The use of the generalized graded unfolding model (GGUM) in computerized adaptive testing was examined. The objective was to minimize the number of items required to produce equiprecise estimates of person locations. Simulations based on real data about college student attitudes toward abortion and on data generated to fit the GGUM were used. It was found that as few as 7 or 8 items were needed to produce accurate and precise person estimates using an expected a posteriori procedure. The number of items in the item bank (20, 40, or 60 items) and their distribution on the continuum (uniform locations or item clusters in moderately extreme locations) had only small effects on the accuracy and precision of the estimates. These results suggest that adaptive testing with the GGUM is a good method for achieving estimates with an approximately uniform level of precision using a small number of items.
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References
1.
Akkermans, W. , & Muraki, E. (1997). Item information and discrimination functions for trinary PCM items. Psychometrika, 62, 569–578.
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). A general hyperbolic cosine latent trait model for unfolding polytomous responses: Reconciling the Thurstone and Likert methodologies. British Journal of Mathematical and Statistical Psychology, 49, 347–365.
4.
Andrich, D. , & Lou, G. (1993). A hyperbolic cosine latent trait model for unfolding dichotomous single-stimulus responses. Applied Psychological Measurement, 17, 253–276.
5.
Bock, R. D. , & Mislevy, R. J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6, 431–444.
6.
Chen, S. , Hou, L., Fitzpatrick, S. J., & Dodd, B. (1997). The effect of population distribution and method of theta estimation on computerized adaptive testing (CAT) using the rating scale model. Educational and Psychological Measurement, 57, 422–439.
7.
Coombs, C. (1964). A theory of data. New York: Wiley.
8.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.
9.
Davison, M. L. (1977). On a metric, unidimensional unfolding model for attitudinal and developmental data. Psychometrika, 42, 523–548.
10.
Dawis, R. V. (1987). Scale construction. Journal of Counseling Psychology, 34, 481–489.
11.
De Ayala, R. J. , Schafer, W. D., & Sava-Bolesta, M. (1995). An investigation of the standard errors of expected a posteriori ability estimates. British Journal of Mathematical and Statistical Psychology, 47, 385–405.
12.
Dodd, B. G. , De Ayala, R. J., & Koch, W. R. (1995). Computerized adaptive testing with polytomous items. Applied Psychological Measurement, 19, 5–22.
13.
Dodd, B. G. , Koch, W. R., & De Ayala, R. J. (1993). Computerized adaptive testing using the partial credit model: Effects of item pool characteristics and different stopping rules. Educational and Psychological Measurement, 53, 61–77.
14.
Hays, W. L. (1988). Statistics (4th ed.). New York: Rinehart and Winston.
15.
Koch, W. R. , & Dodd, B. G. (1995). An investigation of procedures for computerized adaptive testing using the successive intervals Rasch model. Educational and Psychological Measurement, 55, 976–990.
16.
Laughlin, J. E. , Roberts, J. S., & Lin, Y. (1999, June). Optimal fixed length test designs for attitude measures utilizing graded response scales. Paper presented at the 64th annual meeting of the Psychometric Society, Lawrence KS.
17.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 5–55.
18.
Linacre, J. M. ,& Wright, B. D. (1994). A user’s guide to BIGSTEPS (Version 2.4). Chicago: MESA Press.
19.
Michell, J. (1994). Measuring dimensions of belief by unidimensional unfolding. Journal of Mathematical Psychology, 38, 244–273.
20.
Michell, J. (1998). Sensitivity of preferences and ratings to ordered metric structure in attitudes. Australian Journal of Psychology, 50, 199–204.
21.
Mueller, D. J. (1986). Measuring social attitudes: A handbook for researchers and practitioners. New York: Teachers College Press.
22.
Muraki, E. (1992). Ageneralized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159–176.
23.
Muraki, E. (1993). Information functions of the generalized partial credit model. Applied Psychological Measurement, 17, 351–363.
24.
NoÎl, Y. (1999). Recovering unimodal latent patterns of change by unfolding analysis: Application to smoking cessation. Psychological Methods, 4, 173–191.
25.
Roberts, J. S. (1995). Item response theory approaches to attitude measurement (Doctoral dissertation, University of South Carolina, Columbia, 1995). Dissertation Abstracts International, 56, 7089B.
26.
Roberts, J. S. , Donoghue, J. R., & Laughlin, J. E. (1996, June). A generalized item response model for unfolding responses from a graded scale. Paper presented at the 61st annual meeting of the Psychometric Society, Banff, Alberta, Canada.
27.
Roberts, J. S. , Donoghue, J. R., & Laughlin, J. E. (1998). The generalized graded unfolding model: A general parametric item response model for unfolding graded responses (Research Report No. RR-98-32). Princeton NJ: Educational Testing Service.
28.
Roberts, J. S. , Donoghue, J. R., & Laughlin, J. E. (1999, April). Estimating parameters in the generalized graded unfolding model: Sensitivity to the prior distribution assumption and the number of quadrature points used. Paper presented at the 1999 annual meeting of the National Council on Measurement in Education, Montreal, Quebec, Canada.
29.
Roberts, J. S. , Donoghue, J. R., & Laughlin, J. E. (2000). A general model for unfolding unidimensional polytomous responses using item response theory. Applied Psychological Measurement, 24, 3–32.
30.
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.
31.
Roberts, J. S. , Laughlin, J. E.,& Wedell, D. H. (1999). Validity issues in the Likert and Thurstone approaches to attitude measurement. Educational and Psychological Measurement, 59, 211–233.
32.
Spector, P. E. (1992). Summated rating scale construction: An introduction. (Sage University Paper series on Quantitative Applications in the Social Sciences, Series No. 07-082). Newbury Park CA: Sage.
33.
Thissen, D. (1990). Reliability and measurement precision. In H. Wainer, N. J. Dorans, R. Flaugher, B. F. Green, R. L. Mislevy, L. Steinberg,& D. Thissen (Eds.), Computerized adaptive testing: A primer (pp. 161–186). Hillsdale NJ: Erlbaum.
34.
Thurstone, L. L. ,& Chave, E. J. (1929). The measurement of attitude: A psychophysical method and some experiments with a scale for measuring attitude toward the Church. Chicago: University of Chicago Press.
35.
Toothaker, L. E. (1991). Multiple comparisons for research. Newbury Park CA: Sage.
36.
Wainer, H. , Dorans, N. J., Flaugher, R., Green, B. F., Mislevy, R. L., Steinberg, L., & Thissen, D. (1990). Computerized adaptive testing: A primer. Hillsdale NJ: Erlbaum.
37.
Weiss, D. J. (1982). Improving measurement quality and efficiency with adaptive testing. Applied Psychological Measurement, 6, 473–492.
38.
Wright, B. D. , & Masters, G. N. (1982). Rating scale analysis. Chicago IL: MESA Press.
