Journals / International Journal of Assessment Tools in Education / 2018 / Cilt: 5 - Sayı: 1

Investigating the Impact of Missing Data Handling Methods on the Detection of Differential Item Functioning

Pages
2741–15
Publication Date
2022-10-19
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)

The DIF detection methods based on Classical Test Theory are more consistent within themselves compared to DIF detection method based on Item Response Theory, whereas the used missing data handling methods differentiate the DIF detected items and this difference reaches a significant level for Mantel Haenszel method

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it is aimed to investigate the impact of different missing data handling methods on the detection of Differential Item Functioning methods (Mantel Haenszel and Standardization methods based on Classical Test Theory and Likelihood Ratio Test method based on Item Response Theory).
📊
Key Findings & Data:In this regard, on the data acquired from 1046 candidates who entered to Foreign National Student Exam (FNSE) held in year 2016 by Mersin University (MEU) and answered Basic Skills subtest, using different missing data handling methods, differential item functioning analyses with Mantel Haenszel, Standardization and Likelihood Ratio Test methods are performed.
💡
Conclusion & Impact:The DIF detection methods based on Classical Test Theory are more consistent within themselves compared to DIF detection method based on Item Response Theory, whereas the used missing data handling methods differentiate the DIF detected items and this difference reaches a significant level for Mantel Haenszel method
🔬Design:Research Article
📍Scope:2016 Verileri
⚙️Variables:Differential Item Functioning DIF · Test and Item Bias · Missing Values

Abstract

In this study, it is aimed to investigate the impact of different missing data handling methods on the detection of Differential Item Functioning methods (Mantel Haenszel and Standardization methods based on Classical Test Theory and Likelihood Ratio Test method based on Item Response Theory). In this regard, on the data acquired from 1046 candidates who entered to Foreign National Student Exam (FNSE) held in year 2016 by Mersin University (MEU) and answered Basic Skills subtest, using different missing data handling methods, differential item functioning analyses with Mantel Haenszel, Standardization and Likelihood Ratio Test methods are performed. Basic Skills test consists of 80 multiple choice items. The items are all binary scored (1-0) items. Among the participants 523 are female and 523 are male. The findings showed that the number of items flagged as DIF has changed with the used missing data handling methods. The DIF detection methods based on Classical Test Theory are more consistent within themselves compared to DIF detection method based on Item Response Theory, whereas the used missing data handling methods differentiate the DIF detected items and this difference reaches a significant level for Mantel Haenszel method

🔖

Cite This Article

SELVİ, H., ALICI, D. Ö. (2022). Investigating the Impact of Missing Data Handling Methods on the Detection of Differential Item Functioning. International Journal of Assessment Tools in Education, Cilt: 5 - Sayı: 1, 2741–15. https://ajindex.com/articles/540209

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. Abedlazeez, N. (2010). Exploring DIF: Comparison of CTT and IRT methods. International Journal of Sustainable Development, 7(1), 11-46.
  2. Allison, P. D. (2002). Missing data. California: Sage Publication Inc. Alpar, R. (2011). Uygulamalı çok değişkenli istatistiksel yöntemler. Ankara:
  3. Detay Yayıncılık. Angoff, W.H. (1993). Perspectives on differential item functioning methodology. In Holland & Wainer (Ed.), Differential Item Functioning. New Jersey: Lawrence Erlbaum Associates Publishers.
  4. Banks, K., & Walker, C. (2006). Performance of SIBTEST when focal group examinees have missing data. San Francisco: National Council of Measurement in Education. Banks, K. (2015). An introduction to missing data in the context of differential item functioning. Practical Assessment, Research & Evaluation. 20(12). Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25, 464–469.
  5. Bernhard, J., Celia, D.F., &Coates, A.S. (1998). Missing quality of life data in cancer clinical trials: Serious problems and challenges. Statistics in Medicine, 17, 517-532.
  6. Camili, G., & Shepard, L.A. (1994). Methods for identifying biased test items. London: Sage Publication. Demir, E., & Parlak, B. (2012). Türkiye’de eğitim araştırmalarında kayıp veri sorunu. Journal of Measurement and Evaluation in Education and Psychology 3(1), 230-241.
  7. Demir, E. (2013). Kayıp verilerin varlığında çoktan seçmeli testlerde madde ve test parametrelerinin kestirilmesi: SBS örneği [Item and test parameters estimations for multiple choice tests in the presence of missing data: The case of SBS]. Journal of Educational Sciences Research, 3(2), 47–68.
  8. Dişçi, R. (2012). Temel ve klinik biyoistatistik. İstanbul:
  9. Tıp Kitapevi. Doğan, N., & Öğretmen, T. (2008). Değişen Madde Fonksiyonunu belirlemede Mantel–Haenszel, Ki-Kare ve Lojistik Regresyon tekniklerinin karşılaştırılması. Education and Science, 33(148). Embretson, S.E., & Reise, S.P. (2000). Item response theory for psychologists. London: Lawrence Erlbaum Associates. Emenogu, B. C., Falenchuck, O., & Childs, R. A. (2010). The effect of missing data treatment on Mantel-Haenszel DIF detection. The Alberta Journal of Educational Research, 56(4), 459-469.
  10. Falenchuk, O., & Herbert, M. (2009). Investigation of differential non-response as a factor affecting the results of Mantel-Haenszel DIF detection California: American Educational Research Association. Finch, W.H. (2011). The impact of missing data on t