Journals / Kuram ve Uygulamada Eğitim Bilimleri / 2011 / Cilt: 11 - Sayı: 1

Sample size in differential item functioning: An application of hierarchical linear modeling .

Maddenin farklı fonksiyonlaşmasında örneklem büyüklüğü: Genelleştirilmiş aşamalı doğrusal modelleme uygulaması

Pages
279–288
DOI
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AI Summary & Key Takeaways

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

It has been concluded that varieties in sample sizes have a great effect on DIF detection in test items.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The purpose of this study is to examine the number of DIF items detected by HGLM at different sample sizes.
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Key Findings & Data:The population of the study is 798307 students who had taken the 2006 OKS Examination. 10727 students of 798307 are chosen by random sampling method as the sample of the study.
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Conclusion & Impact:It has been concluded that varieties in sample sizes have a great effect on DIF detection in test items.
🔬Design:Research Article
📍Scope:2006 Verileri
⚙️Variables:Domain Parameters

Abstract

The purpose of this study is to examine the number of DIF items detected by HGLM at different sample sizes. Eight different sized data files have been composed. The population of the study is 798307 students who had taken the 2006 OKS Examination. 10727 students of 798307 are chosen by random sampling method as the sample of the study. Turkish, science, and social studies subtests, all composed of 25 items and applied in the OKS-2006, are used as data gathering instruments in this study. It has been concluded that varieties in sample sizes have a great effect on DIF detection in test items.

Özet

Bu çalışmanın amacı, farklı örneklem büyüklüklerinde Maddenin Farklı Fonksiyonlaşmasında (MFF'nin belirlenmesinde) kullanılan yöntemlerden Genelleştirilmiş Aşamalı Doğrusal Modelleme (GADM) ile belirlenen MFF'li madde sayılarının incelenmesidir. Araştırmanın amacı farklı örneklem büyüklüklerinde GADM ile elde edilen MFF'li madde sayılarındaki değişkenliği gözlemek olduğundan sekiz farklı büyüklükte veri dosyaları oluşturulmuştur. Araştırmanın evrenini 2006 OKS' ye giren 798307 öğrenci; örneklemini de bu evrenden tesadüfi örnekleme yöntemi ile seçilen 10727 öğrenci temsil etmektedir. OKS-2006 yılında uygulanmış olan 25'er maddelik Türkçe, Fen Bilgisi ve Sosyal Bilgiler alt testleri veri toplama aracı olarak ele alınmıştır. Bu çalışmada, örneklem büyüklüklerindeki değişkenliğin test maddelerinde MFF belirlemede önemli bir etkisinin olduğu ortaya çıkmıştır.

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Cite This Article

ACAR, T. (2011). Sample size in differential item functioning: An application of hierarchical linear modeling .. Kuram ve Uygulamada Eğitim Bilimleri, Cilt: 11 - Sayı: 1, 279–288. https://ajindex.com/articles/346354

References (4)

  1. Allalouf, A. (2003). Revising translated dif erential item functioning items as a tool for improving cross-lingual assessment. Applied Measurement In Education, 16 (1), 55-73
  2. Binici, S. (2007). Random-Ef ect dif erential item functioning via hierarchical generalized linear model and generalized linear latent mixed model: A comparison of estimation methods. Unpublished doctoral dissertation, The Florida State University, Florida. Chaimongkol, S., Huf erve, F. W., & Kamata, A. (2007). An explanatory dif erential item functioning (DIF) model by the WinBUG 1.4. Songklanakarin Journal of Science Technology, 29 (2). Retrieved February 10, 2008 from http://www. sjst.psu.ac.th/journal/29_2_pdf/19item%20response%20 theory_449-458.pdf Duncan, S. C. (2006). Improving the prediction of dif erential item functioning: A comparison of the use of an effect size for logistic regression dıf and mantel-haenszel dıf methods. Unpublished doctoral dissertation, Sam Houston State University, Texas. Ellis, B. B., & Raju, N. S. (2003). Test and item bias: What they are, what they aren’t, and how to the detect them. Retrieved February 10, 2008 from http://www.eric.ed.gov/ERICDocs/ data/ericdocs2sql/contentşstorageş01/0000019b/80/1b/57/ a3.pdf Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates. French, A. W., & Miller, T. R. (1996). Logistic regression and its use in detecting differential item functioning in polytomous items. Journal of Educational Measurement, 33 (3), 315-332.
  3. Gierl, M., Khaliq, S. N., & Boughton, K. (1999, June). Gender differential item functioning in mathematics and science: Prevalence and policy implications. Paper presented at the Improving Large-Scale Assessment in Education Symposium at the Annual Meeting of the Canadian Society for the Study of Education, Canada. Retrieved February 25, 2008 from http://www.education.ualberta.ca/educ/psych/crame/files/ dif_csse99.pdf. Gondal, M. B.(2001). Differential item functioning analysis of 4h graders’ science and urdu (national language) achievement test items in Pakistan. Unpublished doctoral dissertation, Middle East Technical University, Ankara.
  4. Greer, T. G. (2004). Detection of differential item functioning (DIF) on the SATV: A comparison of four methods: Mantel- Haenszel, logistic regression, simultaneous item bias and likelihood ratio test. Unpublished doctoral dissertation, University of Houston, Texas. Hamzeh, D., & Johanson, G. (2003). An