Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T05:17:46.430Z Has data issue: false hasContentIssue false
  • English
  • Français

What Went Wrong? Reflections on Science by Observation and The Bell Curve

Published online by Cambridge University Press:  01 April 2022

Clark Glymour
Clark Glymour*
Affiliation:
Departments of Philosophy, University of California-San Diego, Carnegie Mellon University
Get access Rights & Permissions [Opens in a new window]

Abstract

The Bell Curve aims to establish a set of causal claims. I argue that the methodology of The Bell Curve is typical of much of contemporary social science and is intrinsically defective. I claim better methods are available for causal inference from observational data, but that those methods would yield no causal conclusions from the data used in the formal analyses in The Bell Curve. Against the laissez-faire social policies advocated in the book, I claim that when combined with common sense and other information, the informal data mustered in The Bell Curve support a range of “liberal” social policies.

Type
Research Article
Information
Philosophy of Science , Volume 65 , Issue 1 , March 1998 , pp. 1 - 32
Copyright
Copyright © Philosophy of Science Association 1998

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

Send reprint requests to the author, Department of Philosophy, University of California-San Diego, La Jolla, CA 92093

References

Akleman, Derya G., Bessler, David A., and Burton, Diana M. (preprint), “Modeling Corn Experts and Exchange Rates with Directed Graphs”, Texas A&M University, Department of Economics, February 1997.Google Scholar
Blau, Peter M. and Duncan, Otis Dudley (1967), The American Occupational Structure. New York: Wiley.Google Scholar
Cooper, Gregory F. (1995), “Causal Discovery from Data in the Presence of Selection Bias”, Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and Statistics, Fort Lauderdale, FL, pp. 140–150.Google Scholar
Druzdzel, Marek J., and Glymour, Clark (1994), “Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges”, Technical report, American Association for Artificial Intelligence. Menlo Park, CA: AAAI Press, pp. 419–430.Google Scholar
Fienberg, Steven et al. (forthcoming) Chance.Google Scholar
Fisher, Ronald A. (1958), The Genetical Theory of Natural Selection. New York: Dover.Google Scholar
Fraser, Steven (ed.) (1995), The Bell Curve Wars. New York: Basic Books.Google Scholar
Glymour, Clark (1980), Theory and Evidence. Princeton: Princeton University Press.Google Scholar
Glymour, Clark, Scheines, Richard, Spirtes, Peter, and Kelly, Kevin (1987), Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling. Orlando: Academic Press.Google Scholar
Hernstein, Richard J. and Murray, Charles (1994), The Bell Curve: Intelligence and Class Structure in American Life. New York: Free Press.Google Scholar
Holland, Paul (1986), “Statistics and Causal Inference”, Journal of the American Statistical Association 81: 945–960.CrossRefGoogle Scholar
Jones, Ll. Wynn and Spearman, Charles (1950), Human Ability, A Continuation of the “Abilities of Man”. London: Macmillan.Google Scholar
Joreskog, Karl and Sorbom, Dag (1990), “Model Search with Tetrad II and LISREL”, Sociological Methods and Research 19: 93–106.CrossRefGoogle Scholar
Junker, Brian W. and Ellis, Seymour (preprint), “A Characterization of Monotone Unidimensional Latent Variables”, Department of Statistics, Carnegie Mellon University, 1995.Google Scholar
Kiiveri, Harry and Speed, Terry (1982), “Structural Analysis of Multivariate Data: A Review”, in Leinhardt, Samuel (ed.), Sociological Methodology. San Francisco: Jossey-Boss.Google Scholar
Kohn, Melvin L. (1967), Class and Conformity: a Study of Values. Homewood, Ill.: Dorsey Press.Google Scholar
Murray, Charles (1984), Losing Ground: American Social Policy 1950–1980. New York: Basic Books.Google Scholar
Pearl, Judea (1988), Probabilistic Reasoning Systems: Networks of Plausible Inference. San Mateo: Morgan Kaufman.Google Scholar
Shipley, Bill (1995), “Structured Interspecific Determinants of Specific Leaf Area in 34 Species of Herbaceous Angiosperms”, Functional Ecology 9: 312–319.CrossRefGoogle Scholar
Shipley, Bill (1997), “Exploratory Path Analysis with Applications in Ecology and Evolution”, The American Naturalist 149: 1113–1138.CrossRefGoogle ScholarPubMed
Shipley, Bill and McKenna, M. F. (in review; submitted to Functional Ecology) “Components of Interspecific Variation in the Relative Growth Rate Between 28 Species of Herbaceous Angiosperms. Part II: Modeling the Determinants of RGR”.Google Scholar
Shipley, Bill and Lechowicz, M. J. (in review; submitted to Ecology), “Variation in the Functional Coordination of Leaf Morphology and Gas Exchange in 40 Wetland Species”.Google Scholar
Spearman, Charles (1904), “General Intelligence Objectively Determined and Measured”, American Journal of Psychology 10: 151–293.Google Scholar
Spirtes, Peter (1993), “Directed Cyclic Graphical Representations of Feedback Models”, Proceedings of the 1995 Conference on Uncertainty in Artificial Intelligence. San Mateo: Morgan Kaufman, 491–498.Google Scholar
Spirtes, Peter, Glymour, Clark, and Scheines, Richard (1993), Causation, Prediction and Search. New York: Springer-Verlag.CrossRefGoogle Scholar
Spirtes, Peter, Meek, Christopher, and Richardson, Thomas (1995), “Causal Inference in the Presence of Latent Variables and Selection Bias”, Proceedings of the 1995 Conference on Uncertainty and Artificial Intelligence. San Mareo: Morgan Kaufman, 499–506.Google Scholar
Thurstone, Louis L. (1947), Multiple-factor Analysis; A Development and Expansion of the Vectors of the Mind. Chicago: University of Chicago Press.Google Scholar