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Comparing Inference Methods for Non‐probability Samples

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  • Bart Buelens
  • Joep Burger
  • Jan A. van den Brakel

Abstract

Social and economic scientists are tempted to use emerging data sources like big data to compile information about finite populations as an alternative for traditional survey samples. These data sources generally cover an unknown part of the population of interest. Simply assuming that analyses made on these data are applicable to larger populations is wrong. The mere volume of data provides no guarantee for valid inference. Tackling this problem with methods originally developed for probability sampling is possible but shown here to be limited. A wider range of model‐based predictive inference methods proposed in the literature are reviewed and evaluated in a simulation study using real‐world data on annual mileages by vehicles. We propose to extend this predictive inference framework with machine learning methods for inference from samples that are generated through mechanisms other than random sampling from a target population. Describing economies and societies using sensor data, internet search data, social media and voluntary opt‐in panels is cost‐effective and timely compared with traditional surveys but requires an extended inference framework as proposed in this article.

Suggested Citation

  • Bart Buelens & Joep Burger & Jan A. van den Brakel, 2018. "Comparing Inference Methods for Non‐probability Samples," International Statistical Review, International Statistical Institute, vol. 86(2), pages 322-343, August.
  • Handle: RePEc:bla:istatr:v:86:y:2018:i:2:p:322-343
    DOI: 10.1111/insr.12253
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    1. Maciej Berk{e}sewicz & Greta Bia{l}kowska & Krzysztof Marcinkowski & Magdalena Ma'slak & Piotr Opiela & Robert Pater & Katarzyna Zadroga, 2019. "Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration," Papers 1908.06731, arXiv.org.
    2. María del Mar Rueda & Sergio Martínez-Puertas & Luis Castro-Martín, 2022. "Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles," Mathematics, MDPI, vol. 10(24), pages 1-19, December.
    3. Luis Castro-Martín & María del Mar Rueda & Ramón Ferri-García, 2020. "Estimating General Parameters from Non-Probability Surveys Using Propensity Score Adjustment," Mathematics, MDPI, vol. 8(11), pages 1-14, November.
    4. Maria del Mar Rueda, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1077-1081, December.
    5. Garcia Maria del Mar Rueda, 2023. "Book Review: Silvia Biffignandi and Jelke Bethlehem. Handbook of Web Surveys, 2nd edition. 2021 Wiley, ISBN: 978-1-119-37168-7, 624 pps," Journal of Official Statistics, Sciendo, vol. 39(4), pages 591-595, December.
    6. Ferri-García, Ramón & Castro-Martín, Luis & Rueda, María del Mar, 2021. "Evaluating Machine Learning methods for estimation in online surveys with superpopulation modeling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 186(C), pages 19-28.
    7. Luis Castro-Martín & Maria del Mar Rueda & Ramón Ferri-García, 2020. "Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    8. Susanne Jacobs, 2023. "“We Can Manage This Corona Disaster”: Psycho-Social Experiences of a Diverse Suburban Middle-Class Community in South Africa: Interview-Based Study," Societies, MDPI, vol. 13(4), pages 1-15, April.
    9. Ramón Ferri-García & María del Mar Rueda, 2022. "Variable selection in Propensity Score Adjustment to mitigate selection bias in online surveys," Statistical Papers, Springer, vol. 63(6), pages 1829-1881, December.
    10. Nijsiree Vongariyajit & Sooksan Kantabutra, 2021. "A Test of the Sustainability Vision Theory: Is It Practical?," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    11. Luis Castro-Martín & María del Mar Rueda & Ramón Ferri-García & César Hernando-Tamayo, 2021. "On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures," Mathematics, MDPI, vol. 9(23), pages 1-23, November.

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