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Bayesian factor mixture modeling with response time for detecting careless respondents.

Lijin Zhang1, Esther Ulitzsch2,3, Benjamin W Domingue4

  • 1Graduate School of Education, Stanford University, 520 Galvez Mall, Stanford, CA, 94305, USA. lijinzhang@stanford.edu.

Behavior Research Methods
|September 15, 2025
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Summary
This summary is machine-generated.

This study introduces a Bayesian factor mixture modeling (FMM) approach that uses response time to identify careless respondents in research data. This method improves data quality and model accuracy by detecting individuals rushing through surveys.

Keywords:
Bayesian AnalysisFactor Mixture ModelingStructural Equation ModelingSurvey Research

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Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Data Science

Background:

  • Careless respondents introduce noise into research data, distorting findings and model fit.
  • Traditional methods for detecting careless responding, such as reverse-worded questions in factor mixture modeling (FMM), have limitations.

Purpose of the Study:

  • To introduce a novel Bayesian FMM that incorporates response time to identify careless respondents.
  • To enhance the accuracy and efficiency of detecting individuals who rush through questionnaires without engaging meaningfully with the items.

Main Methods:

  • Developed a Bayesian FMM that jointly models survey responses and response times.
  • Conducted simulation studies to evaluate the model's parameter estimation and classification accuracy.
  • Applied the model to mediation analyses and an empirical study to demonstrate its real-world applicability.

Main Results:

  • The proposed Bayesian FMM accurately estimates parameters and classifies respondents as attentive or careless within acceptable error rates.
  • Integrating response time information improved model convergence, classification precision, and estimation accuracy.
  • The model effectively identifies respondents rushing through questionnaires, distinguishing them from those genuinely reflecting measured traits.

Conclusions:

  • The Bayesian FMM with response time is a powerful tool for addressing careless responding in quantitative research.
  • This approach enhances data quality and strengthens the validity of research findings in social sciences.
  • An R function is provided to facilitate the implementation of this advanced methodology.