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  • In this paper we present robust weighting adjustments and imputation methods to compensate for selection bias in a nonprobability online web-survey taken from the WageIndicator (WI) programme (www.wageindicator.org). For the substantive study, we estimate the gender pay gap (GPG) using the 2016 WI survey data from the Netherlands. To calculate the adjustment weights, we use the 2016 EUSILC data as a reference sample. Based on the study of GPG, we show that the combination of predictive mean matching and robust weighting adjustment techniques are able to compensate for the selection bias in the nonprobability web survey and ameliorate outcomes of the Blinder-Oaxaca decomposition model in terms of the degree of similarity relative to patterns found in representative probability samples in the Netherlands. (xsd:string)
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  • 2021 (xsd:gyear)
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  • 2021 (xsd:gyear)
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  • 120 (xsd:string)
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  • Improving the reliability of a nonprobability web survey (xsd:string)
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  • CLADAG 2021 Book of Abstracts and Short Papers: 13th Scientific Meeting of the Classification and Data Analysis Group (xsd:string)
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  • In CLADAG 2021 Book of Abstracts and Short Papers: 13th Scientific Meeting of the Classification and Data Analysis Group, edited by Porzio, Giovanni C. and Rampichini, Carla and Bocci, Chiara, 120-123, Firenze University Press, 2021 (xsd:string)
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