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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Propensity score adjustment using machine learning classification algorithms to control selection bias in online

Ramón Ferri-García1, María Del Mar Rueda1

  • 1Department of Statistics and Operations Research, Faculty of Sciences, University of Granada, Granada, Spain.

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Machine learning algorithms effectively reduce online survey selection bias using Propensity Score Adjustment (PSA). These methods outperform traditional logistic regression, though performance varies with data characteristics and selection mechanisms.

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Area of Science:

  • Statistics
  • Survey Methodology
  • Machine Learning

Background:

  • Online surveys often suffer from selection bias due to self-selected participants.
  • Propensity Score Adjustment (PSA) is a common technique to address this bias.
  • Logistic regression is the standard method for estimating propensity scores in PSA.

Purpose of the Study:

  • To evaluate the efficiency of Machine Learning (ML) algorithms for propensity score estimation in PSA.
  • To compare ML models against logistic regression in mitigating online survey selection bias.

Main Methods:

  • Two simulation scenarios mimicking online survey conditions were used.
  • Propensity scores were estimated using both logistic regression and various ML classification algorithms.
  • PSA was applied using the estimated propensity scores for reweighting.

Main Results:

  • ML algorithms demonstrated superior performance in reducing selection bias compared to logistic regression.
  • The effectiveness of ML models was contingent upon the specific selection mechanism and data dimensionality.
  • Certain ML approaches showed greater bias reduction than others.

Conclusions:

  • Machine learning offers a more effective approach to bias correction in online surveys via PSA.
  • Careful consideration of the data and selection process is crucial when implementing ML for propensity estimation.
  • Further research into ML applications for survey bias adjustment is warranted.