Computerized classification test: Difference between revisions

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Methods of item selection fall into two categories: cutscore-based and estimate-based. Cutscore-based methods (also known as sequential selection) maximize the [[information]] provided by the item at the cutscore, or cutscores if there are more than one, regardless of the ability of the examinee. Estimate-based methods (also known as adaptive selection) maximize information at the current estimate of examinee [[ability]], regardless of the ___location of the cutscore. Both work efficiently, but the efficiency depends in part on the termination criterion employed. Because the [[sequential probability ratio test]] only evaluates probabilities near the cutscore, cutscore-based item selection is more appropriate. Because the [[confidence interval]] termination criterion is centered around the examinees ability estimate, estimate-based item selection is more appropriate. This is because the test will make a classification when the confidence interval is small enough to be completely above or below the cutscore (see below). The confidence interval will be smaller when the standard error of measurement is smaller, and the standard error of measurement will be smaller when there is more information at the theta level of the examinee.

== Termination criterion ==

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*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, 47-59.

*Kingsbury, G.G., & Weiss, D.J. (1979). An adaptive testing strategy for mastery decisions. Research report 79-05. Minneapolis: University of Minnesota, Psychometric Methods Laboratory.

*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&nbsp;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 Unpublished doctoral dissertation, University of Iowa, Iowa City IA.

*Lau, C. A., & Wang, T. (1998). Comparing and combining dichotomous and polytomous items with SPRT procedure in computerized classification testing. Paper presented at the annual meeting of the American Educational Research Association, San Diego.

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*Linn, R. L., Rock, D. A., & Cleary, T. A. (1972). Sequential testing for dichotomous decisions. Educational & Psychological Measurement, 32, 85-95.

*Luecht, R. M. (1996). Multidimensional Computerized Adaptive Testing in a Certification or Licensure Context. Applied Psychological Measurement, 20, 389-404.

*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&nbsp;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 1-51–5, 2002, New Orleans, LA.

*Sheehan, K., & Lewis, C. (1992). Computerized mastery testing with nonequivalent testlets. Applied Psychological Measurement, 16, 65-76.

*Spray, J. A. (1993). Multiple-category classification using a sequential probability ratio test (Research Report 93-7). Iowa City, Iowa: ACT, Inc.

*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 97-5). Iowa City, Iowa: ACT, Inc.

*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 87-17). Iowa City, IA: ACT, Inc.

*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 5-75–7, 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, 405-414.

*Thompson, N.A. (2006). Variable-length computerized classification testing with item response theory. CLEAR Exam Review, 17(2).

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==External links==

* [http://edres.org/mdt/ Measurement Decision Theory] by Lawrence Rudner

* [http://www.psych.umn.edu/psylabs/catcentral/ CAT Central] by David J. Weiss

{{DEFAULTSORT:Computerized Classification Test}}

[[Category:Psychometrics]]

[[Category:Educational assessment and evaluation]]