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Testing Interactions in Multinomial Processing Tree Models.

Beatrice G Kuhlmann1, Edgar Erdfelder1, Morten Moshagen2

  • 1Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany.

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|November 19, 2019
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Summary
This summary is machine-generated.

Multinomial processing tree (MPT) models can now test complex interactions, not just main effects. This extends psychological process analysis and can be implemented using the multiTree software.

Keywords:
associative deficit hypothesiscognitive aginginteractionsmultinomial processing tree modelsparametric order constraints

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Area of Science:

  • Psychology
  • Cognitive Science
  • Quantitative Psychology

Background:

  • Multinomial processing tree (MPT) models are used for analyzing latent psychological processes.
  • Previous MPT model applications were limited to analyzing main effects.
  • Interactions, crucial in understanding complex behaviors, were not well-integrated into MPT frameworks.

Purpose of the Study:

  • To extend Multinomial Processing Tree (MPT) models to incorporate interaction hypotheses.
  • To demonstrate the implementation and testing of two-way and higher-order interactions in MPT models.
  • To provide an empirical example and software guidance for applying these extended MPT models.

Main Methods:

  • Adopting the interaction concept from log-linear models for MPT applications.
  • Developing methods for implementing and testing ordinal and disordinal two-way interactions.
  • Generalizing the approach to higher-order interactions involving three or more factors.
  • Utilizing the freely available multiTree software for analysis.

Main Results:

  • Successfully extended MPT models to analyze interaction hypotheses.
  • Demonstrated the implementation of two-way and higher-order interactions.
  • Applied the method to source memory and aging data, testing the associative deficit theory.
  • Showcased the practical utility of the multiTree software for these analyses.

Conclusions:

  • The extended MPT framework allows for the investigation of complex interactions in psychological processes.
  • The developed methods and software facilitate the testing of sophisticated hypotheses, including those related to memory and aging.
  • This advancement broadens the scope of MPT modeling for psychological research.