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== Item selection ==
In a CCT, items are selected for administration throughout the test, unlike the traditional method of administering a fixed set of items to all examinees. While this is usually done by individual item, it can also be done in groups of items known as [[testlet]]s (Leucht & Nungester, 1996;<ref>Luecht, R. M., & Nungester, R. J. (1998). Some practical examples of computer-adaptive sequential testing. Journal of Educational Measurement, 35, 229-249.</ref> Vos & Glas, 2000<ref
</ref>).
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== Bibliography of CCT research ==
{{refbegin}}
*Armitage, P. (1950). Sequential analysis with more than two alternative hypotheses, and its relation to discriminant function analysis. [[Journal of the Royal Statistical Society]], 12,
*Braun, H., Bejar, I.I., and Williamson, D.M. (2006). Rule-based methods for automated scoring: Application in a licensing context. In Williamson, D.M., Mislevy, R.J., and Bejar, I.I. (Eds.) Automated scoring of complex tasks in computer-based testing. Mahwah, NJ: Erlbaum.
*Dodd, B. G., De Ayala, R. J., & Koch, W. R. (1995). Computerized adaptive testing with polytomous items. Applied Psychological Measurement, 19, 5-22.
*Eggen, T. J. H. M. (1999). Item selection in adaptive testing with the sequential probability ratio test. Applied Psychological Measurement, 23,
*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,
*Epstein, K. I., & Knerr, C. S. (1977). Applications of sequential testing procedures to performance testing. Paper presented at the 1977 Computerized Adaptive Testing Conference, Minneapolis, MN.
*Ferguson, R. L. (1969). The development, implementation, and evaluation of a computer-assisted branched test for a program of individually prescribed instruction. Unpublished doctoral dissertation, University of Pittsburgh.
*Frick, T. W. (1989). Bayesian adaptation during computer-based tests and computer-guided exercises. Journal of Educational Computing Research, 5,
*Frick, T. W. (1990). A comparison of three decisions models for adapting the length of computer-based mastery tests. Journal of Educational Computing Research, 6,
*Frick, T. W. (1992). Computerized adaptive mastery tests as expert systems. Journal of Educational Computing Research, 8,
*Huang, C.-Y., Kalohn, J.C., Lin, C.-J., and Spray, J. (2000). Estimating Item Parameters from Classical Indices for Item Pool Development with a Computerized Classification Test. (Research Report
*Jacobs-Cassuto, M.S. (2005). A Comparison of Adaptive Mastery Testing Using Testlets
With the 3-Parameter Logistic Model. Unpublished doctoral dissertation, University of Minnesota, Minneapolis, MN.
*Jiao, H., & Lau, A. C. (2003). The Effects of Model Misfit in Computerized Classification Test. Paper presented at the annual meeting of the National Council of Educational Measurement, Chicago, IL, April 2003.
*Jiao, H., Wang, S., & Lau, C. A. (2004). An Investigation of Two Combination Procedures of SPRT for Three-category Classification Decisions in Computerized Classification Test. Paper presented at the annual meeting of the American Educational Research Association, San Antonio, April 2004.
*Kalohn, J. C., & Spray, J. A. (1999). The effect of model misspecification on classification decisions made using a computerized test. Journal of Educational Measurement, 36,
*Kingsbury, G.G., & Weiss, D.J. (1979). An adaptive testing strategy for mastery decisions. Research report
*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.
*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.
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*Lau, C. A., & Wang, T. (1999). Computerized classification testing under practical constraints with a polytomous model. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Canada.
*Lau, C. A., & Wang, T. (2000). A new item selection procedure for mixed item type in computerized classification testing. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, Louisiana.
*Lewis, C., & Sheehan, K. (1990). Using Bayesian decision theory to design a computerized mastery test. Applied Psychological Measurement, 14,
*Lin, C.-J. & Spray, J.A. (2000). Effects of item-selection criteria on classification testing with the sequential probability ratio test. (Research Report
*Linn, R. L., Rock, D. A., & Cleary, T. A. (1972). Sequential testing for dichotomous decisions. Educational & Psychological Measurement, 32,
*Luecht, R. M. (1996). Multidimensional Computerized Adaptive Testing in a Certification or Licensure Context. Applied Psychological Measurement, 20,
*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.
*Rudner, L. M. (2002). An examination of decision-theory adaptive testing procedures. Paper presented at the annual meeting of the American Educational Research Association, April 1–5, 2002, New Orleans, LA.
*Sheehan, K., & Lewis, C. (1992). Computerized mastery testing with nonequivalent testlets. Applied Psychological Measurement, 16,
*Spray, J. A. (1993). Multiple-category classification using a sequential probability ratio test (Research Report
*Spray, J. A., Abdel-fattah, A. A., Huang, C., and Lau, C. A. (1997). Unidimensional approximations for a computerized test when the item pool and latent space are multidimensional (Research Report
*Spray, J. A., & Reckase, M. D. (1987). The effect of item parameter estimation error on decisions made using the sequential probability ratio test (Research Report
*Spray, J. A., & Reckase, M. D. (1994). The selection of test items for decision making with a computerized adaptive test. Paper presented at the Annual Meeting of the National Council for Measurement in Education (New Orleans, LA, April 5–7, 1994).
*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 & Behavioral Statistics,21,
*Thompson, N.A. (2006). Variable-length computerized classification testing with item response theory. CLEAR Exam Review, 17(2).
*Vos, H. J. (1998). Optimal sequential rules for computer-based instruction. Journal of Educational Computing Research, 19,
*Vos, H. J. (1999). Applications of Bayesian decision theory to sequential mastery testing. Journal of Educational and Behavioral Statistics, 24,
*Wald, A. (1947). Sequential analysis. New York: Wiley.
*Weiss, D. J., & Kingsbury, G. G. (1984). Application of computerized adaptive testing to educational problems. Journal of Educational Measurement, 21,
*Weissman, A. (2004). Mutual information item selection in multiple-category classification CAT. Paper presented at the Annual Meeting of the National Council for Measurement in Education, San Diego, CA.
*Weitzman, R. A. (1982a). Sequential testing for selection. Applied Psychological Measurement, 6,
*Weitzman, R. A. (1982b). Use of sequential testing to prescreen prospective entrants into military service. In D. J. Weiss (Ed.), Proceedings of the 1982 Computerized Adaptive Testing Conference. Minneapolis, MN: University of Minnesota, Department of Psychology, Psychometric Methods Program, 1982.
{{refend}}
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