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Bayesian hierarchical response time modelling-A tutorial.

Christoph Koenig1, Benjamin Becker2, Esther Ulitzsch3

  • 1Goethe University Frankfurt, Frankfurt am Main, Germany.

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Summary
This summary is machine-generated.

This tutorial introduces the lognormal response time model for psychometric and psychological research. It demonstrates how to implement this flexible Bayesian model and its extensions for analyzing cognitive and non-cognitive data.

Keywords:
Bayesian hierarchical modellingcognitive and non-cognitive response time modelsresponse timestutorial

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Area of Science:

  • Psychometrics
  • Psychology

Background:

  • Response time modeling is rapidly advancing in psychometrics and psychology.
  • Joint modeling of response times and responses stabilizes parameter estimation in item response theory.
  • Bayesian estimation techniques are crucial for response time models, but software implementations are limited.

Purpose of the Study:

  • To provide a tutorial on the lognormal response time model within a Bayesian hierarchical framework.
  • To guide researchers in specifying and estimating this model.
  • To showcase the model's flexibility and extensions for novel research questions.

Main Methods:

  • Discusses the lognormal response time model embedded in van der Linden's (2007) hierarchical framework.
  • Provides guidance on Bayesian hierarchical specification and estimation.
  • Illustrates model extensions for non-cognitive data, conditional dependencies, and mixture modeling.

Main Results:

  • The lognormal response time model is flexible and adaptable for various research needs.
  • Model extensions allow application to non-cognitive data (distance-difficulty hypothesis).
  • Conditional dependencies and mixture modeling can be incorporated to identify response behavior differences.

Conclusions:

  • The tutorial enhances understanding of response time model utility.
  • The lognormal model can be easily adapted and extended for cognitive and non-cognitive research.
  • These models address the growing need for analyzing complex response behaviors.