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Background <p>Missing survey data can threaten the validity and generalizability of findings from longitudinal cohort studies. Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data in which respondents begin but do not finish a survey, that can lead to biased conclusions. The objectives of the present research are to demonstrate how machine learning can identify survey non-completion and to characterize individual and methodological factors that are associated with this form of data missingness.</p> Methods <p>The present study developed a novel machine learning algorithm to characterize survey non-completion in the Millennium Cohort Study during the 2019–2021 data collection cycle that included a 30- to 45-min paper or web-based follow-up survey for previously enrolled panels (Panels 1–4, <i>n</i> = 80,986) and a 30- to 45-min web-based baseline survey for new enrollees (Panel 5, <i>n</i> = 58,609). We then examined the effect of individual characteristics and survey attributes on survey non-completion.</p> Results <p>This algorithm achieved 99% accuracy and showed that 0.29% of follow-up respondents and 15.43% of new enrollees were survey non-completers. Our findings suggest that certain military and sociodemographic characteristics (e.g., enlisted pay grades) were associated with increased survey non-completion in the 2019–2021 cycle. Survey attributes explained a large proportion of the variability in survey non-completion, with our analyses indicating a higher likelihood of survey non-completion in Sects.&#xa0;(1) located toward the beginning of the survey, (2) with sensitive questions, and (3) with fewer questions.</p> Conclusion <p>This research highlights the importance of accounting for potential respondent bias due to survey non-completion and identifies factors associated with this type of missing data.</p>

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Characterizing individual and methodological risk factors for survey non-completion using machine learning: findings from the U.S. Millennium Cohort Study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月14日

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  • Nate C. Carnes,
  • Claire A. Kolaja,
  • Crystal L. Lewis,
  • Sheila F. Castañeda,
  • Rudolph P. Rull,
  • Ania Bukowinski,
  • Felicia Carey,
  • Toni Geronimo-Hara,
  • Clinton Hall,
  • Judy Harbertson,
  • David Ignacio,
  • Isabel G. Jacobson,
  • Cynthia A. LeardMann,
  • Vanessa Perez,
  • Aprilyn Piega,
  • Anna Rivera,
  • Rosa Salvatier,
  • Neika Sharifian,
  • Steven Speigle,
  • Daniel Trone,
  • Javier Villalobos,
  • Katie Zhu

摘要

Background

Missing survey data can threaten the validity and generalizability of findings from longitudinal cohort studies. Respondent characteristics and survey attributes may contribute to patterns of survey non-completion, a form of missing data in which respondents begin but do not finish a survey, that can lead to biased conclusions. The objectives of the present research are to demonstrate how machine learning can identify survey non-completion and to characterize individual and methodological factors that are associated with this form of data missingness.

Methods

The present study developed a novel machine learning algorithm to characterize survey non-completion in the Millennium Cohort Study during the 2019–2021 data collection cycle that included a 30- to 45-min paper or web-based follow-up survey for previously enrolled panels (Panels 1–4, n = 80,986) and a 30- to 45-min web-based baseline survey for new enrollees (Panel 5, n = 58,609). We then examined the effect of individual characteristics and survey attributes on survey non-completion.

Results

This algorithm achieved 99% accuracy and showed that 0.29% of follow-up respondents and 15.43% of new enrollees were survey non-completers. Our findings suggest that certain military and sociodemographic characteristics (e.g., enlisted pay grades) were associated with increased survey non-completion in the 2019–2021 cycle. Survey attributes explained a large proportion of the variability in survey non-completion, with our analyses indicating a higher likelihood of survey non-completion in Sects. (1) located toward the beginning of the survey, (2) with sensitive questions, and (3) with fewer questions.

Conclusion

This research highlights the importance of accounting for potential respondent bias due to survey non-completion and identifies factors associated with this type of missing data.