According tothe results, the average SEM estimates of the Bootstrap method are less than the average SEM estimatesof JML in terms of the ability parameter.
📌 3 Maddede Bu Makale
🎯
Amaç & Hipotez:The ability parameter of persons/examinees estimates can be obtained using the Joint MaximumLikelihood (JML) estimation in Item Response Theory (IRT).
📊
Temel Bulgular & Veri:This research evaluates the performances ofJML and Bootstrap estimates of the ability parameter in terms of Standard Error Measurement (SEM) inthe 2-Parameter Logistic (2-PL) model conducting a detailed Monte Carlo simulation study.
💡
Sonuç & Katkı:According tothe results, the average SEM estimates of the Bootstrap method are less than the average SEM estimatesof JML in terms of the ability parameter.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri
Abstract
The ability parameter of persons/examinees estimates can be obtained using the Joint MaximumLikelihood (JML) estimation in Item Response Theory (IRT). However, JML estimates can be biased insome cases. Although the Bootstrap method has been considered for JML, existing studies remain farfrom satisfactory concerning the ability parameter estimation. This research evaluates the performances ofJML and Bootstrap estimates of the ability parameter in terms of Standard Error Measurement (SEM) inthe 2-Parameter Logistic (2-PL) model conducting a detailed Monte Carlo simulation study. According tothe results, the average SEM estimates of the Bootstrap method are less than the average SEM estimatesof JML in terms of the ability parameter.
🔖
Bu Makaleye Atıf Yap
Nazman, E., Olmuş, H. (2022). Inferences from Bootstrap Method for Ability Parametersin 2-Parameter Logistic Model. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 16 - Sayı: 3, 333–338. https://ajindex.com/articles/472159
Rasch, G. Probabilistic Models for Some Intelligence and Attainment Tests; Chicago: MESA; 1960
Hambleton, RK, Jones, RW. 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
Baker, FB. The basis of item response theory. ERIC. 2001
Birnbaum, A. Some latent trait models and their use in inferring an examinee’s ability, In Lord, FM, Novick, MR (Eds.), Statistical Theories of Mental Test Scores; 1968
Paolino, JP. Penalized joint maximum likelihood estimation applied to two parameters logistic item. Columbia University Graduate School of Arts and Sciences; 2013
McCulloch, CE, Searle, SR. Generalized, linear, and mixed models. John Wiley & Sons, New York; 2001
Harris, D. Comparison of 1-,2- and 3-parameter IRT models, instructional topics in educational measurement, An NCME Instructional Module on; 1989
Liou, M., Yu, L. 1991. Assesing statistical accuracy in ability estimation: bootstrap approach. Psychometrika; 56(1): 55-67
Atanasov, D. 2009. Estimation of IRT parameters over a small sample: Bootstrapping of the item responses. Pliska Studia Mathematica Bulgaria; 19: 58-68
Heene, M, Draxler, C, Ziegler, M, Bühner, M. 2011. Performance of the bootstrap Rasch model test under violations of nonintersecting item response functions. Psychological Test and Assessment Modeling; 53:283–294
Wolfe, EW, McGill, MT. Comparison of asymptotic and bootstrap item fit indices in identifying misfit to the Rasch model. National Conference on Measurement in Education New Orleans; 2011
Patton, JM, Cheng, Y, Yuan, KH, Diao, Q. 2014. Bootstrap standard errors for maximum likelihood ability estimates when item parameters are unknown. Educational and Psychological Measurement; 74(4): 697-712
Olmuş, H., Nazman, E. 2017. An evaluation of the two parameter (2-PL) IRT models through a simulation study. Gazi University Journal of Science; 30(1): 235-249
Liu, Y, Yang, JS. 2018. Bootstrap-calibrated interval estimates for latent variable scores in item response theory. Psychometrika; 83(2): 333-354
Liu, Y., Hu, G., Cao, L.Wang, X., Chen, M.H. 2019. A comparison of Monte Carlo methods for computing marginal likelihoods of item response theory models. Journal of the Korean Statistical Society; 48:503-512
Chen, S., Haziza, D., Leger, C., Mashreghi, Z. 2019. Pseudopopulation bootstrap methods for imputed survey d