An approach to evaluating the missing data assumptions of the chain and post-stratification equating methods for the NEAT design

Journal of Educational Measurement

Volumn 45, Issue 1, 2008, Pages 17-43

  Holland, Paul W ^a   Sinharay, Sandip ^a   Von Davier, Alina A ^a   Han, Ning ^b,c  

^a EDUCATIONAL TESTING SERVICE   (United States)

^b Chinese Testing International

^c Chinese Testing International   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 40549127201     PISSN: 00220655     EISSN: 17453984     Source Type: Journal    
DOI: 10.1111/j.1745-3984.2007.00050.x     Document Type: Article

References (17)

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2. Dorans, N. J., Liu, J., & Hammond, S. (2005, April). The role of the anchor test in achieving population invariance across subpopulations and test administrations. Paper presented at the meeting of the National Council on Measurement in Education, San Diego, CA.
3. Holland, P. W., & Thayer, D. T. (2000). Univariate and bivariate loglinear models for discrete test score distributions. Journal of Educational and Behavioral Statistics, 25, 133–183.
4. Kolen, M. J., & Brennan, R. J. (2004). Test equating, scaling, and linking (2nd ed.). New York: Springer.
5. Livingston, S. A. (2004). Equating test scores (without IRT). Princeton, NJ: Educational Testing Service.
6. Livingston, S. A., Dorans, N. J., & Wright, N. K. (1990). What combination of sampling and equating methods works best Applied Measurement in Education, 3, 73–95.
7. Mosteller, F., & Youtz, C. (1961). Tables of the Freeman‐Tukey transformations for the binomial and Poisson distributions. Biometrika, 48, 433–440.
8. Read, T. R. C., & Cressie, N. A. C. (1988). Goodness‐of‐fit statistics for discrete multivariate data. New York: Springer‐Verlag.
9. Sinharay, S., & Holland, P. W. (2007). Is it necessary to make anchor tests mini‐versions of the tests being equated or can some restrictions be relaxed Journal of Educational Measurement, 44, 249–275.
10. Von Davier, A. A. (2003). Notes on linear equating methods for the Non‐Equivalent Groups design (ETS RR‐03–24). Princeton, NJ: Educational Testing Service.
11. Von Davier, A. A., Holland, P. W., Livingston, S. A., Casabianca, J., Grant, M. C., & Martin, K. (2006). An evaluation of the kernel equating method: A special study with pseudo‐tests constructed from real test data (ETS RR‐06–02). Princeton, NJ: Educational Testing Service.
12. Von Davier, A. A., Holland, P. W., & Thayer, D. T. (2003). Population invariance and chain versus poststratification methods for equating and test linking. In N. Dorans (Ed)., Population invariance of score linking: Theory and applications to Advanced Placement Program ® Examinations (ETS RR‐03–27). Princeton, NJ: Educational Testing Service.
13. Von Davier, A. A., Holland, P. W., & Thayer, D. T. (2004a). The kernel method of test equating. New York: Springer Verlag.
14. Von Davier, A. A., Holland, P. W., & Thayer, D. T. (2004b). The chain and poststratification methods for observed‐score equating: Their relationship to population invariance. Journal of Educational Measurement, 41, 15–32.
15. Von Davier, A. A., & Ricker, K. (2006, April). The role of the anchor test in a non‐equivalent group design. Paper presented at the meeting of the American Educational Research Association, San Francisco, CA.
16. Wang, T., Lee, W., Brennan, R. J., & Kolen, M. J. (2006, April). A comparison of the frequency estimation and chained equipercentile methods under the common‐item non‐equivalent groups design. Paper presented at the meeting of the National Council on Measurement in Education, San Francisco, CA.
17. Wright, N. K., & Dorans, N. J. (1993). Using the selection variable for matching or equating (ETS RR‐93–04). Princeton, NJ: Educational Testing Service.