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Ignoring missing data and semi-parametric imputation effectively recover trait estimates in questionnaires. Semi-parametric methods offer the most precise results, highlighting the robustness of Rasch measurement principles.

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Data analysis

Background:

  • Handling missing data is crucial for accurate questionnaire analysis.
  • Traditional imputation methods may introduce bias or reduce precision.
  • Understanding the impact of missingness on trait estimation is essential.

Purpose of the Study:

  • To evaluate four distinct methods for handling missing data in discrete-choice questionnaires.
  • To compare the performance of these methods under varying conditions of missingness and questionnaire length.
  • To identify the most effective strategies for recovering accurate trait estimates.

Main Methods:

  • A simulation study was employed to assess method performance.
  • Four missing data handling techniques were examined: ignoring missingness, nearest-neighbor hot deck, multiple hot deck imputation, and semi-parametric multiple imputation.
  • Data were simulated with varying questionnaire lengths (10, 20, 41 items) and missingness percentages (10%, 25%, 40%) under the assumption of data missing completely at random.

Main Results:

  • Ignoring missing data and semi-parametric imputation demonstrated superior performance in recovering known trait levels across all simulated conditions.
  • Semi-parametric multiple imputation yielded the most precise trait estimates among the evaluated methods.
  • The study confirmed that ignoring missingness generally results in unbiased trait estimates, supporting Rasch measurement principles.

Conclusions:

  • Both ignoring missing data and semi-parametric imputation are effective strategies for handling missing data in polytomous questionnaires scored with the rating scale model.
  • Semi-parametric imputation offers enhanced precision for trait estimation.
  • The findings underscore the importance of specific objectivity in Rasch measurement, particularly when dealing with incomplete data.