Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-12T16:49:51.579Z Has data issue: false hasContentIssue false
  • English
  • Français

Traits versus Issues: Factor versus Ideal-Point Analysis of Candidate Thermometer Ratings

Published online by Cambridge University Press:  04 January 2017

Henry E. Brady
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

This article describes a computationally simple, statistically consistent, reasonably efficient, and statistically informative generalized least squares (GLS) estimator for a general class of nonlinear, multidimensional scaling (MDS) models including the “ideal-point” models of voters' and legislators' behavior proposed by Melvin Hinich, Keith Poole, and others. Unlike other methods, the method described in this article provides a statistical framework for testing a wide range of hypotheses about these models including their functional form, their dimensionality, and the values of specific parameters. The Hinich ideal-point model is estimated using this method. It fits the data remarkably well compared to a standard factor analysis model that does not provide a reasonable fit to the data. This has the substantive implication of suggesting that voters base their voting decisions upon ideal-point dimensions like liberalism-conservatism and not upon factor analysis dimensions like competence and leadership.

Information

Type
Research Article
Information
Political Analysis , Volume 2 , 1990, pp. 97 - 129
Copyright
Copyright © by the University of Michigan 1991 

References

Akaike, H. 1987. “Factor Analysis and AIC.” Psychometrika 52: 317–32.Google Scholar
Barone, Michael, Ujifusa, Grant, and Matthews, Douglas. 1975. The Almanac of American Politics 1976. New York: E. P. Dutton.Google Scholar
Billingsley, Patrick. 1979. Probability and Measure. New York: Wiley.Google Scholar
Brady, Henry E. 1985. “Statistical Consistency and Hypothesis Testing for Nonmetric Multidimensional Scaling.” Psychometrika 50: 509–37.CrossRefGoogle Scholar
Brady, Henry E. 1989a. “Factor and Ideal Point Analysis for Interpersonally Incomparable Data.” Psychometrika 54: 181–202.Google Scholar
Brady, Henry E. 1989b. “Multidimensional Scaling in Political Science.” Revision of a Paper presented at the annual Political Methodology Conference, Los Angeles.Google Scholar
Brady, Henry E. 1990. “Statistical Properties of Metric Multidimensional Scaling.” Paper presented at the annual meeting of the American Political Science Association, San Francisco.Google Scholar
Cahoon, L. S., Hinich, M. J., and Ordeshook, P. C. 1978. “A Statistical Multidimensional Scaling Method Based On the Spatial Theory of Voting.” In Graphical Representation of Multivariate Data, ed. Wang, P. J. New York: Academic Press.Google Scholar
Carmines, Edward G., and Mclver, John P. 1981. “Analyzing Models with Unobserved Variables: Analysis of Covariance Structures.” In Social Measurement: Current Issues, ed. Bohrnstedt, George and Borgatta, Edgar F. Beverly Hills: Sage.Google Scholar
Chiang, Chin Long. 1956. “On Regular Best Asymptotically Normal Estimates.” Annals of Mathematical Statistics 27: 336–51.Google Scholar
Cudek, R., and Browne, M. W. 1983. “Cross-Validation of Covariance Structures.” Multivariate Behavioral Research 18: 147–67.Google Scholar
Enelow, J., and Hinich, M. 1984. The Spatial Theory of Voting: An Introduction. Cambridge: Cambridge University Press.Google Scholar
Ferguson, Thomas S. 1958. “A Method of Generating Best Asymptotically Normal Estimates with Application to the Estimation of Bacterial Densities.” Annals of Mathematical Statistics 29: 1046–62.CrossRefGoogle Scholar
Fuller, Wayne. 1987. Measurement Error Models. New York: Wiley.Google Scholar
Gallant, A. R. 1987. Nonlinear Statistical Models. New York: Wiley.Google Scholar
Johnson, Mark E. 1987. Multivariate Statistical Simulation. New York: Wiley.CrossRefGoogle Scholar
Jöreskog, Karl G., and Sörbom, Dag. 1979. Advances in Factor Analysis and Structural Equation Models. Cambridge, Mass.: Abt Books.Google Scholar
Kruskal, Joseph B. 1964. “Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis.” Psychometrika 29: 1–27.Google Scholar
McDonald, Roderick P., and Marsh, Herbert W. 1989. “Choosing a Multivariate Model: Noncentrality and Goodness of Fit.” Paper presented at the annual meeting of the Psychometric Society, Los Angeles.Google Scholar
McFadden, D. 1989. “A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration.” Econometrica 57: 995–1026.Google Scholar
McKelvey, R. 1975. “Policy Related Voting and Electoral Equilibrium.” Econometrica 43: 815–43.Google Scholar
Page, Benjamin I., and Jones, Calvin C. 1979. “Reciprocal Effects of Policy Preferences, Party Loyalties, and the Vote.” American Political Science Review 73: 1071–89.Google Scholar
Pakes, Ariel, and Pollard, David. 1989. “Simulation and the Asymptotics of Optimization Estimators.” Econometrica 57: 1027–57.CrossRefGoogle Scholar
Poole, Keith T. 1981. “Dimensions of Interest Group Evaluation of the U.S. Senate, 1969-1978.” American Journal of Political Science 25: 49–67.Google Scholar
Poole, Keith T. 1984. “Least Squares Metric, Unidimensional Unfolding.” Psychometrika 49: 311–23.Google Scholar
Poole, Keith T., and Rosenthal, Howard. 1984. “U.S. Presidential Elections 1968-80: A Spatial Analysis.” American Journal of Political Science 28: 282–312.Google Scholar
Rabinowitz, George. 1976. “A Procedure for Ordering Object Pairs Consistent with the Multidimensional Unfolding Model.” Psychometrika 45: 349–73.Google Scholar
Rabinowitz, George. 1978. “On the Nature of Political Issues: Insights from a Spatial Analysis.” American Journal of Political Science 22: 793–817.Google Scholar
Shapiro, A. 1983. “Asymptotic Distribution Theory in the Analysis of Covariance Structures (a Unified Approach).” South African Statistical Journal 17: 33–81.Google Scholar
Shepard, Roger. 1962a. “Analysis of Proximities: Multidimensional Scaling With an Unknown Distance Function I.” Psychometrika 27: 125–40.Google Scholar
Shepard, Roger. 1962b. “Analysis of Proximities: Multidimensional Scaling With an Unknown Distance Function II.“ Psychometrika 27:219–46.Google Scholar
Weisberg, H. F. 1974. “Dimensionland: An Excursion Into Spaces.” American Journal of Political Science 18:743–76.Google Scholar
Weisberg, H. F., and Rusk, J. G. 1970. “Dimensions of Candidate Evaluation.” American Political Science Review 74:1167–85.Google Scholar