by 9 publications

(9 citation statements)

References 35 publications

(47 reference statements)

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“…The availability of incident‐level police data through NCS‐X and NIBRS (Strom and Smith, , this issue) means we will have an alternative to modeling that should provide better data on crime in some subnational areas. Use of incident‐level data on offenses reported to the police allows these data to be blended with incident‐level victimization survey data that can then be used to produce not only jurisdiction‐level estimates but also jurisdiction‐specific estimates for subgroups or subclasses of events in the jurisdiction (Lohr and Brick, ). Different weights are given to the police and survey data when they are blended, and adjustments are made for bias in the data sets.…”

Section: Second Redesign Of the Ncvsmentioning

confidence: 99%

Second Major Redesign of the National Crime Victimization Survey (NCVS)

Langton

Planty

Lynch

2017

Criminology & Public Policy

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B u r e a u o f J u s t i c e S t a t i s t i c s , U . S . D e p a r t m e n t o f J u s t i c e Michael Planty R T I , I n t e r n a t i o n a l James P. Lynch U n i v e r s i t y o f M a r y l a n d Research SummarySince 1973, the National Crime Victimization Survey (NCVS) has served as a key source of data on the level and nature of crime and its consequences. To keep pace with the changing landscape of crime, as well as with the technological advances in survey research, occasional "redesigns" of the survey are necessary to modernize and improve the utility of data collected. Policy ImplicationsThis article describes recent and ongoing efforts to redesign (a) the NCVS survey instruments to reflect the changing demographics of victims, emerging crime types, and timely issues related to victimization risk and victim responses to crime and (b) the NCVS sample to generate state and local estimates of crime. The implications of these improvements for researchers and policy makers are discussed.

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Section: Second Redesign Of the Ncvsmentioning

confidence: 99%

Second Major Redesign of the National Crime Victimization Survey (NCVS)

Langton

Planty

Lynch

2017

Criminology & Public Policy

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“…However, her model assumed that all surveys were equally likely to be biased and the bias across countries canceled each other out. There are a handful of works that account for pooling a gold-standard source with potentially biased sources [7][8][9][10][11]. Earlier, Mosteller [9] studied ways to combine the means from two samples when one is potentially biased.…”

Section: Introductionmentioning

confidence: 99%

“…Mosteller's estimator, chosen as one end of the methods, will be discussed further in the following section. Lohr and Brick [7] explored methods for pooling domain-level estimates from two surveys that measure victimization prevalence: their gold-standard survey, the United States National Crime Victimization Survey, and a larger but potentially biased telephone companion survey. In their study, they compared ten methods that combine a gold-standard survey with another biased data source.…”

Section: Introductionmentioning

confidence: 99%

Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city

Kim

Shankar

2020

BMC Med Res Methodol

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Background: Electronic Health Records (EHR) has been increasingly used as a tool to monitor population health. However, subject-level errors in the records can yield biased estimates of health indicators. There is an urgent need for methods to estimate the prevalence of health indicators using large and real-time EHR while correcting the potential bias. Methods: We demonstrate joint analyses of EHR and a smaller gold-standard health survey. We first adopted Mosteller's method that pools two estimators, among which one is potentially biased. It only requires knowing the prevalence estimates from two data sources and their standard errors. Then, we adopted the method of Schenker et al., which uses multiple imputations of subject-level health outcomes that are missing for the subjects in EHR. This procedure requires information to link some subjects between two sources and modeling the mechanism of misclassification in EHR as well as modeling inclusion probabilities to both sources. Results: In a simulation study, both estimators yielded negligible bias even when EHR was biased. They performed as well as health survey estimator when EHR bias was large and better than health survey estimator when EHR bias was moderate. It may be challenging to model the misclassification mechanism in real data for the subject-level imputation estimator. We illustrated the methods analyzing six health indicators from 2013 to 14 NYC HANES and the 2013 NYC Macroscope, and a study that linked some subjects in both data sources. Conclusions: When a small gold-standard health survey exists, it can serve as a safeguard against potential bias in EHR through the joint analysis of the two sources.

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“…To our knowledge, nothing has been proposed that would be suitable for the multivariate spatio-temporal data described previously. In fact, much of the literature involves simplifications that are not appropriate for our setting; for example, Kern & Borgman (2008), Merkouris (2012) and Kim & Rao (2012) assume marginal independence between surveys and Keller & Olkin (2002), Lohr & Brick (2012), Elliott & Davis (2013), Maples & Brault (2013), Bec & Mogliani (2015) and Lin et al (2016) consider combining summary statistics computed from multiple surveys. Alternatively, we use a hierarchical Bayesian approach for survey fusion and condition on every survey under consideration (e.g., Raghunathan et al, 2007;Wang et al, 2012;Bryant & Graham, 2013;Nandram et al, 2014;Cruze, 2015;and Giorgi et al, 2015, for other examples of Bayesian approaches to survey fusion).…”

Section: Introductionmentioning

confidence: 99%

Multivariate spatio‐temporal survey fusion with application to the American Community Survey and Local Area Unemployment Statistics

Bradley

Holan

Wikle

2016

Stat

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There are often multiple surveys available that estimate and report related demographic variables of interest that are referenced over space and/or time. Not all surveys produce the same information, and thus, combining these surveys typically leads to higher quality estimates. That is, not every survey has the same level of precision nor do they always provide estimates of the same variables. In addition, various surveys often produce estimates with incomplete spatio‐temporal coverage. By combining surveys using a Bayesian approach, we can account for different margins of error and leverage dependencies to produce estimates of every variable considered at every spatial location and every time point. Specifically, our strategy is to use a hierarchical modelling approach, where the first stage of the model incorporates the margin of error associated with each survey. Then, in a lower stage of the hierarchical model, the multivariate spatio‐temporal mixed effects model is used to incorporate multivariate spatio‐temporal dependencies of the processes of interest. We adopt a fully Bayesian approach for combining surveys; that is, given all of the available surveys, the conditional distributions of the latent processes of interest are used for statistical inference. To demonstrate our proposed methodology, we jointly analyze period estimates from the US Census Bureau's American Community Survey, and estimates obtained from the Bureau of Labor Statistics Local Area Unemployment Statistics program. Copyright © 2016 John Wiley & Sons, Ltd.

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Blending domain estimates from two victimization surveys with possible bias

Cited by 9 publications

References 35 publications

Second Major Redesign of the National Crime Victimization Survey (NCVS)

Second Major Redesign of the National Crime Victimization Survey (NCVS)

Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city

Multivariate spatio‐temporal survey fusion with application to the American Community Survey and Local Area Unemployment Statistics

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