References
 
Software
 
  • 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.
  • Thissen, D., Chen, W-H., & Bock, R. D. (2003). MULTILOG 7 for Windows: Multiple-category item analysis and test scoring using item response theory [Computer software]. Skokie, IL: Scientific Software International, Inc.
  • Zimowski, M. F., Muraki, E., Mislevy, R. J., & Bock, R.D. (2003). BILOG-MG 3 for Windows: Multiple-group IRT analysis and test maintenance for binary items [Computer software]. Skokie, IL: Scientific Software International, Inc.
  • Wood, R., Wilson, D. T., Gibbons, R., Schilling, S., Muraki, E., & Bock, R. D. (2003). TESTFACT 4 for Windows: Test scoring, item statistics, and item factor analysis [Computer software]. Skokie, IL: Scientific Software International, Inc.
 
Books
 
  • Baker, F. B. (1992). Item response theory: parameter estimation techniques. Reading, NY: Marcel Dekker.
  • Bock, R. D. (1970). Estimating multinomial response relations. In R. C. Bose, et al.(Eds.), Contributions to statistics and probability. Chapel Hill, NC: University of North Carolina Press, 111-132.
  • Bock, R. D. (1993). Different DIFs. In: P. W. Holland & H. Wainer (Eds.), Differential item functioning. Hillsdale, NJ: Erlbaum, 115-122.
  • Bock, R. D. (1997). The nominal categories model. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of Modern Item Response Theory. New York: Springer Verlag, 33-65.
  • Bock, R. D., & Zimowski, M. F. (1995). Multiple group IRT. In W. van der Linden & R. Hambleton (Eds.),Handbook of item response theory. New York: Springer-Verlag.
  • Haberman, S. J. (1979). Analysis of qualitative data, Vol. 2. New developments. New York: Academic Press.
  • Hambleton, R. K., & Swaminathan, H. (1985). Item Response Theory. Principles and applications. Boston: Kluwer.
  • Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: Sage.
  • Holland, P. W., & Rubin, D. B. (Eds.) (1982). Test equating. Hillsdale, NJ: Erlbaum.
  • Holland, P. W., & Wainer, H. (1993). Differential Item Functioning. Hillsdale, NJ: Erlbaum.
  • Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: Erlbaum.
  • Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores (with contributions by A. Birnbaum). Reading, MA: Addison-Wesley.
  • Muraki, E. (1997). The generalized partial credit model. In W. J. van der Linden & R. K. Hambleton (Eds.),Handbook of Modern Item Response Theory. New York: Springer Verlag, 153164.
  • Petersen, N. S., Kolen, M. J., & Hoover, H. D. (1989). Scaling, norming, and equating. In R. L. Linn (Ed.),Educational Measurement (3rd edition). New York: American Council on Educa-tion-Macmillan, 221-262.
  • Stroud, A. H., & Secrest, D. (1966). Gaussian Quadrature Formulas. Englewood Cliffs, NJ: Prentice-Hall.
  • Thissen, D., Steinberg, L., & Wainer, H. (1993). Detection of DIF using the parameters of item response models. In P. W. Holland & H. Wainer (Eds.), Differential item functioning. Hillsdale, NJ: Erlbaum, 67-113.
  • Van der Linden, W. J., & Hambleton, R. K. (1997). Handbook of modern item response theory. New York: Springer-Verlag.
  • Wainer, H. (Ed.) (1990). Computerized adaptive testing: a primer. Hillsdale, NJ: Erlbaum.
 
Papers
 
  • Bock, R. D. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika, 37, 29-51.
  • Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: application of an EM algorithm. Psychometrika, 46, 443-445.
  • Bock, R. D., Gibbons, R. D., & Muraki, E. (1988). Full information item factor analysis. Applied Psychological Measurement, 12, 261-280.
  • Bock, R. D., & Lieberman, M. (1970). Fitting a response model for n dichotomously scored items. Psychometrika,35, 179-197.
  • Bock, R. D., Muraki, E., & Pfiffenberger, W. (1988). Item pool maintenance in the presence of item parameter drift.Journal of Educational Measurement, 25, 275-285.
  • Bock, R. D., Thissen, D., & Zimowski, M. F. (1997). IRT estimation of domain scores. Journal of Educational Measurement 34, 197-211.
  • Clogg, C. C., & Goodman, L. A. (1984). Latent structure analysis of a set of multi-dimensional contingency tables.Journal of the American Statistical Association, 79, 762-661.
  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1-38.
  • Gibbons, R. D., & Hedeker, D. R. (1992). Full information item bi-factor analysis. Psychometrika, 57, 423-436.
  • Haberman, S. J. (1977). Log-linear models and frequency tables with small expected cell counts. Annals of Statistics, 5, 1148-1169.
  • Hendrickson, E. A., & White, P. O. (1964). Promax: A quick method for rotation to oblique sim- ple structure.British Journal of Mathematical and Statistical Psychology, 17, 65.
  • Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psycho- metrika, 23, 187-200.
  • Kolakowski, D., & Bock, R. D. (1981). A multivariate generalization of probit analysis. Biometrics, 37, 541-551.
  • Linn, R. L., & Hambleton, R. K. (1991). Customized sets and customized norms. Applied Meas-urement in Education, 4, 185-207.
  • Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
  • Masters, G. N. (1985). A comparison of latent trait and latent class analyses of Likert-type data. Psychometrika,50, 69-82.
  • Mislevy, R. J. (1983). Item response models for grouped data. Journal of Educational Statistics, 8, 271-288.
  • Mislevy, R. J. (1984). Estimating latent distributions. Psychometrika, 49, 359-381.
  • Mislevy, R. J. (1986). Bayesian modal estimate in item response models. Psychometrika, 51, 177-195.
  • Mislevy, R. J. (1987). Exploiting auxiliary information about examinees in the estimation of item parameters.Applied Psychological Measurement, 11, 81-91.
  • Mislevy, R. J., Johnson, E. G., & Muraki, E. (1992). Scaling procedures in NAEP. Journal of Educational Statistics, 17, 131-154.
  • Muraki, E. (1990). Fitting a polytomous item response model to Likert-type data. Applied Psychological Measurement, 14, 59-71.
  • Muraki, E. (1992). A generalized partial credit model: application of an EM algorithm. Applied Psychological Measurement, 16, 159-176.
  • Muraki, E., & Engelhard, G. (1985). Full information item factor analysis: applications of EAP scores. Applied Psychological Measurement, 9, 417-430.
  • Naylor, J. C., & Smith, A. F. M. (1982). Applications of a method for the efficient computation of posterior distributions. Applied Statistics, 31, 214-225.
  • Olsson, U., Drasgow, F., & Dorans, N. J. (1982). The polyserial correlation coefficient. Psychometrika, 47(3), 337-347.
  • Samejima, F. (1969). Estimation of latent trait ability using a response pattern of graded scores. Psychometrika Monograph Supplement, No. 17.
  • Samejima, F. (1974). Normal ogive model on the continuous response level in the multidimensional latent space.Psychometrika, 39, 111-121.
  • Thissen, D. (1982). Marginal maximum likelihood estimation for the one-parameter logistic model. Psychometrika,47, 175-186.
  • Thissen, D., & Steinberg, L. (1984). A response model for multiple-choice items. Psychometrika, 49, 501-519.
  • Thissen, D., & Steinberg, L. (1986). A taxonomy of item response models. Psychometrika, 51, 566-577.
  • Thissen, D., & Steinberg, L. (1988). Data analysis using item response theory. Psychological Bulletin, 104, 385-395.
  • Thissen, D., Steinberg, L., & Fitzpatrick, A. R. (1989). Multiple-choice models: The distractors are also part of the item. Journal of Educational Measurement, 26, 161-176.
  • Thissen, D., Steinberg, L., & Money, J. A. (1989). Trace lines for testlets: A use of multiple-categorical-response models. Journal of Educational Measurement, 26, 247-260.
  • Thissen, D., & Wainer, H. (1982). Some standard errors in item response theory. Psychometrika, 47, 397-412.
  • Tsutakawa, R. K. (1992). Prior distribution for item response curves. British Journal of Mathematical and Statistical Psychology, 45, 51-71.
  • Tsutakawa, R. K., & Lin, H. Y. (1986). Bayesian estimation of item response curves. Psycho- metrika, 51, 251-267.
  • Wainer, H., & Kiely, G. L. (1987). Item clusters and computerized adaptive testing: A case for testlets. Journal of Educational Measurement, 24, 185-201.
  • Warm, T. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427-450.
  • Zwick, R. (1987). Assessing the dimensionality of NAEP reading data. Journal of Educational Measurement, 24, 293-308.
 
Other
 
  • Samejima, F. (1979). A new family of models for the multiple-choice item. Research Report, No. 79-4, Department of Psychology, University of Tennessee.
  • Zimowski, M. F. (1985). Attributes of spatial test items that influence cognitive processing.Un-published doctoral dissertation, University of Chicago.