A model for solving very large item selection problems is presented. The model builds on previous work in binary programming applied to test con struction. Expert test construction practices are applied to situations in which all specifications for item selection cannot necessarily be met. A heuristic for selecting items that satisfy the constraints in the model also is presented. The heuristic is particu larly useful for situations in which the size of the test construction problem exceeds the limits of current implementations of linear programming algorithms. A variety of test construction problems involving real test specifications and item data from actual test assemblies were investigated using the model and the heuristic.
Ackerman, T. (1989, March). An alternative methodology for creating parallel test forms using the IRT information function. Paper presented at the annual meeting of the National Council for Measurement in Education, San Francisco.
2.
Adema, J.J. (1988). A note on solving large-scale zero-one programming problems (Research Rep. 88-4. Enschede, The Netherlands: Department of Education, University of Twente.
3.
Adema, J.J. (1990). Models and algorithms for the construction of achievement tests. The Haag, The Netherlands : CIP-gegevens Koninklijke Bibliotheek.
4.
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. 395-479). Reading MA: Addison-Wesley.
5.
Boekkooi-Timminga, E. (1989). Models for computerized test construction. The Haag, The Netherlands: Academisch Boeken Centrum .
6.
Brooke, A., Kendrick, D., & Meeraus, A. (1988). GAMS: A user's guide [Computer program and manual] . Redwood City CA: The Scientific Press.
7.
Hsu, T., & Sadock, S. (1985). Computer-assisted test construction: A state of the art (TME Report 88. Princeton NJ: ERIC Clearinghouse on Tests, Measurement, and Evaluation, Educational Testing Service.
8.
Kester, J.G. (1988). Various mathematical programming approaches toward item selection (Report No. 3 of the Project "Optimal Item Selection". Arnhem, The Netherlands: CITO.
9.
Lord, F.M. (1952). A theory of test scores. Psychometric Monograph, No. 7.
10.
Lord, F.M. (1980). Applications of item response theory to practical testing problems. Hillsdale NJ: Erlbaum.
11.
Luecht, R.M., & Hirsch, T.M. (1992). Item selection using an average growth approximation of target information functions. Applied Psychological Measurement , 16, 41-51.
12.
Nemhauser, G.L., & Wolsey, L.A. (1988). Integer and combinatorial optimization. New York: Wiley.
13.
Stocking, M.L., Swanson, L., & Pearlman, M. (1991). Automatic item selection (AIS) methods in the ETS testing environment (Research Memorandum 91-5. Princeton NJ: Educational Testing Service.
14.
Stocking, M.L., Swanson, L., & Pearlman, M. (1993). The application of an automated item selection method to real data. Applied Psychological Measurement, 17, 167-176.
15.
Theunissen, T.J.J.M. (1985). Binary programming and test design. Psychometrika, 50, 411-420.
16.
Van der Linden, W.J. (1987). Automated test construction using minimax programming . In W. J. Van der Linden (Ed.), IRT-based test construction (pp. 1-16). Enschede, The Netherlands: Department of Education, University of Twente .
17.
Van derLinden, W.J., & Boekkooi-Timminga, E. (1989). A maximin model for test design with practical constraints. Psychometrika, 54, 237-248.
18.
Webb, J. (1969). A system for the computer assisted assembly of tests (Test Development Systems Rep. 69.3. Princeton NJ : Educational Testing Service.
19.
Yen, W.M. (1983). Use of the three-parameter model in the development of a standardized achievement test. In R. K. Hambleton (Ed.), Applications of item response theory (pp. 123-141). Vancouver BC: Educational Research Institute of British Columbia.
