SavedAdd your journal

Journals / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2020 / Cilt: 11 - Sayı: 1

Item Parameter Estimation for Dichotomous Items Based on Item Response Theory: Comparison of BILOG-MG, Mplus and R (ltm)

Neşe ÖZTÜRK GÜBEŞ
Şeyma UYAR
Pages
27–42
Publication Date
2022-10-12
DOI
—
🏛️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)

After lookingat the resulting bias and root mean square error (RMSE) values, it can be concluded that Mplus is an unbiasedprogram when compared to BILOG-MG and R (ltm).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is twofold.
📊
Key Findings & Data:Anotheris to provide information about the performance of Mplus, BILOG-MG and R (ltm) programs in terms of parameterestimation under the conditions which were mentioned above.
💡
Conclusion & Impact:After lookingat the resulting bias and root mean square error (RMSE) values, it can be concluded that Mplus is an unbiasedprogram when compared to BILOG-MG and R (ltm).
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The aim of this study is twofold. The first one is to investigate the effect of sample size and test length on theestimation of item parameters and their standard errors for the two parameter item response theory (IRT). Anotheris to provide information about the performance of Mplus, BILOG-MG and R (ltm) programs in terms of parameterestimation under the conditions which were mentioned above. The simulated data were used in this study. Theexaminee responses were generated by using the open-source program R. After obtaining the data sets, theparameters were estimated in BILOG-MG, Mplus and R (ltm). The accuracy of the item parameters and abilityestimates were evaluated under six conditions that differed in the numbers of items and examinees. After lookingat the resulting bias and root mean square error (RMSE) values, it can be concluded that Mplus is an unbiasedprogram when compared to BILOG-MG and R (ltm). BILOG-MG can estimate parameters and standard errorsclose to the true values, when compared to Mplus and R (ltm)

🔖

Cite This Article

GÜBEŞ, N. Ö., UYAR, Ş. (2022). Item Parameter Estimation for Dichotomous Items Based on Item Response Theory: Comparison of BILOG-MG, Mplus and R (ltm). Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Cilt: 11 - Sayı: 1, 27–42. https://ajindex.com/articles/497176

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (9)

  1. Baker, F. B. (1987). Methodology review: Item parameter estimation under the one, two and three parameter logistic models. Applied Psychological Measurement, 11(2), 111- 141. Baker, F. B. (1990). Some observations on the metric of BILOG results. Applied Psychological Measurement, 14(2), 139–150. DOI: https://doi.org/10.1177/014662169001400203
  2. Baker, F. B. (1998). An investigation of the item parameter recovery of a Gibbs sampling procedure. Applied Psychological Measurement, 22(2), 153–169. DOI: https://doi.org/10.1177/01466216980222005
  3. Bulut, O. & Zopluoğlu, C. (2013). Item parameter recovery of the graded response model using the R package ltm: A Monte Carlo simulation study. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. Crocker, L. & Algina, J. (1986). Introduction to classical and modern test theory. N.Y: CBS College Publishing Company. de Ayala, R. J. (2009). The theory and practice of item response theory. New York: The Guilford Press.
  4. Edelen, M. O., & Reeve, B. B. (2007). Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, 16(1), 5–18. DOI: http://dx.doi.org/10.1007/s11136-007-9198-0
  5. Fan, X. (1998). Item response theory and classical test theory: An empirical comparison of their item/person statistics. Educational and Psychological Measurement, 58(3), 357-381. DOI: https://doi.org/10.1177/0013164498058003001
  6. Foley, B. (2010). Improving IRT parameter estimates with small sample sizes: Evaluating the efficacy of a new data augmentation technique. Open Access Theses and Dissertations from the College of Education and Human Sciences. Paper 75 Gao, F. & Chen, L. (2005). Bayesian or non-Bayesian: A comparison study of item parameter estimation in the three-parameter logistic model. Applied Measurement in Education, 18(4), 351-380.
  7. Gübeş, N. Ö., Paek, I., & Cui, M. (2018). Örneklem büyüklüğünün ve test uzunluğunun MTK parametre kestirimine etkisi. Pegem Atıf İndeksi, 135-148.
  8. Hambleton, R. K. (1989). Principles and selected applications of item response theory. In R. Linn (Ed.), Educational Measurment (3rd.ed., pp. 147-200). New York: Macmillan. Hambleton, R. K., & Jones, R. W. (1993). Comparison of classical test theory and item response theory and their applications to test development. Educational Measurement: Issues and Practice, 12(3), 38–47. DOI: https://doi.org/10.1111/j.1745-3992.1993.tb00543.x
  9. Hambleton, R. K. &