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The Linkage between Constituency Attitudes and Congressional Voting Behavior: A Causal Model

Published online by Cambridge University Press:  01 August 2014

Charles F. Cnudde  and
Donald J. McCrone
Charles F. Cnudde
Affiliation:
University of North Carolina
Donald J. McCrone
Affiliation:
University of Wisconsin
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Extract

Warren E. Miller and Donald E. Stokes' publication in 1963 of a preliminary report on the Survey Research Center's representation study is an important landmark in the development of empirical political theory. That report addressed itself to the crucial theoretical question of the linkage between mass political opinions and governmental policy-making. More specifically, the report found considerable policy agreement between Congressional roll call votes and the attitudes of the individual Congressman's constituency. This policy agreement was then interpreted through several causal paths and the Congressman's perception of his constituency's attitudes was found to be the main path by which the local district ultimately influenced Congressional outputs.

The main body of the report dealt with the broad civil rights issue dimension, and, by specifying the perceptual path by which constituency influence is brought to bear, documented the effect of political issues despite the generally low level of political information held at the mass level. Thus, the Congressmen, through their broad cognitive evaluations, were aware of how far they could proceed in determining their civil rights roll call votes on the basis of their own attitudes before risking the displeasure of their constituents.

Beyond such major substantive contributions the representation study introduced to political science a variance-apportioning technique similar to that developed by Sewall Wright, in 1921. Through this variance-apportioning technique, the importance of the perceptual link was isolated and evaluated. This study, then, symbolizes the growing recognition in political science of the importance of more sophisticated methodological tools in the process of theory building.

Type
Research Article
Information
American Political Science Review , Volume 60 , Issue 1 , March 1966 , pp. 66 - 72
Copyright
Copyright © American Political Science Association 1966

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References

* The authors wish to express their gratitude to Warren E. Miller and Donald E. Stokes for providing the data upon which this analysis is based. We gratefully acknowledge the invaluable assistance of Hubert M. Blalock. Jr. and James W. Prothro. The authors, of course, are solely responsible for the analysis.

1 Miller, Warren E. and Stokes, Donald E., “Constituency Influence in Congress,” this Review, 57 (1963), 45–56Google Scholar.

2 For other material on the linkage problem, see especially Key, V. O. Jr., Public Opinion and American Democracy (New York: Alfred A. Knopf, 1961), pp. 441–531Google Scholar.

3 Briefly, the representation study interrelates three types of data collected in 1958. First, a mass survey was conducted according to probability sampling methods. From this survey central tendencies on attitudinal dimensions were computed for 116 congressional districts. Second, interviews were conducted with incumbent and non-incumbent candidates running for the House of Representatives from these constituencies. The third set of data consisted of Guttman scales of roll call votes taken in Congress on civil rights, social welfare and foreign policy. For a fuller description of the study, see Miller, Warren E. and Stokes, Donald E., Representation in the American Congress (Englewood Cliffs, N. J.: Prentice-Hall, in press)Google Scholar.

4 Wright, Sewall, “Correlation and Causation,” Journal of Agricultural Research, 20 (1921), 557–585Google Scholar.

5 Miller and Stokes, “Constituency Influence in Congress,” op. cit., pp. 50–51.

6 Ibid., p. 53.

7 Loc. cit.

8 Blalock, Hubert M. Jr., Causal Inferences in Nonexperimental Research (Chapel Hill: The University of North Carolina Press, 1964), p. 62Google Scholar. Also see Simon, Herbert A., “Spurious Correlations: A Causal Interpretation,” Journal of the American Statistical Association, 49 (1954), 467–479Google Scholar.

9 In the three-variable model, for example, if we predict that the relationship between B and C is spurious because they are both dependent variables of A, the Simon-Blalock method predicts that the product of the correlations between A and C, and A and B will equal the correlation between C and B. Thus: rCB = rAC rAB.

10 At the very least, then, this method allows us to eliminate logically possible alternative models from our store of explanations. For the utility of regression coefficients, see Blalock, op. cit., pp. 85–87.

11 For a recent application of the Simon technique, see Beyle, Thad L., “Contested Elections and Voter Turnout in A Local Community: A Problem in Spurious Correlation,” this Review, 59 (03, 1965), 111–117Google Scholar.

12 Miller and Stokes, “Constituency Influence in Congress,” op. cit., pp. 52–53.

13 If B is an intervening variable between A and C, then the product of the amount of variation A explains in B, and B explains in C, gives the proportion of the relationship between A and C that is accounted for by the path from A. to B to C. Thus: the proportion of A to C explained by A to B to C = (r 2AB × r 2BC)/r 2AC.

14 Miller and Stokes, “Constituency Influence in Congress,” op. cit, p. 53.

15 It is not necessary to make prediction equations for the relationships between A, P, and R in the latter half of the model for their intercorrelations remain the same. Only the correlations between D and other variables are affected.

16 The degree to which these substantive findings (and many others) apply to other issue dimensions is the subject of Miller and Stokes' forthcoming book on representation. Representation in the American Congress, op. cit.