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Item Selection and Ability Estimation in Adaptive Testing

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Computerized Adaptive Testing: Theory and Practice

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References

  • Andersen, E. B. (1980). Discrete statistical models with social sciences applications. Amsterdam: North-Holland.

    Google Scholar 

  • Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F. M. Lord & M. R. Novick, Statistical theories of mental test scores (pp. 397–479). Reading, MA: Addison-Wesley.

    Google Scholar 

  • Bock, R. D., & Mislevy, R. J. (1988). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6, 431–444.

    Google Scholar 

  • Chang, H.-H., & Ying, Z. (1996). A global information approach to computerized adaptive testing. Applied Psychological Measurement, 20, 213–229.

    Google Scholar 

  • Chang, H.-H., & Ying, Z. α-stratified multistage computerized adaptive testing. Applied Psychological Measurement, 23, 211–222.

    Google Scholar 

  • Chang, H.-H., & Ying, Z. (in press). Nonlinear designs for logistic item response models with application in computerized adaptive tests. The Annals of Statistics.

    Google Scholar 

  • Gelman, A., Carlin, J. B, Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. London: Chapman & Hall.

    Google Scholar 

  • Gulliksen, H. (1950). Theory of mental tests. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Lehmann, E. L., & Casella, G. (1998). Theory of point estimation. New York: Springer Verlag.

    Google Scholar 

  • Lord, F. M. (1971). The self-scoring flexilevel test. Journal of Educational Measurement, 8, 147–151.

    Article  Google Scholar 

  • Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Lord, F. M. (1986). Maximum likelihood and Bayesian parameter estimation in item response theory. Journal of Educational Measurement, 23, 157–162.

    Article  Google Scholar 

  • Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Mislevy, R. J. (1986). Bayes modal estimation in item response models. Psychometrika, 51, 177–195.

    Google Scholar 

  • Mislevy, R. J., & Bock, R. D. (1983). BILOG: Item and test scoring with binary logistic models [Computer program and manual]. Mooresville, IN: Scientific Software.

    Google Scholar 

  • Mislevy, R. J., & Wu, P.-K. (1988). Inferring examinee ability when some items response are missing (Research Report 88-48-ONR). Princeton, NJ: Educational Testing Service.

    Google Scholar 

  • Owen, R. J. (1969). A Bayesian approach to tailored testing (Research Report 69-92). Princeton, NJ: Educational Testing Service.

    Google Scholar 

  • Owen, R. J. (1975). A Bayesian sequential procedure for quantal response in the context of adaptive mental testing. Journal of the American Statistical Association, 70, 351–356.

    Google Scholar 

  • Rasch, G. Probabilistic models for some intelligence and attainment tests. Copenhagen: Denmarks Paedogogiske Institut.

    Google Scholar 

  • Samejima, F. (1973). A comment on Birnbaum’s three-parameter logistic model in latent trait theory. Psychometrika, 38, 221–233.

    Google Scholar 

  • Samejima, F. (1993). The bias function of the maximum-likelihood estimate of ability for the dichotomous response level. Psychometrika, 58, 195–210.

    Google Scholar 

  • Schnipke, D. L., & Green, B. F. (1995). A comparison of item selection routines in linear and adaptive testing. Journal of Educational Measurement, 32, 227–242.

    Article  Google Scholar 

  • Segall, D. O. (1997). Equating the CAT-ASVAB. In W. A. Sands, B. K. Waters and J. R. McBride (Eds.), Computerized adaptive testing: From inquiry to operation (pp. 181–198). Washington, DC: American Psychological Association.

    Google Scholar 

  • Stocking, M. L. (1996). An alternative method for scoring adaptive tests. Journal of Educational and Bahavioral Statistics, 21, 365–389.

    Google Scholar 

  • Thissen, D., & Mislevy, R. J. (1990). Testing algorithms. In H. Wainer (Ed.), Computerized adaptive testing: A primer (pp. 103–134). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Tsutakawa, R. K., & Johnson, C. (1990). The effect of uncertainty on item parameter estimation on ability estimates. Psychometrika, 55, 371–390.

    Google Scholar 

  • van der Linden, W. J. (1998). Bayesian item-selection criteria for adaptive testing. Psychometrika, 62, 201–216.

    Google Scholar 

  • van der Linden, W. J. (1999). A procedure for empirical initialization of the trait estimator in adaptive testing. Applied Psychological Measurement, 23, 21–29.

    Google Scholar 

  • van der Linden, W. J., & Glas, C. A. W. (2000). Capitalization on item calibration in adaptive testing. Applied Measurement in Education, 13, 35–53.

    Google Scholar 

  • van der Linden, W. J., & Glas, C. A. W. (submitted). Cross validating item parameter estimation in adaptive testing.

    Google Scholar 

  • Veerkamp, W. J. J., & Berger, M. P. F. Item-selection criteria for adaptive testing. Journal of Educational and Behavioral Statistics, 22, 203–226.

    Google Scholar 

  • Wainer, H., Lewis, C., Kaplan, B., & Braswell, J. (1991). Building algebra testlets: A comparison of hierarchical and linear structures. Journal of Educational Measurement, 28, 311–323.

    Google Scholar 

  • Wang, T., Hanson, B. A., Lau, C.-M. A. (1999). Reducing bias in CAT trait estimation: A comparison of approaches. Applied Psychological Measurement, 23, 263–278.

    Article  Google Scholar 

  • Wang, T. &, Vispoel, W. P. (1998). Properties of ability estimation methods in computerized adaptive testing. Journal of Educational Measurement, 35, 109–135.

    Article  Google Scholar 

  • Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory with tests of finite length. Psychometrika, 54, 427–450.

    Google Scholar 

  • Weiss, D. J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 4, 473–285.

    Google Scholar 

  • Weiss, D. J., & McBride, J. R. (1984). Bias and information of Bayesian adaptive testing. Applied Psychological Measurement, 8, 273–285.

    Google Scholar 

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van der Linden, W.J., Pashley, P.J. (2000). Item Selection and Ability Estimation in Adaptive Testing. In: van der Linden, W.J., Glas, G.A. (eds) Computerized Adaptive Testing: Theory and Practice. Springer, Dordrecht. https://doi.org/10.1007/0-306-47531-6_1

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  • DOI: https://doi.org/10.1007/0-306-47531-6_1

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-0-7923-6425-2

  • Online ISBN: 978-0-306-47531-3

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