Abstract
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.
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References
Herrnstein, RJ., and Murray, C. (1994), The Bell Curve: Intelligence and Class Structure in American Life, The Free Press, New York.
Taubes, G. (1993), Bad Science: The Short Life and Wierd Times of Cold Fusion, Random House, New York.
Pearson, K. (1911), The Grammar of Science, A. and C.Black, London.
Blau, P., and Duncan, 0. (1967), Tbe American Occupational Structure, Wiley, New York.
Gould, S J. (1981), Tbe Mismeasure of Man, Norton, New York.
Suppes, P., and Zanotti, M. (1981), “When Are Probabilistic Explanations Possible?” Synthese, 48, 191–99.
Thurstone, L. (1935), The Vectors of Mind, The University of Chicago Press, Chicago, IL.
Thurstone, L. (1947), Multiple-FactorAnalysir a Development and Expansion of The Vectors of the Mind, The University of Chicago Press, Chicago, IL.
Hayduk, L. (1996), LISREL Issues, Debates, and Strategies,. Johns Hopkins Press, Baltimore.
Dawes, R. (1988), Rational Choice in an Uncertain World, Harcourt Brace Jovanovich, San Diego, CA.
Thomson, G. (1939), The Factorial Analysis of Human Ability, Houghton Mifflin, Boston.
Mosteller, F., and Tukey, J.W. (1977), Data Analysis and Regression, Addison-Wesley, Reading, MA.
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.
Junker,B.,and Ellis,J.L.(1996),A Characterization of Monotone Unidimensional Latent Variable Models,2/95(revised 3/96).http://www.stat.cmu.edu/brian/bjtrs.html
Madigan, D., Raftery, A.E., Volinsky, C.T., and Hoeting, J.A. (1996), Bayesian Model Averaging. AAAI Workshop on Integrating Multiple Learned Models. http://bayes.stat.washington.edu/papers.html
Heckerman, D. (1995), A Bayesian Approach to Learning Causal Networks, Technical Report MSR-TR-95-o4, Microsoft Research.http://www.research.mi-crosoft.com/research/dtg/heckerma/heckerma.html
Geiger, D.,Heckerman, D., and Meek, C. (1996), Asymptotic Model Selection for Directed Networks with Hidden Variables, preprint, Microsoft Research Center.
Pearl, J., and Verma, T (1990), A Formal Theory of Inductive Causation, Technical Report R-,55, Cognitive Systems Laboratory, Computer Science Department, UCLA.
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.
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.
Spirtes,P.,Glymour,C.,and Scheines, R.(1993),Causation, Prediction and Search, Springer Lecture Notes in Statistics. http://hss.cmu.edu/html/departments/philosophy/TETRAD.BOOK/book.html
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.
Richardson, T. (1996), Discovering Cyclic Causal Structure, Technical Report CMU Phil 68.
Pearl, J., and Dechter, R. (1996), Identifying Independencies in Causal Graphs with Feedback, Technical Report (R-243), Cognitive Science Laboratory, UCLA. http://singapore.cs.ucla.edu/csl-papers.html
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.
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.
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Glymour, C. (1997). Social Statistics and Genuine Inquiry: Reflections on The Bell Curve . In: Devlin, B., Fienberg, S.E., Resnick, D.P., Roeder, K. (eds) Intelligence, Genes, and Success. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0669-9_12
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DOI: https://doi.org/10.1007/978-1-4612-0669-9_12
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