The generalized graded unfolding model (GGUM) has been recently developed to describe item responses to Likert items (agree—disagree) in attitude measurement. In this study, the authors (a) developed two item selection methods in computerized classification testing under the GGUM, the current estimate/ability confidence interval method and the cut score/sequential probability ratio test method and (b) evaluated their accuracy and efficiency in classification through simulations. The results indicated that both methods were very accurate and efficient. The more points each item had and the fewer the classification categories, the more accurate and efficient the classification would be. However, the latter method may yield a very low accuracy in dichotomous items with a short maximum test length. Thus, if it is to be used to classify examinees with dichotomous items, the maximum text length should be increased.
Andrich, D. ( 1978). A rating formulation for ordered response categories . Psychometrika, 43, 561-573.
2.
Andrich, D. ( 1988). The application of an unfolding model of the PIRT type to the measurement of attitude. Applied Psychological Measurement , 12, 33-51.
3.
Andrich, D. ( 1996). Hyperbolic cosine latent trait models for unfolding direct-responses and pairwise preferences. Applied Psychological Measurement , 20, 269-290.
4.
Andrich, D., & Luo, G. ( 1993). A hyperbolic cosine latent trait model for unfolding dichotomous single-stimulus responses. Applied Psychological Measurement, 17, 253-276.
5.
Chang, H.-H., & Ying, Z. ( 1996). A global information approach to computerized adaptive testing. Applied Psychological Measurement, 20, 213-229.
6.
Coombs, C.H. ( 1964). A theory of data. New York, NY: Wiley.
7.
de la Torre, J., Stark, S., & Chernyshenko, O.S. (2006). Markov chain Monte Carlo estimation of item parameters for the generalized graded unfolding model . Applied Psychological Measurement, 30, 216-232.
8.
Eggen, T.J.H. M. (1999). Item selection in adaptive testing with the sequential probability ratio test. Applied Psychological Measurement , 23, 249-261.
9.
Hoijtink, H. ( 1990). A latent trait model for dichotomous choice data. Psychometrika, 55, 641-656.
10.
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 test theory and computerized adaptive testing (pp. 257-283). New York, NY : Academic Press.
11.
Luo, G. ( 2001). A class of probabilistic unfolding models for polytomous responses. Journal of Mathematical Psychology, 45, 224-248.
12.
Muraki, E. ( 1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159-176.
13.
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, NY: Academic Press.
14.
Roberts, J.S., Donoghue, J.R., & Laughlin, J.E. (2000). A general item response theory model for unfolding unidimensional polytomous responses. Applied Psychological Measurement, 24, 3-32.
15.
Roberts, J.S., Donoghue, J.R., & Laughlin, J.E. (2002). Characteristics of MML/EAP parameter estimates in the generalized graded unfolding model. Applied Psychological Measurement, 26, 192-207.
16.
Roberts, J.S., Fang, H., Cui, W., & Wang, Y. ( 2006). GGUM2004: A Windows-based program to estimate parameters in the generalized graded unfolding model. Applied Psychological Measurement, 30, 64-65.
17.
Roberts, J.S., & Huang, C. ( 2003). GGUMLINK: A computer program to link parameter estimates of the generalized graded unfolding model from item response theory. Behavior Research Methods, Instrumentation and Computers, 35, 525-549.
18.
Roberts, J.S., & Laughlin, J.E. (1996). A unidimensional item response model for unfolding responses from a graded disagree-agree response scale . Applied Psychological Measurement, 20, 231-255.
Rost, J., & Luo, G. ( 1997). An application of a Rasch based unfolding model to a questionnaire on adolescent centrism. In J. Rost & R. Langeheine (Eds.), Application of latent trait and latent class models in the social sciences (pp. 278-286). Munster, Germany: Waxmann Verlag.
21.
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, 405-414.
22.
Thompson, N.A. ( 2009). Item selection in computerized classification testing . Educational and Psychological Measurement, 69, 778-793.
23.
Veerkamp, W.J.J., & Berger, M.P.F. (1997). Some new item selection criteria for adaptive testing. Journal of Educational and Behavioral Statistics22, 203-226.
24.
Wald, A. ( 1947). Sequential analysis. New York, NY : Wiley.
25.
Weissman, A. ( 2007). Mutual information item selection in adaptive classification testing. Educational and Psychological Measurement, 67, 41-58.
