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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Parameter identification in multinomial processing tree models.

Verena D Schmittmann1, Conor V Dolan, Maartje E J Raijmakers

  • 1Department of Psychological Methods, University of Amsterdam, Roetersstraat 15, Room A.514, 1018WB Amsterdam, The Netherlands. v.d.schmittmann@uva.nl

Behavior Research Methods
|September 1, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a method for local identification in multinomial processing tree models, crucial for analyzing psychological data. This approach simplifies complex statistical model inference and interpretation in memory research.

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Area of Science:

  • Psychological research
  • Statistical modeling
  • Cognitive science

Background:

  • Multinomial processing tree (MPT) models are widely used for categorical data analysis in psychology.
  • Parameter identification is essential for valid statistical inference, model selection, and result interpretation.
  • Global identification checks can be computationally intractable for complex MPT models.

Purpose of the Study:

  • To present a method for establishing local identification in MPT models.
  • To provide a practical approach for complex statistical models where global identification is challenging.
  • To demonstrate the utility of local identification in memory research, specifically the source-monitoring paradigm.

Main Methods:

  • Utilizing formal methods for local identification, drawing from Catchpole and Morgan (1997) and Bekker, Merckens, and Wansbeek (1994).
  • Applying these methods to multinomial processing tree models.
  • Illustrating the approach with a specific application in memory research.

Main Results:

  • A feasible method for determining local identification in MPT models has been demonstrated.
  • The proposed approach offers a tractable alternative to computationally intensive global identification methods.
  • The application to source-monitoring models provides empirical validation of the method's utility.

Conclusions:

  • Local identification provides a valuable tool for ensuring the identifiability of parameters in complex MPT models.
  • This method enhances the reliability of statistical inference and interpretation in psychological research.
  • The approach is particularly useful for applied researchers dealing with intricate models in areas like memory research.