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Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Traditional continuous response models may not capture nuanced respondent behaviors.
  • Identifying distinct respondent groups is crucial for accurate data interpretation.

Purpose of the Study:

  • To introduce a mixture extension of Samejima's continuous response model for continuous outcomes.
  • To evaluate an estimation approach using limited-information factor analysis.
  • To demonstrate the model's ability to detect distinct respondent groups.

Main Methods:

  • Developed a mixture extension of Samejima's continuous response model.
  • Employed a heuristic estimation approach based on limited-information factor analysis.
  • Validated the approach using an empirical dataset and a Monte Carlo simulation study.

Main Results:

  • The model successfully identified two distinct respondent groups with differing response behaviors.
  • The heuristic estimation approach yielded reliable parameter estimates.
  • Model convergence rates exceeded 80% with sample sizes of 250 and 90% with 500-1,000 participants.

Conclusions:

  • The mixture extension of Samejima's model is effective for identifying heterogeneous respondent groups.
  • The heuristic estimation method is reliable and performs well under various conditions.
  • The findings support the use of this model for analyzing continuous measurement outcomes.