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Related Experiment Video

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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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A modular approach for item response theory modeling with the R package flirt.

Minjeong Jeon1, Frank Rijmen2

  • 1Department of Psychology, Faculty of Quantitative Psychology, The Ohio State University, 228 Lazenby Hall 1827 Neil Avenue, Columbus, OH, 43210, USA. jeon.117@osu.edu.

Behavior Research Methods
|July 16, 2015
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Summary

The new R package flirt offers flexible item response theory (IRT) modeling for psychological and educational assessments. Its modular design allows researchers to easily customize complex IRT models for diverse data analysis needs.

Keywords:
Bifactor modelsDIFExplanatory modelsItem response theoryModular approachMultidimensional modelsR software

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Data Analysis

Background:

  • Item Response Theory (IRT) is crucial for psychological, educational, and behavioral assessments.
  • Existing IRT software may lack flexibility in model specification and customization.
  • A need exists for integrated tools that combine advanced statistical frameworks for IRT.

Purpose of the Study:

  • Introduce the R package flirt for flexible IRT modeling.
  • Demonstrate the integration of generalized linear/nonlinear mixed models with graphical model theory.
  • Highlight the modular approach for user-defined IRT model construction.

Main Methods:

  • Developed the R package flirt leveraging a generalized linear and nonlinear mixed modeling framework.
  • Integrated graphical model theory for efficient maximum likelihood estimation.
  • Implemented a modular system allowing selection of parametric forms, dimensions, covariates, and link functions.

Main Results:

  • flirt provides a flexible and modular framework for specifying customized IRT models.
  • The package facilitates efficient maximum likelihood estimation through its graphical model integration.
  • Examples demonstrate practical application of flirt for various assessment data.

Conclusions:

  • flirt enhances IRT modeling capabilities in R, offering unprecedented flexibility.
  • The modular design empowers researchers to build bespoke IRT models tailored to specific research questions.
  • This package is a valuable tool for advancing psychometric and educational assessment research.