[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125912-en":3,"doc-seo-125912-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125912,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Estimating Response Propensities in Nonprobability Surveys Using Machine Learning Weighted Models","Propensity Score Adjustment (PSA) reduces selection bias in nonprobability surveys by estimating each individual’s (unknown) response probability using a reference probability sample. This representation reflects differences between the target population and the nonprobability sample across auxiliary variables. When complex sampling designs generate auxiliary probability samples, design weights are crucial. While weighted linear models yield consistent inverse probability weighting estimators, the benefit of design weights in machine-learning classifiers remains unclear. The study examines PSA with weighted machine-learning classifiers via theory and simulations.","Mathematics and Computers in Simulation 225 (2024) 779–793  \n| Original articles\u003Cbr>Estimating response propensities in nonprobability surveys using machine learning weighted models\u003Cbr>Ramón Ferri-Garcíaa,∗, Jorge L. Rueda-Sánchez c, María del Mar Rueda a, Beatriz Cobob\u003Cbr>a Department of Statistics and Operations Research, University of Granada, Avenida Fuentenueva, s/n, Granada, 18017, Spain b Department of Quantitative Methods for Economics and Business, University of Granada, Campus Universitario de\u003Cbr>Cartuja, Granada, 18071, Spain\u003Cbr>c Mathematics Institute of the University of Granada (IMAG), Calle Ventanilla, 11, 18001, Granada, Spain |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Propensity score adjustment Design weights Nonprobability samples |  | Propensity Score Adjustment (PSA) is a widely accepted method to reduce selection bias in nonprobability samples. In this approach, the (unknown) response probability of each individual is estimated in a nonprobability sample, using a reference probability sample. This, the researcher obtains a representation of the target population, reflecting the differences (fora set of auxiliary variables) between the population and the nonprobability sample, from which response probabilities can be estimated.\u003Cbr>Auxiliary probability samples are usually produced by surveys with complex sampling designs, meaning that the use of design weights is crucial to accurately calculate response probabilities. When a linear model is used for this task, maximising a pseudo log-likelihood function which involves design weights provides consistent estimates for the inverse probability weighting estimator. However, little is known about how design weights may benefit the estimates when techniques such as machine learning classifiers are used.\u003Cbr>This study aims to investigate the behaviour of Propensity Score Adjustment with machine learning classifiers, subject to the use of weights in the modelling step. A theoretical approximation to the problem is presented, together with a simulation study highlighting the properties of estimators using different types of weights in the propensity modelling step. |  |\n\n1. Introduction  \nNovel information-gathering methods, such as online or smartphone surveys, have many advantages in terms of lower costs, higher response rates and broader questionnaire possibilities, making them attractive for practitioners considering a finite population. However, these surveys are usually self-administered, beyond the researcher’s control, thus generating a nonprobability sample.  \nIn a probability sampling design, all the individuals of the finite population of interest have a known or calculable probability of being included in the sample. If this condition does not apply, we have a nonprobability sample, which may be subject to selection bias, i.e. the sampled population may be different from the nonsampled population in a way that could affect the study variable of interest [1].  \n∗ Corresponding author.  \nE-mail address: [rferri@ugr.es](rferri@ugr.es) (R. Ferri-García).  \n[https://doi.org/10.1016/j.matcom.2024.06.012](https://doi.org/10.1016/j.matcom.2024.06.012)  \nReceived 6 November 2023; Received in revised form 6 June 2024; Accepted 18 June 2024 Available online 22 June 2024  \n0378-4754/© 2024 The Authors. Published by Elsevier B.V. on behalf of International Association for Mathematics and Computers in Simulation (IMACS). This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).  \nAdjustment methods have been proposed to overcome or reduce the selection bias produced in nonprobability samples, but the application and effectiveness of any such method depends on the amount of auxiliary information available. Commonly, a probability sample from the same population is available, from which some auxiliary variables in common w","cbCaipFpKKqLotzR","https://ap.wps.com/l/cbCaipFpKKqLotzR","pdf",613562,7,1,15,"English","en",105,"# Introduction\n## Nonprobability sampling and selection bias\n## Reference samples and auxiliary information\n## Propensity modelling approaches and the role of design weights\n# Propensity Score Adjustment and machine learning weighted models","[{\"question\":\"What is Propensity Score Adjustment (PSA) used for in nonprobability surveys?\",\"answer\":\"PSA is used to reduce selection bias by estimating each individual’s response probability in a nonprobability sample using a reference probability sample and auxiliary variables.\"},{\"question\":\"Why are design weights important in reference probability samples?\",\"answer\":\"Reference samples often come from complex sampling designs, so design weights are needed to correctly estimate response probabilities and maintain an accurate representation of the target population.\"},{\"question\":\"How does the study extend PSA using machine learning?\",\"answer\":\"The study investigates PSA behavior when machine-learning classifiers are used for propensity estimation, focusing on how incorporating design weights in the modelling (training) step affects estimator properties.\"}]","Estimating Response Propensities in Nonprobability Surveys Using Machine Learning Weighted Models | 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is Propensity Score Adjustment (PSA) used for in nonprobability surveys?","Question",{"text":77,"@type":78},"PSA is used to reduce selection bias by estimating each individual’s response probability in a nonprobability sample using a reference probability sample and auxiliary variables.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Why are design weights important in reference probability samples?",{"text":82,"@type":78},"Reference samples often come from complex sampling designs, so design weights are needed to correctly estimate response probabilities and maintain an accurate representation of the target population.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the study extend PSA using machine learning?",{"text":86,"@type":78},"The study investigates PSA behavior when machine-learning classifiers are used for propensity estimation, focusing on how incorporating design weights in the modelling (training) step affects estimator 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