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

ABSTRACT: 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 population. Several techniques have been proposed to tackle this issue. One such is Propensity Score Adjustment (PSA), which is widely used and has been analysed in various studies. The usual method of estimating the propensity score is logistic regression, which requires a reference probability sample in addition to the online nonprobability sample. The predicted propensities can be used for reweighting using various estimators. However, in the online survey context, there are alternatives that migh

SUBMITTER: Ferri-Garcia R 

PROVIDER: S-EPMC7176094 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature