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Getting started with the graded response model: An introduction and tutorial in R.

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Summary
This summary is machine-generated.

This tutorial explains the graded response model (GRM), a tool within item response theory (IRT), for assessing measurement precision in psychological scales. It guides researchers on performing GRM analysis using R packages for better item and person evaluation.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Psychological Statistics

Background:

  • Item response theory (IRT) provides advanced methods for analyzing measurement properties.
  • Psychological scales often use polytomous (Likert-style) items, requiring specialized analysis models.
  • Accurate measurement of item and person characteristics is crucial for research validity.

Purpose of the Study:

  • To introduce the graded response model (GRM) as a tool for measurement precision.
  • To guide applied researchers in conducting unidimensional GRM analyses in the R environment.
  • To demonstrate the application of GRM using real-world data from the right-wing authoritarianism (RWA) scale.

Main Methods:

  • Utilizing R packages such as psych, mirt, and ggmirt for GRM analysis.
  • Outlining theoretical underpinnings of the graded response model.
  • Detailing steps for data preparation, assumption testing, and model fitting.

Main Results:

  • Demonstration of GRM analysis on the RWA scale data.
  • Guidance on plotting item parameters and interpreting results.
  • Illustrating how GRM assesses psychometric properties of polytomous items.

Conclusions:

  • The graded response model (GRM) is a valuable tool for analyzing polytomous items in psychological research.
  • Applied researchers can effectively use R packages for conducting GRM analyses.
  • GRM analysis enhances understanding of item and person properties, improving measurement precision.