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A genetic algorithm for optimal assembly of pairwise forced-choice questionnaires.

Rodrigo Schames Kreitchmann1, Francisco J Abad2, Miguel A Sorrel2

  • 1Department of Social Psychology and Methodology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Calle Iván Pavlov, 6, Ciudad Universitaria de Cantoblanco, 28049, Madrid, Spain. rodrigo.schames@uam.es.

Behavior Research Methods
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Summary

This study introduces a genetic algorithm (GA) to optimize multidimensional forced-choice questionnaires, improving trait estimate precision for personality assessments. The GA enhances validity in high-stakes scenarios, offering better accuracy than brute-force methods.

Keywords:
forced-choice formatgenetic algorithmsipsative datamultidimensional item response theoryreliabilitytest assembly

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Area of Science:

  • Psychometrics
  • Personality Assessment
  • Computational Psychology

Background:

  • Multidimensional forced-choice questionnaires offer improved validity for non-cognitive assessments in high-stakes settings.
  • A key limitation is the reduced precision of trait estimates inherent in this format.

Purpose of the Study:

  • To present an optimization procedure for constructing pairwise forced-choice questionnaires.
  • To maximize posterior marginal reliabilities by adapting a genetic algorithm (GA).

Main Methods:

  • A genetic algorithm (GA) was adapted for combinatorial optimization to assemble questionnaires.
  • A simulation study compared the GA with a quasi-brute-force (BF) search using five-factor model personality item pools.
  • Factors manipulated included questionnaire length, item pool size, and trait correlations.

Main Results:

  • The GA consistently yielded more accurate trait estimates than the BF search across all simulation conditions.
  • Improvements were particularly notable when dealing with correlated traits and larger relative item pool sizes.
  • The GA operated within reasonable computation times.

Conclusions:

  • The proposed GA-based optimization procedure effectively enhances the precision of trait estimates in multidimensional forced-choice questionnaires.
  • This method offers a practical solution for developing more valid and reliable personality assessments.
  • An accessible online implementation is available for broader use.