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A Boundary Mixture Approach to Violations of Conditional Independence

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Abstract

Conditional independence is a fundamental principle in latent variable modeling and item response theory. Violations of this principle, commonly known as local item dependencies, are put in a test information perspective, and sharp bounds on these violations are defined. A modeling approach is proposed that makes use of a mixture representation of these boundaries to account for the local dependence problem by finding a balance between independence on the one side and absolute dependence on the other side. In contrast to alternative approaches, the nature of the proposed boundary mixture model does not necessitate a change in formulation of the typical item characteristic curves used in item response theory. This has attractive interpretational advantages and may be useful for general test construction purposes.

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References

  • Ashford, J.R., & Sowden, R.R. (1970). Multivariate probit analysis. Biometrics, 26, 535–546.

    Article  PubMed  Google Scholar 

  • Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee’s ability. In F.M. Lord & M.R. Novick (Eds.), Statistical theories of mental test scores (pp. 397–497). Reading: Addison-Wesley.

    Google Scholar 

  • Braeken, J., & Tuerlinckx, F. (2009). A mixed model framework for teratology studies. Biostatistics, 10, 744–755.

    Article  PubMed  Google Scholar 

  • Braeken, J., Tuerlinckx, F., & De Boeck, P. (2007). Copulas for residual dependency. Psychometrika, 72, 393–411.

    Article  Google Scholar 

  • Chen, W., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22, 265–289.

    Google Scholar 

  • Cureton, E.E. (1959). Note on φ/φ max . Psychometrika, 24, 89–91.

    Article  Google Scholar 

  • Ferrara, S., Huynh, H., & Michaels, H. (1999). Contextual explanations of local dependence in item clusters in a large-scale hands-on science performance assessment. Journal of Educational Measurement, 36, 119–140.

    Article  Google Scholar 

  • Fréchet, M. (1951). Sur les tableaux de corrélation dont les marges sont données. Annales de l’Université Lyon: Série 3, 14, 53–77.

    Google Scholar 

  • Gibbons, R.D., & Hedeker, D.R. (1992). Full-information item bi-factor analysis. Psychometrika, 57, 423–436.

    Article  Google Scholar 

  • Hoeffding, W. (1940). Masstabinvariante Korrelations Theorie. Schriften des Matematischen Instituts und des Instituts für angewandte Mathematik der Universität Berlin, 5, 179–223. [Reprinted as Scale-invariant correlation theory in the Collected Works of Wassily Hoeffding, N.I. Fischer, and P.K. Sen (Eds.), New York: Springer.]

    Google Scholar 

  • Hoskens, M., & De Boeck, P. (1997). A parametric model for local item dependencies among test items. Psychological Methods, 2, 261–277.

    Article  Google Scholar 

  • Ip, E. (2001). Testing for local dependence in dichotomous and polutomous item response models. Psychometrika, 66, 109–132.

    Article  Google Scholar 

  • Joe, H. (1997). Multivariate models and dependence concepts. London: Chapman & Hall.

    Google Scholar 

  • Junker, B.W. (1991). Essential independence and likelihood-based ability estimation for polytomous items. Psychometrika, 56, 255–278.

    Article  Google Scholar 

  • Lazarsfeld, P.F. (1950). The logical and mathematical foundation of latent structure analysis & the interpretation and mathematical foundation of latent structure analysis. In S.A. Stouffer, L. Guttman, E.A. Suchman, P.F. Lazarsfeld, S.A. Star, & J.A. Claussen (Eds.), Measurement and prediction (pp. 7–56). Princeton University Press: Thousand Oaks.

    Google Scholar 

  • Lord, F.M. (1980). Applications of item response theory to practical testing problems. Mahwah: Erlbaum.

    Google Scholar 

  • MacCallum, R. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100, 107–120.

    Article  Google Scholar 

  • Masters, G.N. (1988). Item discrimination: when more is worse. Journal of Educational Measurement, 25, 15–29.

    Article  Google Scholar 

  • Mood, A.M., Graybill, F.A., & Boes, D.C. (1974). Introduction to the theory of statistics. New York: McGraw-Hill.

    Google Scholar 

  • Nelsen, R.B. (1998). An introduction to copulas. New York: Springer.

    Google Scholar 

  • Salhi, S. (1998). Heuristic search methods. In G.A. Marcoulides (Ed.), Modern methods for business research (pp. 147–175). Mahwah: Lawrence Erlbaum.

    Google Scholar 

  • Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 7.

  • Samejima, F. (1972). A general model for free-response data. Psychometrika Monograph Supplement, 18.

  • Shaffer, J.P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561–584.

    Article  Google Scholar 

  • Sireci, S.G., Thissen, D., & Wainer, H. (1991). On the reliability of testlet-based tests. Journal of Educational Measurement, 28, 237–247.

    Article  Google Scholar 

  • Sklar, A. (1959). Fonctions de répartition à n dimension et leurs marges. Publications Statistiques Université de Paris, 8, 229–231.

    Google Scholar 

  • Steiger, J.H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180.

    Article  Google Scholar 

  • Tate, R. (2003). A comparison of selected empirical methods for assessing the structure of responses to test items. Applied Psychological Measurement, 27, 159–203.

    Article  Google Scholar 

  • Tuerlinckx, F., & De Boeck, P. (2001). Non-modeled item interactions lead to distorted discrimination parameters: A case study. Methods of Psychological Research, 6. [Retrieved May 20, 2005 from http://www.mpr-online.de/issue14/art3/Tuerlinckx.pdf.

  • Verhelst, N.D., & Glas, C.A.W. (1993). A dynamic generalization of the Rasch model. Psychometrika, 58, 395–415.

    Article  Google Scholar 

  • Wainer, H., Bradlow, E., & Wang, X. (2007). Testlet response theory and its applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Yen, W.M. (1993). Scaling performance assessments: Strategies for managing local item dependence. Journal of Educational Measurement, 30, 187–213.

    Article  Google Scholar 

  • Zeger, S.L., Liang, K.-Y., & Albert, P.S. (1988). Models for longitudinal data: A generalized estimation equation approach. Biometrics, 44, 1049–1060.

    Article  PubMed  Google Scholar 

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Correspondence to Johan Braeken.

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Braeken, J. A Boundary Mixture Approach to Violations of Conditional Independence. Psychometrika 76, 57–76 (2011). https://doi.org/10.1007/s11336-010-9190-4

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