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Revisiting the Influence of Campaign Tone on Turnout in Senate Elections

Published online by Cambridge University Press:  04 January 2017

Robert A. Jackson  and
Jason C. Sides
Robert A. Jackson
Affiliation:
Department of Political Science, Florida State University, Tallahassee, FL 32306-2230. e-mail: rjackson@garnet.acns.fsu.edu (corresponding author)
Jason C. Sides
Affiliation:
Reubin O'D. Askew School of Public Administration and Policy, Florida State University, Tallahassee, FL 32306. e-mail: jcs6605@garnet.acns.fsu.edu
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Abstract

Examining the influence of campaign tone on individual turnout in the 1990 U.S. Senate elections, this note revisits Kahn and Kenney's conclusion that the political profile of citizens (as based on partisanship, level of political interest, and level of political expertise) conditions their responsiveness. Implementing an appropriate modeling strategy for making group comparisons, our analyses do not provide statistical support for the conditional effects that they highlight. More generally, our results do reinforce Kahn and Kenney's finding that negativity in tone of news coverage mobilizes citizens, but they do not reveal significant turnout influence for television advertising tone.

Information

Type
Research Article
Information
Political Analysis , Volume 14 , Issue 2 , Spring 2006 , pp. 206 - 218
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Ansolabehere, Stephen, and Iyengar, Shanto. 1995. Going Negative: How Political Advertising Shrinks and Polarizes the Electorate. New York: Free Press.Google Scholar
Ansolabehere, Stephen, Iyengar, Shanto, Simon, Adam, and Valentino, Nicholas. 1994. “Does Attack Advertising Demobilize the Electorate?” American Political Science Review 88: 829–838.Google Scholar
Ansolabehere, Stephen D., Iyengar, Shanto, and Simon, Adam. 1999. “Replicating Experiments Using Aggregate and Survey Data: The Case of Negative Advertising and Turnout.” American Political Science Review 93: 901–909.Google Scholar
Brambor, Thomas, Clark, William Roberts, and Golder, Matt. 2005. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis doi:10.1093/pan/mpi014.Google Scholar
Carsey, Thomas M., and Wright, Gerald C. 1998. “State and National Factors in Gubernatorial and Senatorial Elections.” American Journal of Political Science 42: 994–1002.Google Scholar
Clinton, Joshua D., and Lapinski, John S. 2004. “‘Targeted’ Advertising and Voter Turnout: An Experimental Study of the 2000 Presidential Election.” Journal of Politics 66: 69–96.Google Scholar
Finkel, Steven E., and Geer, John G. 1998. “A Spot Check: Casting Doubt on the Demobilizing Effect of Attack Advertising.” American Journal of Political Science 42: 573–595.Google Scholar
Freedman, Paul, and Goldstein, Ken. 1999. “Measuring Media Exposure and the Effects of Negative Campaign Ads.” American Journal of Political Science 43: 1189–1208.CrossRefGoogle Scholar
Friedrich, Robert J. 1982. “In Defense of Multiplicative Terms in Multiple Regression Equations.” American Journal of Political Science 26: 797–833.Google Scholar
Goldstein, Ken, and Freedman, Paul. 2002. “Campaign Advertising and Voter Turnout: New Evidence for a Stimulation Effect.” Journal of Politics 64: 721–740.Google Scholar
Jaccard, James. 2001. Interaction Effects in Logistic Regression. Thousand Oaks, CA: Sage.Google Scholar
Jackson, Robert A. 2002. “Gubernatorial and Senatorial Campaign Mobilization of Voters.” Political Research Quarterly 55: 825–844.Google Scholar
Jackson, Robert A., and Carsey, Thomas M. 2006. “U.S. Senate Campaigns, Negative Advertising, and Voter Mobilization in the 1998 Midterm Elections.” Electoral Studies in press.Google Scholar
Kahn, Kim Fridkin, and Kenney, Patrick J. 1999a. “Do Negative Campaigns Mobilize or Suppress Turnout? Clarifying the Relationship between Negativity and Participation.” American Political Science Review 93: 877–889.Google Scholar
Kahn, Kim Fridkin, and Kenney, Patrick J. 1999b. The Spectacle of U.S. Senate Campaigns. Princeton, NJ: Princeton University Press.Google Scholar
Kahn, Kim F., and Kenney, Patrick J. United States Senate Campaign Strategies and Media Analysis, 1988–1992 [Computer file]. ICPSR version. Tempe, AZ: Arizona State University [producer], 1995. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor], 2001.Google Scholar
King, Gary, Honaker, James, Joseph, Anne, and Scheve, Kenneth. 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review 95: 49–69.Google Scholar
Kreft, Ita, and de Leeuw, Jan. 1998. Introducing Multilevel Modeling. Thousand Oaks, CA: Sage.Google Scholar
Lau, Richard R., and Pomper, Gerald M. 2001. “Effects of Negative Campaigning on Turnout in U.S. Senate Elections, 1988–1998.” Journal of Politics 63: 804–819.Google Scholar
Lau, Richard R., Sigleman, Lee, Heldman, Caroline, and Babbit, Paul. 1999. “The Effects of Negative Political Advertisements: A Meta-analytic Assessment.” American Political Science Review 93: 851–875.CrossRefGoogle Scholar
Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage.Google Scholar
Long, J. Scott, and Freese, Jeremy. 2003. Regression Models for Categorical Dependent Variables Using Stata, Revised Edition. College Station, TX: Stata Press.Google Scholar
Long, J. Scott, and Miethe, Terance D. 1988. “The Statistical Comparison of Groups.” In Common Problems/Proper Solutions: Avoiding Error in Quantitative Research, ed. Long, Scott J. Newbury Park, CA: Sage, pp. 108–131.Google Scholar
Luke, Douglas A. 2004. Multilevel Modeling. Thousand Oaks, CA: Sage.Google Scholar
Miller, Warren E., Kinder, Donald R., and Rosenstone, Steven J., and the National Election Studies. American National Election Study, 1988, 1990, 1992 [Computer file]. 3rd ICPSR version. Ann Arbor, MI: Interuniversity Consortium for Political and Social Research [distributor], 2005.Google Scholar
Moulton, Brent R. 1990. “An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units.” Review of Economics and Statistics 72: 334–338.Google Scholar
Nagler, Jonathan. 1991. “The Effect of Registration Laws and Education on U.S. Voter Turnout.” American Political Science Review 85: 1393–1405.Google Scholar
Raudenbush, Stephen W., and Bryk, Anthony S. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed. Thousand Oaks, CA: Sage.Google Scholar
Rogers, William H. 1993. “Regression Standard Errors in Clustered Samples.” Stata Technical Bulletin 13: 19–23. Reprinted in Stata Technical Bulletin Reports 3: 88–94.Google Scholar
Snijders, Tom, and Bosker, Roel. 1999. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Thousand Oaks, CA: Sage.Google Scholar
Steenbergen, Marco R., and Jones, Bradford S. 2002. “Modeling Multilevel Data Structures.” American Journal of Political Science 46: 218–237.Google Scholar
Stoker, Laura, and Bowers, Jake. 2002. “Designing Multi-level Studies: Sampling Voters and Electoral Contexts.” Electoral Studies 21: 235–267.Google Scholar
Wattenberg, Martin P. 2002. Where Have All the Voters Gone? Cambridge, MA: Harvard University Press.Google Scholar
Wattenberg, Martin P., and Brians, Craig Leonard. 1999. “Negative Campaign Advertising: Demobilizer or Mobilizer?” American Political Science Review 93: 891–899.Google Scholar
Wooldridge, Jeffrey M. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: MIT Press.Google Scholar
Zeng, Langche. 2000. “A Heteroscedastic Generalized Extreme Value Discrete Choice Model.” Sociological Methods and Research 29: 118–144.CrossRefGoogle Scholar
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