This paper describes a method for detecting and in terpreting disturbances of the local independence as sumption among items that share common stimulus material or other substantive features. Dichotomous and polytomous Rasch models are used in an example to analyze Structure of the Learning Outcome (SOLO) superitems and examine the results for local independ ence problems. The results indicate that some disturb ances were present among particular subsets of the items. Index terms: local independence, partial credit model, one-parameter logistic model, Rasch model, rating scale model.
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References
1.
Andrich, D. (1978). A rating formulation for ordered response categories . Psychometrika, 43, 561-573.
2.
Andrich, D. (1984). The attenuation paradox of traditional test theory as a breakdown of local independence in Person-item Response Theory. Paper presented at the annual meeting of the National Council on Measurement in Education, New Orleans.
3.
Andrich, D. (1985). A latent-trait model for items with response dependencies: Implications for test construction and analysis. In S. E. Ernbrctson (Ed.), Test design: Developments in psychology and psychometrics. Orlando FL: Academic Press.
4.
Andrich, D., & Pedler, P. (1983). The attenuation paradox in latent trait theory . Paper presented at the annual meeting of the National Council on Measurement in Education, Montreal .
5.
Biggs, J.B., & Collis, K.F. (1982). Evaluating the quality of learning: The SOLO taxonomy . New York: Academic Press.
6.
Collis, K.F. (1983). Development of a group test of mathematical understanding using superitem SOLO technique. Journal of Science and Mathematics Education in South East Asia, 6(1), 5-14.
7.
Collis, K.F. ( 1986). A technique for evaluating skills in high school science. Journal of Research in Science Teaching, 23, 651-663.
8.
Collis, K.F., & Davey, H.A. (1984). The development of a set of SOLO items for high school science. Hobart, Tasmania : University of Tasmania, Department of Education.
9.
Hambleton, R., & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston: Kluwer-Nijhoff .
10.
Masters, G.N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
11.
Masters, G.N., & Wilson, M.R. (1988). PC-CREDIT [Computer program]. Melbourne: University of Melbourne, Centre for the Study of Higher Education.
12.
Pedler, P. (1988). Accounting for psychometric dependence with a class of latent trait models. Unpublished doctoral dissertation, University of Western Australia.
13.
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research. [Expanded edition, University of Chicago Press, 1980.]
14.
Romberg, T.A., Collis, K.F., Donovan, B.F., Buchanan, A.E., & Romberg, M.N. (1982). The development of mathematical problem solving superitems. (Report of NIE/ECS Item Development Project.)Madison: Wisconsin Center for Education Research.
15.
Romberg, T.A., Jurdak, M.E., Collis, K.F., & Buchanan, A.E. (1982). Construct validity of a set of mathematical superitems. (Report of NIE/ECS Item Development Project.)Madison: Wisconsin Center for Education Research.
16.
Rost, J. (1982). An unconditional maximum likelihood ratio for testing item homogeneity in the Rasch model. Educational Research and Perspectives, 9, 7-17.
17.
van den Wollenberg, A.L. (1982). Two new test statistics for the Rasch model. Psychometrika, 47, 123-140.
18.
Wilson, M. (1985). Measuring stages of growth. Hawthorn, Australia: ACER.
19.
Wilson, M. (in press). A comparison of deterministic and probabilistic approaches to measuring learning structures. Australian Journal of Education .
Wright, B.D., Masters, G.N., & Ludlow, L.H. (1981). CREDIT: A Rasch program for ordered categories [Computer program]. Chicago: University of Chicago, Department of Education, MESA Psychometric Laboratory.
22.
Wright, B.D., & Stone, M. (1979). Best test design. Chicago: MESA Press.
23.
Yen, W.M. (1984). Effects of local item dependence on the fit and equating performance of the three-parameter logistic model. Applied Psychological Measurement, 8, 125-145.
