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Nonequivalent Groups: Equipercentile Methods

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Part of the book series: Statistics for Social and Behavioral Sciences ((SSBS))

Abstract

In this chapter, we continue our discussion of equating with nonequivalent groups with a presentation of equipercentile methods. Including the use of smoothing.

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Notes

  1. 1.

    The basic idea is to find a linear transformation of observed scores to estimated true scores such that the estimates have a variance equal to true score variance.

  2. 2.

    Note that if \(\rho _1(V,V') = \rho _2(V,V')\), then \(v_1 = v_2 + [\mu _2(V) - \mu _1(V)]/\sqrt{\rho _1(V,V')}\).

  3. 3.

    The CIPE computer program and EQUATING RECIPES can be used for FE. In addition, EQUATING RECIPES provides results for MFE and chained equipercentile equating.

References

  • Angoff, W. H. (1971). Scales, norms, and equivalent scores. In R. L. Thorridike (Ed.), Educational measurement (2nd ed., pp. 508–600). Washington, DC: American Council on Education.

    Google Scholar 

  • Braun, H. I., & Holland, P. W. (1982). Observed-score test equating: A mathematical analysis of some ETS equating procedures. In P. W. Holland & D. B. Rubin (Eds.), Test equating (pp. 9–49). New York: Academic.

    Google Scholar 

  • Brennan, R. L., & Lee, W. (2006). Correcting for bias in single-administration decision consistency indexes. Iowa City, IA: Center for Advanced Studies in Measurement and Assessment, The University of Iowa. Available on http://www.education.uiowa.edu/centers/casma. (CASMA Research Report No. 18)

  • Chen, H., & Holland, P. W. (2010). New equating methods and their relationships with Levine observed score linear equating under the kernel equating framework. Psychometrika, 75, 542–557.

    Article  MATH  MathSciNet  Google Scholar 

  • Chen, H., Livingston, S. A., & Holland, P. W. (2011). Generalized equating functions for NEAT designs. In A. A. von Davier (Ed.), Statistical models for test equating, scaling, and linking (pp. 185–200). New York: Springer.

    Google Scholar 

  • Dorans, N. J. (1990). Equating methods and sampling designs. Applied Measurement in Education, 3, 3–17.

    Article  Google Scholar 

  • Draper, N. R., & Smith, H. (1998). Applied regression analysis (3rd ed.). New York: Wiley-Interscience.

    MATH  Google Scholar 

  • Hagge, S. L., & Kolen, M. J. (2011). Equating mixed-format tests with format representative and non-representative common items. In M. J. Kolen & W. Lee (Eds.), Mixed-format tests: Psychometric properties with a primary focus on equating (volume 1). (CASMA Monograph Number 2.1) (pp. 95–135). Iowa City, IA: CASMA, The University of Iowa.

    Google Scholar 

  • Hagge, S. L., & Kolen, M. J. (2012). Effects of group differences on equating using operational and pseudo-tests. In M. J. Kolen & W. Lee (Eds.), Mixed-format tests: Psychometric properties with a primary focus on equating (volume 2). (CASMA Monograph Number 2.2) (pp. 45–86). Iowa CIty, IA: CASMA, The University of Iowa.

    Google Scholar 

  • Hanson, B. A. (1991). A comparison of bivariate smoothing methods in common-item equipercentile equating. Applied Psychological Measurement, 15, 391–408.

    Article  Google Scholar 

  • Harris, D. J., & Kolen, M. J. (1990). A comparison of two equipercentile equating methods for common item equating. Educational and Psychological Measurement, 50, 61–71.

    Article  Google Scholar 

  • Holland, P. W., Sinharay, S., von Davier, A. A., & Han, N. (2008). An approach to evaluating the missing data assumptions of the chain and post-stratification equating methods for the NEAT design. Journal of Educational Measurement, 45, 17–43.

    Article  Google Scholar 

  • Holland, P. W., & Thayer, D. T. (1987). Notes on the use of log-linear models for fitting discrete probability distributions. Princeton, NJ: Educational Testing Service. (Technical Report 87–79)

    Google Scholar 

  • Holland, P. W., & Thayer, D. T. (1989). The kernel method of equating score distributions. Princeton, NJ: Educational Testing Service. (Technical Report 89–84)

    Google Scholar 

  • 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.

    Google Scholar 

  • Jarjoura, D., & Kolen, M. J. (1985). Standard errors of equipercentile equating for the common item nonequivalent populations design. Journal of Educational Statistics, 10, 143–160.

    Article  Google Scholar 

  • Karabatsos, G., & Walker, S. (2009). A Bayesian nonparametric approach to test equating. Psychometrika, 74, 211–232.

    Article  MATH  MathSciNet  Google Scholar 

  • Karabatsos, G., & Walker, S. (2011). A Bayesian nonparameteric model for test equating. In A. A. von Davier (Ed.), Statistical models for test equating, scaling, and linking (pp. 175–184). New York: Springer.

    Google Scholar 

  • Kolen, M. J., & Jarjoura, D. (1987). Analytic smoothing for equipercentile equating under the common item nonequivalent populations design. Psychometrika, 52, 43–59.

    Article  MathSciNet  Google Scholar 

  • Lee, W., He, Y., Hagge, S. L., Wang, W., & Kolen, M. J. (2012). Equating mixed-format tests using dichotomous common items. In M. J. Kolen & W. Lee (Eds.), Mixed-format tests: Psychometric properties with a primary focus on equating (volume 2). (CASMA Monograph Number 2.2) (pp. 13–44). Iowa CIty, IA: CASMA, The University of Iowa.

    Google Scholar 

  • Liou, M., & Cheng, P. E. (1995). Equipercentile equating via data-imputation techniques. Psychometrika, 60, 119–136.

    Article  MATH  Google Scholar 

  • Liu, C., & Kolen, M. J. (2011). A comparison among IRT equating methods and traditional equating methods for mixed-format tests. In M. J. Kolen & W. Lee (Eds.), Mixed-format tests: Psychometric properties with a primary focus on equating (volume 1). (CASMA Monograph Number 2.1) (pp. 75–94). Iowa City, IA: CASMA, The University of Iowa.

    Google Scholar 

  • Livingston, S. A. (1993). Small-sample equating with log-linear smoothing. Journal of Educational Measurement, 30, 23–39.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Livingston, S. A., & Feryok, N. J. (1987). Univariate vs. bivariate smoothing in frequency estimation equating. Princeton, NJ: Educational Testing Service. (Research Report 87–36)

    Google Scholar 

  • Lord, F. M. (1965). A strong true score theory with applications. Psychometrika, 30, 239–270.

    Article  Google Scholar 

  • Marco, G. L., Petersen, N. S., & Stewart, E. E. (1983). A test of the adequacy of curvilinear score equating models. In D. Weiss (Ed.), New horizons in testing (pp. 147–176). New York: Academic.

    Google Scholar 

  • Moses, T., & Holland, P. W. (2010a). The effects of selection strategies for bivariate loglinear smoothing models on NEAT equating functions. Journal of Educational Measurement, 47, 76–91.

    Article  Google Scholar 

  • Moses, T., & Holland, P. W. (2010b). A comparison of statistical selection strategies for univariate and bivariate log-linear models. British Journal of Mathematical and Statistical Psychology, 63, 557–574.

    Article  MathSciNet  Google Scholar 

  • Powers, S. J., Hagge, S. L., Wang, W., He, Y., Liu, C., & Kolen, M. J. (2011). Effects of group differences on mixed-format equating. In M. J. Kolen & W. Lee (Eds.), Mixed-format tests: Psychometric properties with a primary focus on equating (volume 1). (CASMA Monograph Number 2.1) (pp. 51–73). Iowa City, IA: CASMA, The University of Iowa.

    Google Scholar 

  • Powers, S. J., & Kolen, M. J. (2011). Evaluating equating accuracy and assumptions for groups that differ in performance. In M. J. Kolen & W. Lee (Eds.), Mixed-format tests: Psychometric properties with a primary focus on equating (volume 1). (CASMA Monograph Number 2.1) (pp. 137–175). Iowa City, IA: CASMA, The University of Iowa.

    Google Scholar 

  • Powers, S. J., & Kolen, M. J. (2012). Using matched samples equating methods to improve equating accuracy. In M. J. Kolen & W. Lee (Eds.), Mixed-format tests: Psychometric properties with a primary focus on equating (volume 2). (CASMA Monograph Number 2.2) (pp. 87–114). Iowa CIty, IA: CASMA, The University of Iowa.

    Google Scholar 

  • Rosenbaum, P. R., & Thayer, D. (1987). Smoothing the joint and marginal distributions of scored two-way contingency tables in test equating. British Journal of Mathematical and Statistical Psychology, 40, 43–49.

    Article  MATH  Google Scholar 

  • Sinharay, S. (2011). Chain equipercentile equating and frequency estimation equipercentile equating: Comparisons based on real and simulated data. In N. J. Dorans & S. Sinharay (Eds.), Looking Back: Proceedings of a Conference in Honor of Paul W. Holland. Lecture Notes in Statistics 202 (pp. 203–219). New York: Springer.

    Google Scholar 

  • Sinharay, S., & Holland, P. W. (2010a). The missing data assumptions of the NEAT design and their implications for test equating. Psychometrika, 75, 309–327.

    Article  MATH  MathSciNet  Google Scholar 

  • Sinharay, S., & Holland, P. W. (2010b). A new approach to comparing several equating methods in the context of the NEAT design. Journal of Educational Measurement, 47, 261–285.

    Article  Google Scholar 

  • Sinharay, S., Holland, P. W., & von Davier, A. A. (2011). Evaluating the missing data assumptions of the chain and poststratification equating methods. In A. A. von Davier (Ed.), Statistical models for test equating, scaling, and linking (pp. 281–296). New York: Springer.

    Google Scholar 

  • von Davier, A. A., Holland, P. W., & Thayer, D. T. (2004a). The kernel method of test equating. New York: Springer.

    MATH  Google Scholar 

  • von Davier, A. A., Holland, P. W., & Thayer, D. T. (2004b). The chain and post-stratification methods for observed-score equating: Their relationship to population invariance. Journal of Educational Measurement, 41, 15–32.

    Article  Google Scholar 

  • Wang, T., & Brennan, R. L. (2006). A modified frequency estimation equating method for the common-item non-equivalent groups design. Iowa City, IA: Center for Advanced Studies in Measurement and Assessment, The University of Iowa. Available on http://www.education.uiowa.edu/centers/casma (CASMA Research Report No. 19)

  • Wang, T., & Brennan, R. L. (2009). A modified frequency estimation equating method for the common-item non-equivalent groups design. Applied Psychological Measurement, 33, 118–132.

    Article  MathSciNet  Google Scholar 

  • Wang, T., Lee, W., Brennan, R. L., & Kolen, M. J. (2008). A comparison of the frequency estimation and chained equipercentile methods under the common-item non-equivalent groups design. Applied Psychological Measurement, 32, 632–651.

    Article  MathSciNet  Google Scholar 

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Correspondence to Michael J. Kolen .

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Kolen, M.J., Brennan, R.L. (2014). Nonequivalent Groups: Equipercentile Methods. In: Test Equating, Scaling, and Linking. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0317-7_5

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