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One Dimensional Turing-Like Handshake Test for Motor Intelligence
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A graded response model for measuring person reliability.

Pere Joan Ferrando1

  • 1Facultad de Psicologia, 'Rovira i Virgili' University, Tarragona, Spain. perejoan.ferrando@urv.cat

The British Journal of Mathematical and Statistical Psychology
|January 23, 2009
PubMed
Summary

This study introduces a new graded response model (GRM) for item response theory, extending Lumsden's model. The new GRM accurately estimates person parameters and fits personality measurement data.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Statistics

Background:

  • Lumsden's fluctuation model provides a foundation for understanding response variability.
  • Existing item response theory models may not fully capture nuances in graded response data.
  • Accurate measurement of individual differences is crucial in personality assessment.

Purpose of the Study:

  • To extend Lumsden's fluctuation model to a graded response context.
  • To develop a novel one-dimensional item response theory graded response model (GRM).
  • To investigate the approximation of item category response functions (ICRFs) to standard GRM with equal discrimination.

Main Methods:

  • Development of a new graded response model (GRM) based on Lumsden's fluctuation model.
  • Mathematical derivation of item category response functions (ICRFs).
  • Simulation studies to assess the accuracy of person parameter estimation.

Main Results:

  • The proposed GRM's ICRFs closely approximate those of the standard GRM under specific assumptions.
  • Item responses are determined by person central location and person reliability parameters.
  • The model demonstrates good fit and accurate estimation of person parameters in empirical examples.

Conclusions:

  • The developed GRM offers a viable alternative for analyzing graded response data in personality measurement.
  • The model provides a robust framework for estimating individual differences.
  • The study validates the model's utility through simulation and empirical application.