Propensity score adjustment using machine learning classification algorithms to control selection bias in online surveys

Modern survey methods may be subject to non-observable bias, from various sources. Among online surveys, for example, selection bias is prevalent, due to the sampling mechanism commonly used, whereby participants self-select from a subgroup whose characteristics differ from those of the target popul...

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Bibliographic Details
Published in:	PloS one Vol. 15; no. 4; p. e0231500
Main Authors:	Ferri-García, Ramón, Rueda, María del Mar
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 22.04.2020 Public Library of Science (PLoS)
Subjects:	Algorithms Bias Classification Computer and Information Sciences Computer simulation Computer Simulation - statistics & numerical data Data Interpretation, Statistical Engineering and Technology Humans Internet Internet access Learning algorithms Logistic Models Machine learning Machine Learning - statistics & numerical data Methods Neural networks Operations research Physical Sciences Polls & surveys Probability Propensity Score Regression analysis Research and Analysis Methods Research Design - statistics & numerical data Selection Bias Statistical analysis Studies Subgroups Surveys Surveys and Questionnaires - statistics & numerical data Variables Volunteers Spain
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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