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Bayesian Thurstonian models for ranking data using JAGS.

Timothy R Johnson1, Kristine M Kuhn

  • 1Department of Statistical Science, University of Idaho, 875 Perimeter Drive Stop 1104, Moscow, ID, 83844-1104, USA. trjohns@uidaho.edu

Behavior Research Methods
|March 30, 2013
PubMed
Summary
This summary is machine-generated.

Bayesian Thurstonian models for ranking data are now easily implemented using JAGS software. This simplifies complex statistical analyses, making Thurstonian models more accessible for researchers studying ranking data.

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Area of Science:

  • Statistics
  • Psychometrics

Background:

  • Thurstonian models link observed rankings to underlying continuous variables.
  • These models are appealing due to their connection to linear regression.
  • Practical application of Thurstonian models is limited by complex inference methods.

Purpose of the Study:

  • To demonstrate the easy implementation of Bayesian Thurstonian models for ranking data.
  • To provide accessible JAGS software tools for Thurstonian model analysis.
  • To encourage wider adoption of Thurstonian models in research.

Main Methods:

  • Utilized the JAGS (Just Another Gibbs Sampler) software package.
  • Developed and provided JAGS model files for Thurstonian ranking models.
  • Applied the models to illustrate their practical implementation in data analysis.

Main Results:

  • Bayesian Thurstonian models are shown to be easily implementable with JAGS.
  • The provided JAGS model files facilitate general use and analysis.
  • Implementation and use of these models are demonstrated through examples.

Conclusions:

  • JAGS software significantly lowers the technical barrier for using Thurstonian models.
  • This work promotes broader application of Thurstonian models in statistical and psychological research.
  • Researchers can now more readily analyze ranking data using these advanced statistical techniques.