Supplementing small probability samples with nonprobability samples: A Bayesian approach


Sakshaug, Joseph W. ; Perez Ruiz, Diego Andres ; Wiśniowski, Arkadiusz ; Blom, Annelies G.


DOI: https://doi.org/10.2478/jos-2019-0027
URL: https://content.sciendo.com/view/journals/jos/35/3...
Additional URL: https://www.researchgate.net/publication/335720025...
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: Journal of Official Statistics : JOS
Volume: 35
Issue number: 3
Page range: 653-681
Place of publication: Stockholm
Publishing house: Statistics Sweden
ISSN: 0282-423X , 2001-7367
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
School of Social Sciences > Methoden d. empirischen Sozialforschung insbes. Internet Panel Survey-Forschung (Blom 2017-)
Subject: 300 Social sciences, sociology, anthropology
Abstract: Carefully designed probability-based sample surveys can be prohibitively expensive to conduct. As such, many survey organizations have shifted away from using expensive probability samples in favor of less expensive, but possibly less accurate, nonprobability web samples. However, their lower costs and abundant availability make them a potentially useful supplement to traditional probability-based samples. We examine this notion by proposing a method of supplementing small probability samples with nonprobability samples using Bayesian inference. We consider two semi-conjugate informative prior distributions for linear regression coefficients based on nonprobability samples, one accounting for the distance between maximum likelihood coefficients derived from parallel probability and non-probability samples, and the second depending on the variability and size of the nonprobability sample. The method is evaluated in comparison with a reference prior through simulations and a real-data application involving multiple probability and nonprobability surveys fielded simultaneously using the same questionnaire. We show that the method reduces the variance and mean-squared error (MSE) of coefficient estimates and model-based predictions relative to probability-only samples. Using actual and assumed cost data we also show that the method can yield substantial cost savings (up to 55%) for a fixed MSE.

Dieser Eintrag ist Teil der Universitätsbibliographie.




+ Citation Example and Export

Sakshaug, Joseph W. ; Perez Ruiz, Diego Andres ; Wiśniowski, Arkadiusz ; Blom, Annelies G. (2019) Supplementing small probability samples with nonprobability samples: A Bayesian approach. Journal of Official Statistics : JOS Stockholm 35 3 653-681 [Article]


+ Search Authors in

+ Page Views

Hits per month over past year

Detailed information



You have found an error? Please let us know about your desired correction here: E-Mail


Actions (login required)

Show item Show item