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Tail-measurability in monotone latent variable models

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Abstract

We consider latent variable models for an infinite sequence (or universe) of manifest (observable) variables that may be discrete, continuous or some combination of these. The main theorem is a general characterization by empirical conditions of when it is possible to construct latent variable models that satisfy unidimensionality, monotonicity, conditional independence, andtail-measurability. Tail-measurability means that the latent variable can be estimated consistently from the sequence of manifest variables even though an arbitrary finite subsequence has been removed. The characterizing,necessary and sufficient, conditions that the manifest variables must satisfy for these models are conditional association and vanishing conditional dependence (as one conditions upon successively more other manifest variables). Our main theorem considerably generalizes and sharpens earlier results of Ellis and van den Wollenberg (1993), Holland and Rosenbaum (1986), and Junker (1993). It is also related to the work of Stout (1990).

The main theorem is preceded by many results for latent variable modelsin general—not necessarily unidimensional and monotone. They pertain to the uniqueness of latent variables and are connected with the conditional independence theorem of Suppes and Zanotti (1981). We discuss new definitions of the concepts of “true-score” and “subpopulation,” which generalize these notions from the “stochastic subject,” “random sampling,” and “domain sampling” formulations of latent variable models (e.g., Holland, 1990; Lord & Novick, 1968). These definitions do not require the a priori specification of a latent variable model.

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References

  • Andrich, D. (1978). A rating formulation for ordered response categories.Psychometrika, 43, 561–573.

    Google Scholar 

  • Billingsley, P. (1986).Probability and measure. New York: Wiley.

    Google Scholar 

  • Byrne, B. M. (1989). Multigroup comparisons and the assessment of equivalent construct validity across groups: Methodological and substantive issues.Multivariate Behavioral Research, 24, 503–523.

    Google Scholar 

  • Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972).The dependability of behavioral measurements: Theory of generalizability for scores and profiles. New York: Wiley.

    Google Scholar 

  • Ellis, J. L. (1993). Subpopulation invariance of patterns in covariance matrices.British Journal of Mathematical and Statistical Psychology, 46, 231–254.

    Google Scholar 

  • Ellis, J. L., & van den Wollenberg, A. L. (1993). Local homogeneity in latent trait models. A characterization of the homogeneous monotone IRT model.Psychometrika, 58, 417–429.

    Google Scholar 

  • Esary, J. D., Proschan, F., & Walkup, D. W. (1967). Association of random variables, with applications.Annals of Mathematical Statistics, 38, 1466–1474.

    Google Scholar 

  • Fischer, G. H. (1974).Einfuhrung in die Theorie psychologischer Tests [Introduction to mental test theory]. Bern: Huber.

    Google Scholar 

  • Fischer, G. H. (1987). Applying the principles of specific objectivity and of generalizability to the measurement of change.Psychometrika, 52, 565–587.

    Google Scholar 

  • Fischer, G. H. (1995). Derivations of the Rasch model. In G. H. Fischer & I. W. Molenaar, (Eds.),Rasch models. Foundations, recent developments and applications (pp. 15–38). New York: Springer-Verlag.

    Google Scholar 

  • Grayson, D. A. (1988). Two-group classification in latent trait theory: Scores with monotone likelihood ratio.Psychometrika, 53, 383–392.

    Google Scholar 

  • Grayson, D. A. (1990). Selection effects and unidimensionality in item response theory.British Journal of Mathematical and Statistical Psychology, 43, 207–216.

    Google Scholar 

  • Guttman, L. (1945). A basis for analyzing test-retest reliability.Psychometrika, 10, 255–282.

    Google Scholar 

  • Guttman, L. (1953). Image theory for the structure of quantitative variates.Psychometrika, 18, 227–296.

    Google Scholar 

  • Holland, P. W. (1981). When are item response models consistent with observed data?Psychometrika, 46, 79–92.

    Google Scholar 

  • Holland, P. W. (1990). On the sampling theory foundations of item response theory models.Psychometrika, 55, 577–601.

    Google Scholar 

  • Holland, P. W., & Rosenbaum, P. R. (1986). Conditional association and unidimensionality in monotonc latent variable models.The Annals of Statistics, 14, 1523–1543.

    Google Scholar 

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

    Google Scholar 

  • Junker, B. W. (1993). Conditional association, essential independence, and monotone unidimensional item response models.Annals of Statistics, 21, 1359–1378.

    Google Scholar 

  • Junker, B. W., & Ellis, J. L. (1997).A characterization of monotone unidimensional latent variable models.Annals of Statistics, 25, 1327–1343.

    Google Scholar 

  • Kaiser, H., & Caffrey, J. (1965). Alpha factor analysis.Psychometrika, 30, 1–14.

    Google Scholar 

  • Kingman, J. F. C. (1978). Uses of exchangeability.Annals of Probability, 6, 183–197.

    Google Scholar 

  • Lazarsfeld, P. F. (1959). Latent structure analysis. In S. Koch (Ed.),Psychology: A study of science, Vol. 3 (pp. 476–543). New York: McGraw Hill.

    Google Scholar 

  • Lazarsfeld, P. F., & Henry, N. W. (1968).Latent structure analysis. Boston: Houghton Mifflin.

    Google Scholar 

  • Lehmann, E. L. (1986).Testing statistical hypotheses. New York: Wiley.

    Google Scholar 

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

    Google Scholar 

  • Lord, F. M., & Novick, M. R. (1968).Statistical theories of mental test scores. Reading, MA: Addison-Wesley.

    Google Scholar 

  • McDonald, R. P. (1977). The indeterminacy of components and the definition of common factors.British Journal of Mathematical and Statistical Psychology, 30, 165–176.

    Google Scholar 

  • McDonald, R. P. (1981). The dimensionality of tests and items.British Journal of Mathematical and Statistical Psychology, 34, 100–117.

    Google Scholar 

  • McDonald, R. P., & Mulaik, S. A. (1979). Determinacy of common factors: A nontechnical review.Psychological Bulletin, 86, 297–306.

    Google Scholar 

  • Mellenbergh, G. J. (1989). Item bias and item response theory.International Journal of Educational Research, 13, 127–143.

    Google Scholar 

  • Meredith, W., & Millsap, R. E. (1992). On the misuse of manifest variables in the detection of measurement bias.Psychometrika, 57, 289–311.

    Google Scholar 

  • Mokken, R. J. (1971).A theory and procedure of scale-analysis. The Hague: Mouton.

    Google Scholar 

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

    Google Scholar 

  • Mulaik, S. A., & McDonald, R. P. (1978). The effect of additional variables on factor indeterminacy in models with a single common factor.Psychometrika, 43, 177–192.

    Google Scholar 

  • Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation.Psychometrika, 56, 611–630.

    Google Scholar 

  • Rasch, G. (1960).Probabilistic models for some intelligence and attainment tests. Copenhagen: Nielson & Lydiche.

    Google Scholar 

  • Rasch, G. (1977). On specific objectivity: An attempt at formalizing the request for generality and validity of scientific statements. In M. Blegvad (Ed.),The Danish yearbook of philosophy. Copenhagen: Munksgaard.

    Google Scholar 

  • Rosenbaum, P. R. (1984). Testing the conditional independence and monotonicity assumptions of item response theory.Psychometrika, 49, 425–435.

    Google Scholar 

  • Roskam, E. E., & Jansen, P. G. W. (1984). A new derivation of the Rasch model. In E. Degreef & J. Van Buggenhaut (Eds),Trends in mathematical psychology. Amsterdam: Elsevier.

    Google Scholar 

  • Rozenboom, W. W. (1988). Factor indeterminacy: The saga continues.British Journal of Mathematical and Statistical Psychology, 41, 209–226.

    Google Scholar 

  • Schervish, M. J. (1995).Theory of statistics. New York: Springer-Verlag.

    Google Scholar 

  • Shepard, R. N., & Metzler, J. (1971). Mental rotation of three-dimensional objects.Science, 171, 701–703.

    Google Scholar 

  • Sijtsma, K. (1988).Contributions to Mokken's nonparametric item response theory. Amsterdam: Free University Press.

    Google Scholar 

  • Steiger, J. H. (1979). Factor indeterminacy in the 1930's and the 1970's some interesting parallels.Psychometrika, 44, 157–167.

    Google Scholar 

  • Stout, W. (1990). A new item response theory modeling approach with applications to unidimensionality assessment and ability estimation.Psychometrika, 55, 293–325.

    Google Scholar 

  • Suppes, P., & Zanotti, M. (1981). When are probabilistic explanations possible?Synthese, 48, 191–199.

    Google Scholar 

  • Thurstone, L. L. (1957).Multiple-factor analysis. Chicago: University of Chicago Press.

    Google Scholar 

  • Tucker, L. R. (1966). Some mathematical notes on three-mode factor analysis.Psychometrika, 31, 279–311.

    Google Scholar 

  • Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory.Psychometrika, 54, 427–250.

    Google Scholar 

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The authors made equivalent contributions to the results of this article. Ellis' research was supported by the Dutch Interuniversitary Graduate School of Psychometrics and Sociometrics. Junker's research was supported by ONR Grant N00014-87-K-0277, NIMH Grant MH15758, and a Carnegie Mellon University Faculty Development Grant. In addition Junker would like to acknowledge the hospitality of the Nijmegen Institute for Cognition and Information during his visit to the University of Nijmegen in August 5–10, 1993.

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Ellis, J.L., Junker, B.W. Tail-measurability in monotone latent variable models. Psychometrika 62, 495–523 (1997). https://doi.org/10.1007/BF02294640

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