Journals / International Journal of Assessment Tools in Education / 2026 / Volume: 12 Issue: 2

The effect of polytomous item ratio on ability estimation in multistage tests

Hasibe Yahsi Sari(NECMETTİN ERBAKAN ÜNİVERSİTESİ)0000-0002-0451-6034
Hülya Kelecioğlu(HACETTEPE ÜNİVERSİTESİ)0000-0002-0741-9934
Pages
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Publication Date
2025-06-01
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AI Summary & Key Takeaways

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

In 1-2 and 1-4 panel designs, it does not matter which routing method is used.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of the study is to examine the effect of polytomous item ratio on ability estimation in different conditions in multistage tests (MST) using mixed tests.
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Key Findings & Data:In the PISA 2018 application, the ability parameters of the individuals and the item pool were created by using the item parameters estimated from the dichotomous and polytomous items obtained in the field of reading skills.
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Conclusion & Impact:In 1-2 and 1-4 panel designs, it does not matter which routing method is used.
🔬Design:Lab & Field Experiment
📍Scope:2018 Verileri
⚙️Variables:Multistage testing · Polytomous item · Mixed test

Abstract

The aim of the study is to examine the effect of polytomous item ratio on ability estimation in different conditions in multistage tests (MST) using mixed tests. The study is simulation-based research. In the PISA 2018 application, the ability parameters of the individuals and the item pool were created by using the item parameters estimated from the dichotomous and polytomous items obtained in the field of reading skills. MST conditions; panel design, test lengths, routing methods, and polytomous item ratio. Simulation data, MST pattern and analysis were obtained with the help of WinGen, CPLEX, and the “mstR” package in the R Studio program. A total of 108 conditions and 100 replications were examined in the study. As a result of the simulations, RMSE, mean absolute bias and correlation values were calculated. As a result of the research, it is seen that when the ratio of polytomous items in the tests increases from 10% to 50%, the mean absolute bias and RMSE values decrease while the correlation values increase. As the test length increases, RMSE and mean absolute bias values decrease while correlation values increase. In terms of routing methods, MFI performed better than the NC routing method. In general, three-stage panel designs gave significantly better results than two-stage panel designs. In 1-2 and 1-4 panel designs, it does not matter which routing method is used.

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Cite This Article

Sari, H. Y., Kelecioğlu, H. (2025). The effect of polytomous item ratio on ability estimation in multistage tests. International Journal of Assessment Tools in Education, Volume: 12 Issue: 2. https://doi.org/10.21449/ijate.1474855

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References (37)

  1. Armstrong, R.D. (2002). Routing rules for multi-form structures (LSAC Computerized Testing Report No. 02 08). Law School Admission Council. https://searchworks.stanford.edu/view/6794803
  2. Bock, R.D., & Mislevy, R.J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6(4), 431 444. https://doi.org/10.1177/014662168200600405
  3. Boztunç-Öztürk, N. (2019). How the length and characteristics of routing module affect ability estimation in CA-MST? Universal Journal of Educational Research, 7(1), 164-170. https://doi.org/10.13189/ujer.2019.070121
  4. Cıkrıkcı, N., Yalçın, S., Kalender, İ., Gül, E., et al. (2020). Development of a computerized adaptive version of the Turkish Driving Licence Exam. International Journal of Assessment Tools in Education, 7(4), 570-587. https://doi.org/10.21449/ijate.716177
  5. Dallas, A. (2014). The effects of routing and scoring within a computer adaptive multi-stage framework [Doctoral dissertation, University of North Carolina]. University of North Carolina Libraries. https://libres.uncg.edu/ir/uncg/f/Dallas_uncg_0154D_11394.pdf
  6. Doğruöz, E. (2018). Bireyselleştirilmiş çok aşamalı testlerin test birleştirme yöntemlerine göre incelenmesi [Doktora tezi, Hacettepe Üniversitesi]. Hacettepe Üniversitesi Açık Erişim Sistemi. http://hdl.handle.net/11655/5298
  7. Erdem-Kara, B. (2019). Değişen madde fonksiyonu gösteren madde oranının bireyselleştirilmiş bilgisayarlı ve çok aşamalı testler üzerindeki etkisi [Doktora tezi, Hacettepe Üniversitesi]. Hacettepe Üniversitesi Açık Erişim Sistemi. http://hdl.handle.net/11655/11968
  8. Han, K.T. (2007). WinGen: Windows Software That Generates Item Response Theory Parameters and Item Responses. Applied Psychological Measurement, 31(5), 457-459. https://doi.org/10.1177/0146621607299271
  9. Hendrickson, A. (2007). An NCME instructional module on multistage testing. Educational Measurement: Issues and Practice, 26(2), 44-52. https://doi.org/10.1111/j.1745-3992.2007.00093.x
  10. Kim, J., Chung, H., & Dodd, B.G. (2010, April). Comparing routing methods in the multistage test based on the partial credit model [Conference presentation] Annual meeting of the American Educational Research Association, Denver, CO.
  11. Kim, J., Chung, H., Dodd, B.G., & Park, R. (2012). Panel design variations in the multistage test using the mixed-format tests. Educational and Psychological Measurement, 72(4), 574-588. https://doi.org/10.1177/001316441142897
  12. Kim, J., Chung, H., Park, R., & Dodd, B.G. (2013). A comparison of panel designs with routing methods in the multistage test with the partial credit model. Behavior Research Methods, 45(4), 1087-1098. https://doi.org/10.3758/s13428-013-0316-3
  13. Kim, J., & Dodd, B.G. (2014). Mixed-format multistage tests: Issues and methods. In D. Yan, A.A. von Davier, & C. Lewis (Eds.), Computerized multistage testing: Theory and applications (pp. 55–67). CRC Press.
  14. Kim, H., & Plake, B.S. (1993). Monte carlo simulation comparison of two-stage testing and computerized adaptive testing. Annual Meeting of the National Council on Measurement in Education, Atlanta, GA. https://eric.ed.gov/?id=ED357041
  15. Luo, X., & Kim, D. (2018). A top‐down approach to designing the computerized adaptive multistage test. Journal of Educational Measurement, 55(2), 243 263. https://doi.org/10.1111/jedm.12174
  16. Luecht, R.M., & Nungester, R.J. (1998). Some practical examples of computer‐adaptive sequential testing. Journal of Educational Measurement, 35(3), 229 249. http://www.jstor.org/stable/1435202
  17. Magis, D., Yan, D., & von Davier A.A. (2018). Package ‘mstR’: Procedures to generate patterns under multistage testing. https://cran.r-project.org/web/packages/mstR/mstR.pdf
  18. OECD (2002). Proposed standard practice for surveys on research and experimental development: Frascati Manual 2002. OECD Publishing. https://doi.org/10.1787/9789264199040-en
  19. Park, R. (2015). Investigating the impact of a mixed-format item pool on optimal test designs for multistage testing [Doctoral dissertation, The University of Texas]. University of Texas Libraries. http://hdl.handle.net/2152/31011
  20. Park, R., Kim, J., Chung, H., & Dodd, B.G. (2014). Enhancing pool utilization in constructing the multistage test using mixed-format tests. Applied Psychological Measurement, 38(4), 268-280. https://doi.org/10.1177/0146621613515
  21. Park, R., Kim, J., Chung, H., & Dodd, B. G. (2017). The development of mst test information for the prediction of test performances. Educational and Psychological Measurement, 77(4), 570–586. https://doi.org/10.1177/0013164416662960
  22. Patsula, L.N. (1999). A comparison of computerized adaptive testing and multistage testing. [Doctoral dissertation, University of Massachusetts]. University of Massachusetts Libraries. https://doi.org/10.7275/10994910
  23. R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/
  24. Rosa, K., Swygert, K.A., Nelson, L., & Thissen, D. (2001). Item response theory applied to combinations of multiple-choice and constructed-response items—scale scores for patterns of summed scores. In D. Thissen & H. Wainer (Eds.), Test scoring (1st ed., pp. 253–292). Routledge. https://doi.org/10.4324/9781410604729
  25. Sahin, M.G., & Ozturk, N.B. (2019). Analyzing the maximum likelihood score estimation method with fences in ca-MST. International Journal of Assessment Tools in Education, 6(4), 555-567. https://dx.doi.org/10.21449/ijate.634091
  26. Sari, H.I., & Raborn, A. (2018). What information works best?: A Comparison of Routing Methods. Applied Psychological Measurement, 42(6), 499 515. https://doi.org/10.1177/0146621617752990
  27. Senel, S., & Kutlu, O. (2018). Computerized adaptive testing design for students with visual impairment. Education and Science Journal,43(194), 261 284. https://doi.org/10.15390/EB.2018.7515
  28. Wang, K. (2017). A fair comparison of the performance of computerized adaptive testing and multistage adaptive testing [Doctoral dissertation, University of Michigan State]. University of Michigan State Libraries. https://doi.org/doi:10.25335/ypy5-6g68
  29. Weiss, D.J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 6(4), 473 492. https://doi.org/10.1177/014662168200600408
  30. Weiss, D.J. (1983). Latent trait theory and adaptive testing. In Weiss D.J. (Ed.), New horizons in testing (pp. 5-7). Academic Press.
  31. Weiss, D.J., & Kingsbury, G.G. (1984). Application of computer adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361 375. https://doi.org/10.1111/j.1745-3984.1984.tb01040.x
  32. Weissman, A., Belov, D.I., & Armstrong, R.D. (2007). Information-based versus number-correct routing in multistage classification tests, LSAC Computerized Testing Report, 7(5). Law School Admission Council.
  33. Yahsi Sari, H., & Kelecioglu, H. (2023). Ability estimation with polytomous items in computerized multistage tests. Journal of Measurement and Evaluation in Education and Psychology, 14(3), 171-184. https://doi.org/10.21031/epod.1056079
  34. Yamamoto, K., Shin, H., & Khorramdel, L. (2019). Introduction of multistage adaptive testing design in PISA 2018. OECD Education Working Papers, No. 209, OECD Publishing. https://doi.org/10.1787/b9435d4b-en
  35. Zenisky, A.L. (2004). Evaluating the effects of several multi-stage testing design variables on selected psychometric outcomes for certification and licensure assessment [Doctoral dissertation, University of Massachusetts]. University of Massachusetts Libraries. https://doi.org/10.7275/18739572
  36. Zenisky, A.L., Hambleton, R.K., & Luecht, R.M. (2010). Multistage testing: Issues, designs, and research. In W.J. van der Linden & C.A.W. Glas (Eds.), Elements of adaptive testing (pp. 355-372). Springer.
  37. Zheng, Y., Nozawa, Y., Gao, X., & Chang, H.H. (2012). Multistage adaptive testing for a large-scale classification test: The designs, automated heuristic assembly, and comparison with other testing modes. ACT Research Reports 2012 6. https://www.act.org/content/dam/act/unsecured/documents/ACT_RR2012-6.pdf