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Methodological Artifacts in Measures of Political Efficacy and Trust: A Multiple Correspondence Analysis

Published online by Cambridge University Press:  04 January 2017

Jörg Blasius  and
Victor Thiessen
Jörg Blasius
Affiliation:
University of Cologne, Zentralarchiv für Empirische Sozialforschung, Bachemer Str. 40, 50931 Köln, Germany. e-mail: blasius@za.uni-koeln.de
Victor Thiessen
Affiliation:
Department of Sociology and Social Anthropology, Halifax, Dalhousie University, Nova Scotia, Canada. e-mail: victor.thiessen@dal.ca
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Abstract

Many authors report a positive relationship of education and political interest with political efficacy and trust, but it is well known that both of the former are associated with response styles, such as a tendency to “strongly agree.” Since they are related to both a substantive concept (political efficacy and trust), and to methodological effects (agreement bias and a tendency to give non-substantive responses) it is important to assess whether the substantive relationship is due to methodological artifacts. Applying multiple correspondence analysis to the 1984 Canadian National Election Study, we will discuss a method which allows to test a set of items for measurement effects such as ordinality and response sets. In the given example, ordinality of the political efficacy and trust items could be confirmed only for politically interested respondents. For respondents with low political interest, there is clear evidence of a response set resulting in a tendency to “strongly agree” regardless of the direction of the items. Taken together, these findings call into question the substantive relationships reported in the literature.

Type
Research Article
Information
Political Analysis , Volume 9 , Issue 1 , 2001 , pp. 1 - 20
Copyright
Copyright © 2001 by the Society for Political Methodology 

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References

Balch, George I. 1974. “Multiple Indicators in Survey Research: The Concept ‘Sense of Political Efficacy.’” Political Methodology 1: 1–43.Google Scholar
Benzécri, Jean-Paul, et al. 1973. L'Analyse des Données. Paris: Dunod.Google Scholar
Blasius, Jörg. 1994. “Correspondence Analysis in Social Science Research.” In Correspondence Analysis in the Social Sciences. Recent Developments and Applications, eds. Greenacre, Michael and Blasius, Jörg. London: Academic Press, pp. 23–52.Google Scholar
Campbell, Angus, Gurin, Gerald, and Miller, Warren E. 1954. The Voter Decides. Evanston, IL: Row, Peterson.Google Scholar
Converse, Jean M. 1976. “Predicting No Opinion in the Polls.” Public Opinion Quarterly 40: 515–530.CrossRefGoogle Scholar
Craig, Stephen C., and Maggiotto, Michael A. 1982. “Measuring Political Efficacy.” Political Methodology 8: 85–109.Google Scholar
De Leeuw, Jan. 1998. “Here's Looking at Multivaribles.” In Visualization of Categorical Data, eds. Blasius, Jörg and Greenacre, Michael J. San Diego: Academic Press, pp. 1–11.Google Scholar
Eckart, Carl, and Young, Gale. 1936. “The Approximation of One Matrix by Another of Lower Rank.” Psychometrika 1: 211–218.Google Scholar
Faulkenberry, G. David, and Mason, Robert. 1978. “Characteristics of Nonopinion and No Opinion Response Groups.” Public Opinion Quarterly 42: 533–543.Google Scholar
Ferligoj, A., Ule, M., and Rener, T. 1991. “Sex Differences in ‘Don't Know’ Rate: The Case of Slovenia.” Wisdom 1/2: 1–21.Google Scholar
Finkel, Steven E. 1985. “Reciprocal Effects of Participation and Political Efficacy.” American Journal of Political Science 29: 891–913.Google Scholar
Francis, Joe, and Busch, Lawrence. 1975. “What We Don't Know About ‘I Don't Know's.” Public Opinion Quarterly 39: 207–218.Google Scholar
Gifi, Albert. 1990. Nonlinear Multivariate Analysis. Chichester: John Wiley.Google Scholar
Goodman, Leo A. 1986. “Some Useful Extensions in the Usual Correspondence Analysis Approach in the Analysis of Contingency Tables (with Discussion).” International Statistical Review 54: 243–270.Google Scholar
Goodman, Leo A. 1991. “Measures, Models, and Graphical Display in the Analysis of Cross-Classified Data (with Discussion).” Journal of the American Statistical Association 86: 1085–1138.Google Scholar
Greenacre, Michael J. 1984. Theory and Applications of Correspondence Analysis. London: Academic Press.Google Scholar
Greenacre, Michael J. 1988. “Correspondence Analysis of Multivariate Categorical Data by Weighted Least Squares.” Biometrika 75: 457–467.Google Scholar
Greenacre, Michael J. 1990. “Some Limitations of Multiple Correspondence Analysis.” Computational Statistics Quarterly 3: 249–256.Google Scholar
Greenacre, Michael J. 1991. “Interpreting Multiple Correspondence Analysis.” Applied Stochastic Models and Data Analysis 7: 195–210.Google Scholar
Greenacre, Michael J. 1993. Correspondence Analysis in Practice. London: Academic Press.Google Scholar
Hayashi, Chikio. 1980. “Data Analysis in a Comparative Study.” In Data Analysis and Informatics, eds. Diday, E., Lebart, L., Pages, J. P., and Tomassone, R. Amsterdam: North-Holland, pp. 31–51.Google Scholar
Hayes, Bernadette C., and Bean, Clive S. 1993. “Political Efficacy: A Comparative Study of the United States, West Germany, Great Britain and Australia.” European Journal of Political Research 23: 261–280.Google Scholar
Heiser, Willem J., and Meulman, Jacqueline J. 1994. “Homogeneity Analysis: Exploring the Distribution of Variables and their Nonlinear Relationships.” In Correspondence Analysis in the Social Sciences. Recent Developments and Applications, eds. Greenacre, Michael and Blasius, Jörg. London: Academic Press, pp. 179–209.Google Scholar
Le Roux, Brigitte, and Rouanet, Henry 1998. “Interpreting Axes in Multiple Correspondence Analysis: Method of the Contributions of Points and Deviations.” In Visualization of Categorical Data, eds. Blasius, Jörg and Greenacre, Michael J. San Diego: Academic Press, pp. 197–220.Google Scholar
Mondak, Jeffrey J. 1999. “Reconsidering the Measurement of Political Knowledge.” Political Analysis 8: 57–82.CrossRefGoogle Scholar
Niemi, Richard G., Craig, Stephen C., and Mattei, Franco. 1991. “Measuring Internal Political Efficacy in the 1988 National Election Study.” American Political Science Review 85: 1407–1413.CrossRefGoogle Scholar
Nishisato, Shizuhiko. 1980. Analysis of Categorical Data Dual Scaling and Its Applications. Toronto: University of Toronto.CrossRefGoogle Scholar
Parry, Geraint, Moyser, Georg, and Day, Neil. 1992. Political Participation and Democracy in Britain. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Stewart, Marianne C., Kornberg, Allan, Clarke, Harold D., and Acock, Alan. 1992. “Arenas and Attitudes: A Note on Political Efficacy in a Federal System.” Journal of Politics 54: 179–196.CrossRefGoogle Scholar
SPSS Inc. 1990. SPSS Categories. Chicago, IL: SPSS Inc.Google Scholar
Thiessen, Victor, and Blasius, Jörg. 1998. “Using Multiple Correspondence Analysis to Distinguish Between Substantive and Non-Substantive Responses.” In Visualization of Categorical Data, eds. Blasius, Jörg and Greenacre, Michael. San Diego: Academic Press, pp. 239–252.CrossRefGoogle Scholar
Van der Heijden, Peter G.M., and de Leeuw, Jan. 1985. “Correspondence Analysis Used Complementary to Loglinear Analysis.” Psychometrika 50: 429–447.Google Scholar
Van der Heijden, Peter G.M., de Falguerolles, Antoine, and de Leeuw, Jan. 1989. “A Combined Approach to Contingency Table Analysis Using Correspondence Analysis and Loglinear Analysis.” Applied Statistics 38: 249–292.Google Scholar
Van der Heijden, Peter G.M., Mooijaart, Ab, and Takane, Yoshio. 1994. “Correspondence Analysis and Contingency Table Models.” In Correspondence Analysis in the Social Sciences. Recent Developments and Applications, eds. Blasius, Jörg and Greenacre, Michael J. London: Academic Press, pp. 79–111.Google Scholar