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fcirt: An R Package for Forced Choice Models in Item Response Theory.

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  • 1Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA.

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Summary
This summary is machine-generated.

The fcirt package offers Bayesian analysis for multidimensional forced choice (MFC) assessments, improving noncognitive trait measurement. It supports the Generalized Graded Unfolding Model (GGUM) and aids in evaluating assessment quality.

Keywords:
Generalized Graded Unfolding ModelHamiltonian Monte CarloMulti-Unidimensional Pairwise Preference modelStanmultidimensional forced choice

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Computational statistics

Background:

  • Multidimensional forced choice (MFC) formats offer advantages over Likert-type scales for assessing noncognitive traits by mitigating response biases.
  • The increasing adoption of MFC formats necessitates robust analytical tools for accurate parameter estimation and model evaluation.

Purpose of the Study:

  • To introduce the `fcirt` R package, a novel tool designed to facilitate the analysis of multidimensional forced choice (MFC) data.
  • To provide researchers with a comprehensive package for estimating parameters of the Multi-Unidimensional Pairwise Preference (MUPP) model, which is based on the Generalized Graded Unfolding Model (GGUM).

Main Methods:

  • The `fcirt` package employs Bayesian estimation methods, specifically utilizing the `rstan` package for Hamiltonian Monte Carlo (HMC) sampling.
  • It implements the Generalized Graded Unfolding Model (GGUM)-based Multi-Unidimensional Pairwise Preference (MUPP) model for parameter estimation.
  • The package includes functions for calculating item and test information functions and performing Bayesian diagnostic plotting for model assessment.

Main Results:

  • The `fcirt` package enables the estimation of MUPP model parameters within a Bayesian framework.
  • It provides tools for evaluating the psychometric properties of MFC assessments through information functions.
  • Bayesian diagnostic plots are available for assessing model convergence and overall fit.

Conclusions:

  • The `fcirt` package offers a valuable resource for researchers utilizing MFC formats, providing advanced Bayesian analytical capabilities.
  • It supports the rigorous evaluation of MFC assessment quality, contributing to more reliable measurement of noncognitive traits.
  • The package facilitates improved model evaluation and convergence assessment through integrated diagnostic tools.