A Comparison of Methods for Detecting Examinee Preknowledge of Items

References

• Belov, D. I. (2013). Detection of test collusion via Kullback-Leibler divergence. Journal of Educational Measurement, 50(2), 141–163. doi: 10.1111/jedm.12008
   Web of Science ®Google Scholar
• Belov, D. I. (2014). Detecting item preknowledge in computerized adaptive testing using information theory and combinatorial optimization. Journal of Computerized Adaptive Testing, 2(3), 37–58. doi: 10.7333/1410-0203037
   Google Scholar
• Belov, D. I. (2015). Comparing the performance of eight item preknowledge detection statistics. Applied Psychological Measurement, 40(2), 83–97. doi: 10.1177/0146621615603327
   PubMed Web of Science ®Google Scholar
• Birnbaum, A. (1968). Some latent trait models. In F. M. Lord & M. R. Novick, (Eds.), Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
   Google Scholar
• Casella, G., & Berger, R. L. (2002). Statistical inference. Australia: Thomson Learning.
   Google Scholar
• Diao, Q., & van der Linden, W. J. (2011). Automated test assembly using lp_Solve version 5.5 in R. Applied Psychological Measurement, 35(5), 398–409. doi: 10.1177/0146621610392211
   Web of Science ®Google Scholar
• Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. New York: Chapman & Hall.
   Google Scholar
• Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Thousand Oaks, CA, US: Sage Publications, Inc.
   Google Scholar
• Hechinger, J. (2008). Schools cancel GMAT scores. Wall Street Journal. Retrieved from http://www.wsj.com/articles/SB122109733923122015.
   Google Scholar
• International Test Commission (ITC). (2014, July 6). ITC guidelines on the security of tests, examinations, and other assessments. Retrieved from https://www.intestcom.org/files/guideline_test_security.pdf.
   Google Scholar
• Karabatsos, G. (2003). Comparing the aberrant response detection performance of thirty-six person-fit statistics. Applied Measurement in Education, 16(4), 277–298. doi: 10.1207/S15324818AME1604_2
   Web of Science ®Google Scholar
• Konis, K. (2009). lpSolveAPI, version 5.5.0.15 [Computer software]. Retrieved from http://CRAN.R-project.org/package=lpSolveAPI.
   Google Scholar
• Lewis, C., Lee, Y., & von Davier, A. A. (2012, May). Test security for multistage tests: A quality control perspective. Paper presented at the Conference on Statistical Detection of Potential Test Fraud, Lawrence, KS.
   Google Scholar
• McLeod, L. D., & Lewis, C. (1999). Detecting item memorization in the CAT environment. Applied Psychological Measurement, 23(2), 147–160. doi: 10.1177/01466219922031275
   Web of Science ®Google Scholar
• McLeod, L. D., Lewis, C., & Thissen, D. (2003). A Bayesian method for the detection of item preknowledge in computerized adaptive testing. Applied Psychological Measurement, 27(2), 121–137. doi: 10.1177/0146621602250534
   Web of Science ®Google Scholar
• Meijer, R. R., & Nering, M. L. (1997). Trait level estimation for nonfitting response vectors. Applied Psychological Measurement, 21(4), 321–336. 
 doi: 10.1177/01466216970214003
   Web of Science ®Google Scholar
• Meijer, R. R., & Sijtsma, K. (2001). Methodology review: Evaluating person fit. Applied Psychological Measurement, 25(2), 107–135. doi: 10.1177/01466210122031957
   Web of Science ®Google Scholar
• Muraki, E., & Bock, R. D. (2003). PARSCALE 4 for Windows: IRT based test scoring and item analysis for graded items and rating scales [Computer software]. Skokie, IL: Scientific Software International, Inc.
   Google Scholar
• R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/.
   Google Scholar
• Segall, D. (2002). An item response model for characterizing test comprise. Journal of Educational and Behavioral Statistics, 27(2), 163–179. doi: 10.3102/10769986027002163
   Web of Science ®Google Scholar
• Shu, Z., Henson, R., & Luecht, R. (2013). Using deterministic, gated item response theory model to detect test cheating due to item compromise. Psychometrika, 78(3), 481–497. doi: 10.1007/s11336-012-9311-3
   PubMed Web of Science ®Google Scholar
• Sinharay, S. (2015). Assessment of person fit for mixed-format tests. Journal of Educational and Behavioral Statistics, 40(4), 343–365. doi: 10.3102/1076998615589128
   Web of Science ®Google Scholar
• Sinharay, S. (2017a). Detection of item preknowledge using likelihood ratio test and score test. Journal of Educational and Behavioral Statistics, 42(1), 46–68. doi: 10.3102/1076998616673872
   Web of Science ®Google Scholar
• Sinharay, S. (2017b). Which statistic should be used to detect item preknowledge when the set of compromised items is known? Applied Psychological Measurement, 41(6), 403–421. doi: 10.1177/0146621617698453
   PubMed Web of Science ®Google Scholar
• Wang, X., Liu, Y., & Hambleton, R. K. (2017). Detecting candidate preknowledge of items using a predictive checking method. Applied Psychological Measurement, 41(4), 243–263. doi: 10.1177/0146621616687285
   PubMed Web of Science ®Google Scholar
• Wang, C., Zheng, Y., & Chang, H.-H. (2014). Does standard deviation matter? Using “standard deviation” to quantify security of multistage testing. Psychometrika, 79(1), 154–174. doi: 10.1007/s11336-013-9356-y
   PubMed Web of Science ®Google Scholar
• Wollack, J. A., & Fremer, J. J. (Eds.). (2013). Handbook of test security. New York: Routledge.
   Google Scholar
• Yan, D., von Davier, A. A., & Lewis, C. (2014). Computerized multistage testing: Theory and applications. New York, NY: CRC Press.
   Google Scholar