Research Article
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Year 2021, Volume 12, Issue 1, 15 - 27, 31.03.2021
https://doi.org/10.21031/epod.787865

Abstract

References

  • Bao, Y., Shen, Y., Wang, S., & Bradshaw, L. (2021). Flexible computerized adaptive tests to detect misconceptions and estimate ability simultaneously. Applied Psychological Measurement, 45(1), 3-21. doi: 10.1177/0146621620965730
  • Dooley, K. (2002). Simulation research methods. In J. Baum (Ed.), Companion to organizations (pp. 829-848). London: Blackwell.
  • Eggen, T. J. H. M. (1999). Item selection in adaptive testing with the sequential probability ratio test. Applied Psychological Measurement, 23(3), 249-261. doi: 10.1177/01466219922031365
  • Eggen, T. J. H. M., & Straetmans, G. J. J. M. (2000). Computerized adaptive testing for classifying examinees into three categories. Educational and Psychological Measurement, 60(5), 713-734. doi: 10.1177/00131640021970862
  • Fan, Z., Wang, C., Chang, H., & Douglas, J. (2012). Utilizing response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37(5), 655-670. doi: 10.3102/1076998611422912
  • Finkelman, M. (2008). On using stochastic curtailment to shorten the SPRT in sequential mastery testing. Journal of Educational and Behavioral Statistics, 33(4), 442-463. doi: 10.3102/1076998607308573
  • Gündeğer, C., & Doğan, N. (2018a). A comparison of computerized adaptive classification test criteria in terms of test efficiency and measurement precision. Journal of Measurement and Evaluation in Education and Psychology, 9(2), 161-177. doi: 10.21031/epod.401077
  • Gündeğer, C., & Doğan, N. (2018b). The effects of item pool characteristics on test length and classification accuracy in computerized adaptive classification testings. Hacettepe University Journal of Education, 33(4), 888-896. doi: 10.16986/HUJE.2016024284
  • Huebner, A. (2012). Item overexposure in computerized classification tests using sequential item selection. Practical Assessment, Research & Evaluation, 17(12), 1-9. Retrieved from https://pareonline.net/getvn.asp?v=17&n=12
  • Huebner, A., & Li, Z. (2012). A stochastic method for balancing item exposure rates in computerized classification tests. Applied Psychological Measurement, 36(3), 181-188. doi: 10.1177/0146621612439932
  • Kingsbury, G. G., & Weiss, D. J. (1980). A Comparison of adaptive, sequential and conventional testing strategies for mastery decisions (Research Report 80-4). University of Minnesota, Minneapolis: MN. Retrieved from http://iacat.org/sites/default/files/biblio/ki80-04.pdf
  • Kingsbury, G. G., & Weiss, D.J. (1983). A comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure. In D. J. Weiss (Ed.), New horizons in testing: Latent trait theory and computerized adaptive testing, (pp. 237-254). New York: Academic Press.
  • Kingsbury, G. G., & Zara, A.R. (1989). Procedures for selecting items for computerized adaptive tests. Applied Measurement in Education, 2(4), 359-375. doi: 10.1207/s15324818ame0204_6
  • Lau, C. A. (1996). Robustness of a unidimensional computerized testing mastery procedure with multidimensional testing data (Unpublished doctoral dissertation). University of Iowa, Iowa City IA.
  • Lau, C. A., & Wang, T. (1999, April). Computerized classification testing under practical constraints with a polytomous model. Paper presented at the annual meeting of the American Educational Research Association (AERA), Montreal, Canada. Retrieved from http://iacat.org/sites/default/files/biblio/la99-01.pdf
  • Leroux, A. J., Waid-Ebbs, J. K., Wen, P-S., Helmer, D. A., Graham, D. P., O’Connor, M. K, & Ray, K. (2019). An investigation of exposure control methods with variable-length cat using the partial credit model. Applied Psychological Measurement, 43(8),624-638. doi: 10.1177/0146621618824856
  • Leung, C.-K., Chang, H. H., & Hau, K. T. (2002). Item selection in computerized adaptive testing: Improving the a-stratified design with the Sympson–Hetter algorithm. Applied Psychological Measurement, 26(4), 376-392. doi: 10.1177/014662102237795
  • Lin, C. (2011). Item selection criteria with practical constraints for computerized classification testing. Applied Psychological Measurement 71(1), 20-36. doi: 10.1177/0013164410387336
  • Lin, C. J., & Spray, J. (2000). Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT (Research Report 2000-8). Iowa city, IA: ACT Research Report Series. Retrieved from https://eric.ed.gov/?id=ED445066
  • Miller, I., & Miller, M. (2004). John E. Freund’s mathematical statistics with applications. (7th Ed.). New Jersey: Prentice Hall.
  • Nydick, S. W., Nozawa, Y., & Zhu, R. (2012, April). Accuracy and efficiency in classifying examinees using computerized adaptive tests: An application to a large-scale test. Paper presented at the annual meeting of the National Council on Measurement in Education (NCME), Vancouver, British Columbia, Canada. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.3381&rep=rep1&type=pdf
  • R Core Team (2013). R: A language and environment for statistical computing, (Version 3.0.1) [Computer software], Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/
  • Reckase, M. D. (1983). A procedure for decision making using tailored testing. In D. J. Weiss (Ed.), New horizons in testing: latent trait theory and computerized adaptive testing, (pp. 237-254). New York: Academic Press.
  • Sie, H., Finkelman, M. D., Riley, B., & Smits, N. (2015). Utilizing response times in computerized classification testing. Applied Psychological Measurement, 39(5), 389-405. doi: 10.1177/0146621615569504
  • Spray, J. A., & Reckase, M. D. (1996). Comparison of SPRT and sequential bayes procedures for classifying examinees into two categories using a computerized test. Journal of Educational and Behavioral Statistics, 21(4), 405-414. doi: 10.3102/10769986021004405
  • Sympson, J. B., & Hetter, R. D. (1985, October). Controlling item exposure rates in computerized adaptive testing. In Proceedings of the 27th annual meeting of the Military Testing Association (pp. 937-977). San Diego, CA: Navy Personnel Research and Development Center. Retrieved from http://www.iacat.org/content/controlling-item-exposure-rates-computerized-adaptive-testing
  • Thompson, N. A. (2007a). A comparison of two methods of polytomous computerized classification testing for multiple cutscores (Unpublished doctoral dissertation). University of Minnesota, Minneapolis.
  • Thompson, N. A. (2007b). A practitioner’s guide for variable-length computerized classification testing. Practical Assessment Research & Evaluation, 12(1), 1-13. Retrieved from http://www.iacat.org/sites/default/files/biblio/th07-01.pdf
  • Thompson, N. A. (2009). Item selection in computerized classification testing. Educational and Psychological Measurement, 69(5), 778-793. doi: 10.1177/0013164408324460
  • Thompson, N. A. (2011). Termination criteria for computerized classification testing. Practical Assessment, Research & Evaluation, 16(4), 1-7. Retrieved from https://pareonline.net/getvn.asp?v=16&n=4
  • Thompson, N. A., & Ro, S. (2007). Computerized classification testing with composite hypotheses. In D. J. Weiss (Ed.). Proceedings of the 2007 GMAC conference on computerized adaptive testing. Retrieved from http://www.iacat.org/sites/default/files/biblio/cat07nthompson.pdf
  • Van der Linden, W. J., & Veldkamp, B. P. (2004). Constraining item exposure in computerized adaptive testing with shadow tests. Journal of Educational and Behavioral Statistics, 29(3), 273-291. doi: 10.3102/10769986029003273
  • Van Groen, M. M., Eggen, T. J. H. M., & Veldkamp, B. P. (2016). Multidimensional computerized adaptive testing for classifying examinees with within-dimensionality. Applied Psychological Measurement, 40(6), 387-404. doi: 10.1177/0146621616648931
  • Wang, S., & Wang, T. (2001). Precision of warm’s weighted likelihood estimates for a polytomous model in computerized adaptive testing. Applied Psychological Measurement, 25(4), 317–331. doi: 10.1177/01466210122032163
  • Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54(3), 427-450. doi: 10.1007/BF02294627
  • Weiss, D. J., & Kingsbury, G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361-375. Retrieved from https://doi.org/10.1111/j.1745-3984.1984.tb01040.x
  • Wouda, J. T., & Eggen, T. J. H. M. (2009). Computerized classification testing in more than two categories by using stochastic curtailment. In D. J. Weiss (Ed.), Proceedings of the 2009 GMAC conference on computerized adaptive testing. Retrieved from http://iacat.org/sites/default/files/biblio/cat09wouda.pdf
  • Yang, X., Poggio, J. C., & Glasnapp, D. R. (2006). Effects of estimation bias on multiple category classification with an IRT-based adaptive classification procedure. Educational and Psychological Measurement, 66(4), 545-564. doi: 10.1177/0013164405284031

Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests

Year 2021, Volume 12, Issue 1, 15 - 27, 31.03.2021
https://doi.org/10.21031/epod.787865

Abstract

This study aims to compare Sequential Probability Ratio Test (SPRT) and Confidence Interval (CI) classification criteria, Maximum Fisher Information method on the basis of estimated-ability (MFI-EB) and Cut-Point (MFI-CB) item selection methods while ability estimation method is Weighted Likelihood Estimation (WLE) in Computerized Adaptive Classification Testing (CACT), according to the Average Classification Accuracy (ACA), Average Test Length (ATL), and measurement precision under content balancing (Constrained Computerized Adaptive Testing: CCAT and Modified Multinomial Model: MMM) and item exposure control (Sympson-Hetter Method: SH and Item Eligibility Method: IE) when the classification is done based on two, three, or four categories for a unidimensional pool of dichotomous items. Forty-eight conditions are created in Monte Carlo (MC) simulation for the data, generated in R software, including 500 items and 5000 examinees, and the results are calculated over 30 replications. As a result of the study, it was observed that CI performs better in terms of ATL, and SPRT performs better in ACA and correlation, bias, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values, sequentially; MFI-EB is more useful than MFI-CB. It was also seen that MMM is more successful in content balancing, whereas CCAT is better in terms of test efficiency (ATL and ACA), and IE is superior in terms of item exposure control though SH is more beneficial in test efficiency. Besides, increasing the number of classification categories increases ATL but decreases ACA, and it gives better results in terms of the correlation, bias, RMSE, and MAE values.

References

  • Bao, Y., Shen, Y., Wang, S., & Bradshaw, L. (2021). Flexible computerized adaptive tests to detect misconceptions and estimate ability simultaneously. Applied Psychological Measurement, 45(1), 3-21. doi: 10.1177/0146621620965730
  • Dooley, K. (2002). Simulation research methods. In J. Baum (Ed.), Companion to organizations (pp. 829-848). London: Blackwell.
  • Eggen, T. J. H. M. (1999). Item selection in adaptive testing with the sequential probability ratio test. Applied Psychological Measurement, 23(3), 249-261. doi: 10.1177/01466219922031365
  • Eggen, T. J. H. M., & Straetmans, G. J. J. M. (2000). Computerized adaptive testing for classifying examinees into three categories. Educational and Psychological Measurement, 60(5), 713-734. doi: 10.1177/00131640021970862
  • Fan, Z., Wang, C., Chang, H., & Douglas, J. (2012). Utilizing response time distributions for item selection in CAT. Journal of Educational and Behavioral Statistics, 37(5), 655-670. doi: 10.3102/1076998611422912
  • Finkelman, M. (2008). On using stochastic curtailment to shorten the SPRT in sequential mastery testing. Journal of Educational and Behavioral Statistics, 33(4), 442-463. doi: 10.3102/1076998607308573
  • Gündeğer, C., & Doğan, N. (2018a). A comparison of computerized adaptive classification test criteria in terms of test efficiency and measurement precision. Journal of Measurement and Evaluation in Education and Psychology, 9(2), 161-177. doi: 10.21031/epod.401077
  • Gündeğer, C., & Doğan, N. (2018b). The effects of item pool characteristics on test length and classification accuracy in computerized adaptive classification testings. Hacettepe University Journal of Education, 33(4), 888-896. doi: 10.16986/HUJE.2016024284
  • Huebner, A. (2012). Item overexposure in computerized classification tests using sequential item selection. Practical Assessment, Research & Evaluation, 17(12), 1-9. Retrieved from https://pareonline.net/getvn.asp?v=17&n=12
  • Huebner, A., & Li, Z. (2012). A stochastic method for balancing item exposure rates in computerized classification tests. Applied Psychological Measurement, 36(3), 181-188. doi: 10.1177/0146621612439932
  • Kingsbury, G. G., & Weiss, D. J. (1980). A Comparison of adaptive, sequential and conventional testing strategies for mastery decisions (Research Report 80-4). University of Minnesota, Minneapolis: MN. Retrieved from http://iacat.org/sites/default/files/biblio/ki80-04.pdf
  • Kingsbury, G. G., & Weiss, D.J. (1983). A comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure. In D. J. Weiss (Ed.), New horizons in testing: Latent trait theory and computerized adaptive testing, (pp. 237-254). New York: Academic Press.
  • Kingsbury, G. G., & Zara, A.R. (1989). Procedures for selecting items for computerized adaptive tests. Applied Measurement in Education, 2(4), 359-375. doi: 10.1207/s15324818ame0204_6
  • Lau, C. A. (1996). Robustness of a unidimensional computerized testing mastery procedure with multidimensional testing data (Unpublished doctoral dissertation). University of Iowa, Iowa City IA.
  • Lau, C. A., & Wang, T. (1999, April). Computerized classification testing under practical constraints with a polytomous model. Paper presented at the annual meeting of the American Educational Research Association (AERA), Montreal, Canada. Retrieved from http://iacat.org/sites/default/files/biblio/la99-01.pdf
  • Leroux, A. J., Waid-Ebbs, J. K., Wen, P-S., Helmer, D. A., Graham, D. P., O’Connor, M. K, & Ray, K. (2019). An investigation of exposure control methods with variable-length cat using the partial credit model. Applied Psychological Measurement, 43(8),624-638. doi: 10.1177/0146621618824856
  • Leung, C.-K., Chang, H. H., & Hau, K. T. (2002). Item selection in computerized adaptive testing: Improving the a-stratified design with the Sympson–Hetter algorithm. Applied Psychological Measurement, 26(4), 376-392. doi: 10.1177/014662102237795
  • Lin, C. (2011). Item selection criteria with practical constraints for computerized classification testing. Applied Psychological Measurement 71(1), 20-36. doi: 10.1177/0013164410387336
  • Lin, C. J., & Spray, J. (2000). Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT (Research Report 2000-8). Iowa city, IA: ACT Research Report Series. Retrieved from https://eric.ed.gov/?id=ED445066
  • Miller, I., & Miller, M. (2004). John E. Freund’s mathematical statistics with applications. (7th Ed.). New Jersey: Prentice Hall.
  • Nydick, S. W., Nozawa, Y., & Zhu, R. (2012, April). Accuracy and efficiency in classifying examinees using computerized adaptive tests: An application to a large-scale test. Paper presented at the annual meeting of the National Council on Measurement in Education (NCME), Vancouver, British Columbia, Canada. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.3381&rep=rep1&type=pdf
  • R Core Team (2013). R: A language and environment for statistical computing, (Version 3.0.1) [Computer software], Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/
  • Reckase, M. D. (1983). A procedure for decision making using tailored testing. In D. J. Weiss (Ed.), New horizons in testing: latent trait theory and computerized adaptive testing, (pp. 237-254). New York: Academic Press.
  • Sie, H., Finkelman, M. D., Riley, B., & Smits, N. (2015). Utilizing response times in computerized classification testing. Applied Psychological Measurement, 39(5), 389-405. doi: 10.1177/0146621615569504
  • Spray, J. A., & Reckase, M. D. (1996). Comparison of SPRT and sequential bayes procedures for classifying examinees into two categories using a computerized test. Journal of Educational and Behavioral Statistics, 21(4), 405-414. doi: 10.3102/10769986021004405
  • Sympson, J. B., & Hetter, R. D. (1985, October). Controlling item exposure rates in computerized adaptive testing. In Proceedings of the 27th annual meeting of the Military Testing Association (pp. 937-977). San Diego, CA: Navy Personnel Research and Development Center. Retrieved from http://www.iacat.org/content/controlling-item-exposure-rates-computerized-adaptive-testing
  • Thompson, N. A. (2007a). A comparison of two methods of polytomous computerized classification testing for multiple cutscores (Unpublished doctoral dissertation). University of Minnesota, Minneapolis.
  • Thompson, N. A. (2007b). A practitioner’s guide for variable-length computerized classification testing. Practical Assessment Research & Evaluation, 12(1), 1-13. Retrieved from http://www.iacat.org/sites/default/files/biblio/th07-01.pdf
  • Thompson, N. A. (2009). Item selection in computerized classification testing. Educational and Psychological Measurement, 69(5), 778-793. doi: 10.1177/0013164408324460
  • Thompson, N. A. (2011). Termination criteria for computerized classification testing. Practical Assessment, Research & Evaluation, 16(4), 1-7. Retrieved from https://pareonline.net/getvn.asp?v=16&n=4
  • Thompson, N. A., & Ro, S. (2007). Computerized classification testing with composite hypotheses. In D. J. Weiss (Ed.). Proceedings of the 2007 GMAC conference on computerized adaptive testing. Retrieved from http://www.iacat.org/sites/default/files/biblio/cat07nthompson.pdf
  • Van der Linden, W. J., & Veldkamp, B. P. (2004). Constraining item exposure in computerized adaptive testing with shadow tests. Journal of Educational and Behavioral Statistics, 29(3), 273-291. doi: 10.3102/10769986029003273
  • Van Groen, M. M., Eggen, T. J. H. M., & Veldkamp, B. P. (2016). Multidimensional computerized adaptive testing for classifying examinees with within-dimensionality. Applied Psychological Measurement, 40(6), 387-404. doi: 10.1177/0146621616648931
  • Wang, S., & Wang, T. (2001). Precision of warm’s weighted likelihood estimates for a polytomous model in computerized adaptive testing. Applied Psychological Measurement, 25(4), 317–331. doi: 10.1177/01466210122032163
  • Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54(3), 427-450. doi: 10.1007/BF02294627
  • Weiss, D. J., & Kingsbury, G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21(4), 361-375. Retrieved from https://doi.org/10.1111/j.1745-3984.1984.tb01040.x
  • Wouda, J. T., & Eggen, T. J. H. M. (2009). Computerized classification testing in more than two categories by using stochastic curtailment. In D. J. Weiss (Ed.), Proceedings of the 2009 GMAC conference on computerized adaptive testing. Retrieved from http://iacat.org/sites/default/files/biblio/cat09wouda.pdf
  • Yang, X., Poggio, J. C., & Glasnapp, D. R. (2006). Effects of estimation bias on multiple category classification with an IRT-based adaptive classification procedure. Educational and Psychological Measurement, 66(4), 545-564. doi: 10.1177/0013164405284031

Details

Primary Language English
Subjects Social
Published Date İlkbahar
Journal Section Articles
Authors

Seda DEMİR> (Primary Author)
TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ
0000-0003-4230-5593
Türkiye


Burcu ATAR>
HACETTEPE ÜNİVERSİTESİ
0000-0003-3527-686X
Türkiye

Publication Date March 31, 2021
Published in Issue Year 2021, Volume 12, Issue 1

Cite

Bibtex @research article { epod787865, journal = {Journal of Measurement and Evaluation in Education and Psychology}, issn = {1309-6575}, eissn = {1309-6575}, address = {}, publisher = {Eğitimde ve Psikolojide Ölçme ve Değerlendirme Derneği}, year = {2021}, volume = {12}, number = {1}, pages = {15 - 27}, doi = {10.21031/epod.787865}, title = {Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests}, key = {cite}, author = {Demir, Seda and Atar, Burcu} }
APA Demir, S. & Atar, B. (2021). Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests . Journal of Measurement and Evaluation in Education and Psychology , 12 (1) , 15-27 . DOI: 10.21031/epod.787865
MLA Demir, S. , Atar, B. "Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests" . Journal of Measurement and Evaluation in Education and Psychology 12 (2021 ): 15-27 <https://dergipark.org.tr/en/pub/epod/issue/61109/787865>
Chicago Demir, S. , Atar, B. "Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests". Journal of Measurement and Evaluation in Education and Psychology 12 (2021 ): 15-27
RIS TY - JOUR T1 - Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests AU - Seda Demir , Burcu Atar Y1 - 2021 PY - 2021 N1 - doi: 10.21031/epod.787865 DO - 10.21031/epod.787865 T2 - Journal of Measurement and Evaluation in Education and Psychology JF - Journal JO - JOR SP - 15 EP - 27 VL - 12 IS - 1 SN - 1309-6575-1309-6575 M3 - doi: 10.21031/epod.787865 UR - https://doi.org/10.21031/epod.787865 Y2 - 2021 ER -
EndNote %0 Journal of Measurement and Evaluation in Education and Psychology Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests %A Seda Demir , Burcu Atar %T Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests %D 2021 %J Journal of Measurement and Evaluation in Education and Psychology %P 1309-6575-1309-6575 %V 12 %N 1 %R doi: 10.21031/epod.787865 %U 10.21031/epod.787865
ISNAD Demir, Seda , Atar, Burcu . "Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests". Journal of Measurement and Evaluation in Education and Psychology 12 / 1 (March 2021): 15-27 . https://doi.org/10.21031/epod.787865
AMA Demir S. , Atar B. Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests. JMEEP. 2021; 12(1): 15-27.
Vancouver Demir S. , Atar B. Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests. Journal of Measurement and Evaluation in Education and Psychology. 2021; 12(1): 15-27.
IEEE S. Demir and B. Atar , "Investigation of Classification Accuracy, Test Length and Measurement Precision at Computerized Adaptive Classification Tests", Journal of Measurement and Evaluation in Education and Psychology, vol. 12, no. 1, pp. 15-27, Mar. 2021, doi:10.21031/epod.787865