Combining Data from New and Traditional Sources in Population Surveys

Thompson, Mary E.

doi:10.1111/insr.12292

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…I have not discussed other practical issues related to big data and nonprobability samples, such as privacy, access and transparency, and I refer the reader to the following overview and appraisal papers: Baker et al (2013), Brick (2011), Citro (2014, Couper (2013), Elliott and Valliant (2017), Groves (2011), Kalton (2019) , Keiding and Louis (2016), Lohr and Raghunathan (2017), Mercer et al (2017), Tam and and Thompson (2019). The report by the National Academies of Sciences, Engineering, and Medicine (2017) extensively treated the privacy issue, in addition to methodology for integrating data from multiple sources.…”

Section: Discussionmentioning

confidence: 99%

On Making Valid Inferences by Integrating Data from Surveys and Other Sources

Rao

2020

Sankhya B

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Survey samplers have long been using probability samples from one or more sources in conjunction with census and administrative data to make valid and efficient inferences on finite population parameters. This topic has received a lot of attention more recently in the context of data from non-probability samples such as transaction data, web surveys and social media data. In this paper, I will provide a brief overview of probability sampling methods first and then discuss some recent methods, based on models for the non-probability samples, which could lead to useful inferences from a non-probability sample by itself or when combined with a probability sample. I will also explain how big data may be used as predictors in small area estimation, a topic of current interest because of the growing demand for reliable local area statistics.

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Section: Discussionmentioning

confidence: 99%

On Making Valid Inferences by Integrating Data from Surveys and Other Sources

Rao

2020

Sankhya B

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“…Most methods in this area focus on estimation of the population mean and derive from the causal inference literature, repurposing tools used to control for confounding to now address variables influencing selection (Mercer et al, 2017). These methods also require a reference probability sample or auxiliary population data to properly calibrate the non-probability sample and avoid bias (Thompson, 2019). While many methods still fall within the frequentist paradigm, there are some recent advances that have incorporated Bayesian modeling (Rafei et al, 2020(Rafei et al, , 2022Tan et al, 2019).…”

Section: Future Directions For Health Disparities Researchmentioning

confidence: 99%

Bayesian estimation methods for survey data with potential applications to health disparities research

Wu,

Stephenson

2023

WIREs Computational Stats

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Understanding how and why certain communities bear a disproportionate burden of disease is challenging due to the scarcity of data on these communities. Surveys provide a useful avenue for accessing hard‐to‐reach populations, as many surveys specifically oversample understudied and vulnerable populations. When survey data is used for analysis, it is important to account for the complex survey design that gave rise to the data, to avoid biased conclusions. The field of Bayesian survey statistics aims to account for such survey design while leveraging the advantages of Bayesian models, which can flexibly handle sparsity through borrowing of information and provide a coherent inferential framework to easily obtain variances for complex models and data types. For these reasons, Bayesian survey methods seem uniquely well‐poised for health disparities research, where heterogeneity and sparsity are frequent considerations. This review discusses three main approaches found in the Bayesian survey methodology literature: (1) multilevel regression and post‐stratification, (2) weighted pseudolikelihood‐based methods, and (3) synthetic population generation. We discuss the advantages and disadvantages of each approach, examine recent applications and extensions, and consider how these approaches may be leveraged to improve research in population health equity.This article is categorized under: Applications of Computational Statistics > Clinical Trials Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Applications of Computational Statistics > Health and Medical Data/Informatics Statistical and Graphical Methods of Data Analysis > Sampling

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“…Combining survey data and other data source like administrative data, transaction data, social media data etc. is to produce more efficient information [3,10,11,14]. Methodologically record linkage and statistical matching techniques can be used to combine multiple data sources.…”

Section: Transition To Combining Data From Multiple Sourcesmentioning

confidence: 99%

Impact of Covid-19 on Survey Methods and Challenges

Kim

2021

AJBSR

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Combining Data from New and Traditional Sources in Population Surveys

Cited by 10 publications

References 39 publications

On Making Valid Inferences by Integrating Data from Surveys and Other Sources

On Making Valid Inferences by Integrating Data from Surveys and Other Sources

Bayesian estimation methods for survey data with potential applications to health disparities research

Impact of Covid-19 on Survey Methods and Challenges

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