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Oops, we missed a spot: Comparing data substitution methods for non-random missing survey data in a longitudinal

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When advanced missing data methods fail, Last Observation Carried Forward (LOCF) proved more accurate than mean substitution or regression imputation for estimating child anxiety levels. This comparison aids researchers in data processing decisions.

Keywords:
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Area of Science:

  • Psychology
  • Statistics
  • Data Science

Background:

  • Missing data imputation is crucial in longitudinal studies.
  • Standard imputation methods may not apply when data are entirely missing.
  • Evaluating alternative substitution methods is necessary for reliable data analysis.

Purpose of the Study:

  • To compare the accuracy of three missing data imputation methods: mean substitution, Last Observation Carried Forward (LOCF), and regression-predicted values.
  • To determine the most reliable method for estimating missing child anxiety data in a longitudinal study.
  • To provide guidance for researchers facing missing data challenges.

Main Methods:

  • Utilized data from the Ontario COVID-19 and Kids' Mental Health Study, involving 384 parents reporting on child anxiety.
  • Applied mean substitution, LOCF, and regression imputation to estimate missing survey items at one and four months post-baseline.
  • Conducted Within-Subjects ANOVA and post-hoc analyses to compare imputation methods against actual data.

Main Results:

  • Mean substitution significantly differed from actual data.
  • Regression-predicted values overestimated the median compared to actual values.
  • LOCF produced comparable means and identical medians to actual data, indicating higher accuracy and reliability.

Conclusions:

  • LOCF is a more accurate and reliable imputation method than mean substitution or regression-predicted values when advanced methods are not feasible.
  • Systematic comparison of alternative imputation methods is vital for informed data processing decisions.
  • Findings support the use of LOCF for estimating missing child anxiety data in similar research contexts.