Social Statistics and Genuine Inquiry: Reflections on The Bell Curve

  • Clark Glymour


The Bell Curve by Herrnstein and Murry put American academic social scientists in an uncomfortable place.1 The conclusions of the book are unwelcome, while the methods of the book appear to be the standbys of everyday social science. The unstated problem for many commentators is how to reject the particular conclusions of The Bell Curve without also rejecting the larger enterprises of statistical social science, psychometrics, and social psychology. In some sense, that is the general problem addressed in various ways in this collection of essays. The hard issues lurking behind the discussion are whether large parts of the social sciences and their methods are bogus, phony, pseudo-scientific, and whether, if and insofar as they are, they must be.


Causal Structure Prior Probability Distribution Substantive Knowledge Causal Information Bell Curve 
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  1. 1.
    Herrnstein, RJ., and Murray, C. (1994), The Bell Curve: Intelligence and Class Structure in American Life, The Free Press, New York.Google Scholar
  2. 2.
    Taubes, G. (1993), Bad Science: The Short Life and Wierd Times of Cold Fusion, Random House, New York.Google Scholar
  3. 3.
    Pearson, K. (1911), The Grammar of Science, A. and C.Black, London.Google Scholar
  4. 4.
    Blau, P., and Duncan, 0. (1967), Tbe American Occupational Structure, Wiley, New York.Google Scholar
  5. 5.
    Gould, S J. (1981), Tbe Mismeasure of Man, Norton, New York.Google Scholar
  6. 6.
    Suppes, P., and Zanotti, M. (1981), “When Are Probabilistic Explanations Possible?” Synthese, 48, 191–99.MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Thurstone, L. (1935), The Vectors of Mind, The University of Chicago Press, Chicago, IL.Google Scholar
  8. 8.
    Thurstone, L. (1947), Multiple-FactorAnalysir a Development and Expansion of The Vectors of the Mind, The University of Chicago Press, Chicago, IL.Google Scholar
  9. 9.
    Hayduk, L. (1996), LISREL Issues, Debates, and Strategies,. Johns Hopkins Press, Baltimore.Google Scholar
  10. 10.
    Dawes, R. (1988), Rational Choice in an Uncertain World, Harcourt Brace Jovanovich, San Diego, CA.Google Scholar
  11. 11.
    Thomson, G. (1939), The Factorial Analysis of Human Ability, Houghton Mifflin, Boston.Google Scholar
  12. 12.
    Mosteller, F., and Tukey, J.W. (1977), Data Analysis and Regression, Addison-Wesley, Reading, MA.Google Scholar
  13. 13.
    Spirtes, P., Meek, C., and Richardson, T (1996), “Causal Inference in the Presence of Latent Variables and Selection Bias,” P. Besnard and S. Hanks (Eds.), in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence,Morgan Kaufmann Publishers, San Francisco, CA, pp. 499–506.Google Scholar
  14. 14.
    Junker,B.,and Ellis,J.L.(1996),A Characterization of Monotone Unidimensional Latent Variable Models,2/95(revised 3/96). Google Scholar
  15. 15.
    Madigan, D., Raftery, A.E., Volinsky, C.T., and Hoeting, J.A. (1996), Bayesian Model Averaging. AAAI Workshop on Integrating Multiple Learned Models. Google Scholar
  16. 16.
    Heckerman, D. (1995), A Bayesian Approach to Learning Causal Networks, Technical Report MSR-TR-95-o4, Microsoft Research. Google Scholar
  17. 17.
    Geiger, D.,Heckerman, D., and Meek, C. (1996), Asymptotic Model Selection for Directed Networks with Hidden Variables, preprint, Microsoft Research Center.Google Scholar
  18. 18.
    Pearl, J., and Verma, T (1990), A Formal Theory of Inductive Causation, Technical Report R-,55, Cognitive Systems Laboratory, Computer Science Department, UCLA.Google Scholar
  19. 19.
    Pearl, J., and Verma, T (1991), “A Theory of Inferred Causation,” Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, Morgan Kaufmann, San Mateo, CA.Google Scholar
  20. 20.
    Spirtes, P., Glymour, C. and Scheines, R. (1990) “Causality From Probability,” J. Tiles et al. [Eds.], Evolving Knowledge in Natural Science and Artificial Intelligence, Pitman, London, pp. 181–199.Google Scholar
  21. 21.
    Spirtes,P.,Glymour,C.,and Scheines, R.(1993),Causation, Prediction and Search, Springer Lecture Notes in Statistics. Google Scholar
  22. 22.
    Spines, P. (1996), Discovering Causal Relations Among Latent Variables in Directed Acyclic Graphical Models, Technical report, CMU-Phil-69, Department of Philosophy, Carnegie Mellon University.Google Scholar
  23. 23.
    Richardson, T. (1996), Discovering Cyclic Causal Structure, Technical Report CMU Phil 68.Google Scholar
  24. 24.
    Pearl, J., and Dechter, R. (1996), Identifying Independencies in Causal Graphs with Feedback, Technical Report (R-243), Cognitive Science Laboratory, UCLA. Google Scholar
  25. 25.
    Scheines, R. (1994), “Inferring Causal Structure Among Unmeasured Variables,” in Proceedings of the Fourth International Workshop on Statistics and AI, Springer-Verlag, Ft. Lauderdale, FL.Google Scholar
  26. 26.
    Druzdzel, M., and Glymour, C. (1994), “Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges,” in Working Notes of the AAAI-94 Workshop on Knowledge Discovery in Databases (KDD-94), Seattle, WA, pp. 429–430.Google Scholar

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© Springer Science+Business Media New York 1997

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  • Clark Glymour

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