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Scale abbreviation with supervised machine learning: A comparison of feature selection techniques.

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Summary
This summary is machine-generated.

Researchers can optimize survey data collection by abbreviating scales using supervised machine learning (SML). Evaluating seven feature selection methods with SML reveals no single best approach, guiding researchers in choosing optimal techniques for their specific needs.

Keywords:
Feature selectionPredictive powerPsychometric qualityScale abbreviationSupervised machine learning

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Area of Science:

  • Psychometrics
  • Data Science
  • Survey Methodology

Background:

  • Scale abbreviation is vital for reducing response burden in surveys.
  • Supervised machine learning (SML) offers accurate prediction of total scores for abbreviated scales.
  • Limited research exists on evaluating SML-abbreviated scales using both SML and psychometric metrics across feature selection techniques.

Purpose of the Study:

  • To evaluate seven feature selection methods (ITC, MRMR, Lasso, SFS, SBS, GA, NSGA-II) with SML for scale abbreviation.
  • To compare the psychometric properties of SML methods against two non-SML approaches.
  • To provide guidance on selecting appropriate feature selection methods for scale abbreviation.

Main Methods:

  • Simulated datasets with varying sample sizes, model error, and factorial correlations were used.
  • Evaluated predictive accuracy, reliability, and recovery of inter-subscale and external criterion correlations.
  • Compared seven feature selection techniques (ITC, MRMR, Lasso, SFS, SBS, GA, NSGA-II) in conjunction with SML.

Main Results:

  • No single feature selection method consistently outperformed others across all simulated conditions.
  • Specific feature selection techniques demonstrated superior performance under particular dataset characteristics.
  • The study identified key insights for researchers to select feature selection methods based on their data and objectives.

Conclusions:

  • The effectiveness of SML-based scale abbreviation depends on the chosen feature selection method and dataset properties.
  • Researchers should carefully consider their specific research goals and data characteristics when selecting feature selection techniques.
  • This study contributes to optimizing survey design and data collection through informed SML application.