SavedAdd your journal

Journals / Anadolu Üniversitesi Eğitim Fakültesi Dergisi / 2026 / Volume: 6 Issue: 1

irtDemo R Package: Pedagogical Interactive Web Applications for Estimation, Scoring, and Multi Dimensionality in Item Response Theory

Metin Bulus(ADIYAMAN UNIVERSITY)0000-0003-4348-6322
Wes Bonifay(UNIVERSITY OF MISSOURI - COLUMBIA)0000-0003-3853-7607
Pages
—
Publication Date
2022-02-10
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We hope that these applications give a head-start to emerging practitioners and researchers interested in advanced measurement topics.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Comprehension of foundational but fairly complex statistical theories may require assistive interactive tools to understand underlying equations and theory.
📊
Key Findings & Data:We provide a collection of interactive web applications to demonstrate or explore some of the fundamental yet complex item response theory concepts such as estimation, scoring and multidimensionality.
💡
Conclusion & Impact:We hope that these applications give a head-start to emerging practitioners and researchers interested in advanced measurement topics.
🔬Design:Technical Brief
📍Scope:Comprehensive Sample
⚙️Variables:item response theory · multidimensional item response theory · maximum likelihood estimation

Abstract

Comprehension of foundational but fairly complex statistical theories may require assistive interactive tools to understand underlying equations and theory. We provide a collection of interactive web applications to demonstrate or explore some of the fundamental yet complex item response theory concepts such as estimation, scoring and multidimensionality. Interactive web applications were developed via shiny R package. Users can access to these applications through irtDemo R package or links provided in this article. We hope that these applications give a head-start to emerging practitioners and researchers interested in advanced measurement topics.

Özet

Temel düzeyde ancak oldukça karmaşık istatistiksel teorilerin anlaşılması, temellerinde yatan denklemleri ve teoriyi anlamak için yardımcı etkileşimli teknolojik araçlar gerektirebilir. Bu çalışmada, tahmin, puanlama ve çok boyutluluk gibi bazı temel ancak karmaşık madde tepki kuramı kavramlarını göstermek veya keşfetmek için interaktif web uygulamaları koleksiyonu sunulmuştur. İnteraktif web uygulamaları shiny R paketi kullanılarak oluşturulmuştur. Kullanıcılar bu uygulamalara hem irtDemo R paketinden hem de bu çalışmada verilen linkleri kullanarak erişebilirler. Bu uygulamaların, gelişmiş ölçme konularıyla ilgilenen uygulayıcılara ve araştırmacılara başlangıç için bir avantaj sağlayacağını düşünülmektedir.

🔖

Cite This Article

Bulus, M. (2022). irtDemo R Package: Pedagogical Interactive Web Applications for Estimation, Scoring, and Multi Dimensionality in Item Response Theory. Anadolu Üniversitesi Eğitim Fakültesi Dergisi, Volume: 6 Issue: 1. https://doi.org/10.34056/aujef.913781

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord and M. R.
  2. Novick (Eds.), Statistical theories of mental test scores (pp. 397-472), Reading, MA: Addison-Wesley.
  3. Bock, R. D., & Mislevy, R. J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6(4), 431-444.
  4. Bonifay, W. (2019). Multidimensional item response theory. SAGE Publications.
  5. Braun, J. W., White, B. J., & Craig, G. (2014). R tricks for kids. Teaching Statistics, 36, 7–12.
  6. Bulus, M., & Bonifay, W. (2018). irtDemo: Item response theory demo collection. R package version 0.1.4. Available at https://CRAN.R-project.org/package=irtDemo
  7. Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2017). shiny: Web Application Framework for R. R package version 1.0.3. Available at https://CRAN.R-project.org/package=shiny
  8. De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford Publications. Garfield, J., and Ben-Zvi, D. (2007). How students learn statistics revisited: a current review of research on teaching and learning statistics. International Statistical Review, 75, 372–396.
  9. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.
  10. Reckase, M. D. (2009). Multidimensional Item Response Theory Models. In Multidimensional Item Response Theory (pp. 79-112). New York, NY: Springer.