A Comparison of Optimization Algorithms for Forced-Choice Questionnaire Assembly

Authors

  • Scarlett Escudero Orcid
    Department of Educational Psychology, University of Minnesota, Minneapolis, MN, USA; Department of Social Psychology and Methodology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
  • Miguel A. Sorrel Orcid
    Department of Social Psychology and Methodology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
  • Rodrigo S. Kreitchmann Orcid
    Department of Methodology of Behavioral Sciences, Faculty of Psychology, Universidad Nacional de Educación a Distancia, Madrid, Spain
  • Francisco J. Abad Orcid
    Department of Social Psychology and Methodology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain

Abstract

Forced-choice questionnaires (FCQs) are increasingly favored over traditional Likert-type formats due to their reduced susceptibility to faking and social desirability (SD). Their construction typically involves pairing items from existing single-stimulus banks. This study compares four methods for assembling FCQs: a genetic algorithm (GA), two simulated annealing (SA) strategies (blueprint-based and scale-parameter-optimized), and brute-force (BF) random search. These methods are evaluated via simulation and an empirical example, focusing on trait score recovery. The effects of questionnaire length and SD matching on recovery are also examined. Three item banks varying in the aj-SDj relationship and inclusion of heteropolar blocks were used to assess performance across pairing scenarios. GA consistently produced the most reliable scores, followed by SA with aj optimization. All examined factors significantly affected reliability. GA is recommended for FCQ assembly, especially with short questionnaires, no heteropolar blocks, and high aj-SDj correlation.