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A Generalized Speed-Accuracy Response Model for Dichotomous Items.

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  • 1ETS Global, Amsterdam, The Netherlands. pvanrijn@ets.org.

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Summary
This summary is machine-generated.

A new two-parameter speed-accuracy response model (SARM) offers improved fit over the one-parameter version. This enhanced model, estimated using an expectation-maximization algorithm, better captures item response data, including speed and accuracy.

Keywords:
expectation–maximizationgeneralized residualsitem response theoryresponse timessaddlepoint approximationsscoring rulesspeed–accuracy

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Area of Science:

  • Psychometrics
  • Cognitive modeling
  • Statistical modeling

Background:

  • The speed-accuracy response model (SARM) is used to model scores incorporating response speed and accuracy.
  • Existing models may not fully capture the nuances of speed-accuracy trade-offs in cognitive tasks.

Purpose of the Study:

  • To propose and evaluate a generalized two-parameter speed-accuracy response model (SARM).
  • To develop an expectation-maximization (EM) algorithm for parameter estimation and standard error calculation.
  • To provide methods for assessing model fit using generalized residuals and saddlepoint approximations.

Main Methods:

  • Generalization of the one-parameter SARM to a two-parameter model, analogous to Rasch to Birnbaum models in IRT.
  • Development of an expectation-maximization (EM) algorithm for parameter estimation.
  • Implementation of generalized residuals and saddlepoint approximations for model fit assessment.

Main Results:

  • A simulation study demonstrated good parameter recovery and acceptable Type I error rates for the proposed methods.
  • Application to two real data sets showed that the two-parameter SARM provided a better fit than the one-parameter SARM.
  • The developed EM algorithm and model fit assessment tools were effective in analyzing the data.

Conclusions:

  • The proposed two-parameter SARM represents a valuable extension of existing speed-accuracy modeling techniques.
  • The developed estimation and model fit procedures are robust and applicable to real-world cognitive data.
  • This enhanced model offers a more nuanced understanding of the speed-accuracy trade-off in psychological and cognitive research.