Changes in Candidate Evaluations over the Campaign Season: A Comparison of House, Senate, and Presidential Races

Abstract

How do citizens’ preferences for candidates change during a campaign season? For the first time, this panel study examines how citizens’ preferences for candidates change during the general election campaign season for House, Senate, and presidential elections, which vary widely in their salience and contestedness. House races exhibit the greatest mean change in candidate evaluations and presidential races exhibit the least. At the individual level, there is considerable variation across the three types of contest in the presence of a candidate preference and in change over the campaign season. We investigate differences across the three types of races in initial familiarity with candidates and estimate transition models to evaluate the effect of race contestedness, partisanship, presidential approval, political sophistication and knowledge on change in candidate preferences in each type of race. Change in knowledge of the candidates during the campaign season has the greatest effect in House contests, where initial familiarity with the candidates is the most limited.

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Notes

  1. 1.

    The “minimal effects” literature is large (Wlezien and Erikson 2001). It usually includes Bartels (1993); Bartels and Zaller (2001); Berelson et al. (1954); Campbell et al. (1960); Finkel (1993); Gelman and King (1993); Levitt (1994); Lewis-Beck and Rice (1992). There were important exceptions, such as Goldenberg and Traugott (1987), which showed campaign effects in congressional campaigns.

  2. 2.

    These include Ansolabehere and Iyengar (1995); Box-Steffensmeier et al. (2009); Campbell et al. (1992); Claassen (2011); Dilliplane (2014); Fridkin et al. (2007); Geer (1988); Grant et al. (2010); Hill et al. (2010); Holbrook (1996); Lenz (2009); Masket (2009); Shaw (1999a, b); Vavreck (2009).

  3. 3.

    Ansolabehere and Iyengar (1995); Brady et al. (2006); Cox and Munger (1989); Gilliam (1985); Gimpel et al. (2007); Hillygus (2005); Holbrook and McClurg (2005); McGhee and Sides (2011); Peterson (2009); Stimson (2004); Masket (2009)

  4. 4.

    See footnotes 1 and 2. On competitiveness and turnout, also see Blais (2006); Coleman and Manna (2000); Cox and Munger (1989); Gershtenson (2009); Goldstein and Freedman (2002); Highton (2010); Lachat (2011); Timpone (1998); Westlye (1991); Wolak (2006). On competitiveness and candidate familiarity, see Huckfeldt et al. (2007) and Niemi et al. (1986). On media coverage of congressional campaigns, see Freedman et al. (2004); Goldenberg and Traugott (1987); Prinz (1995); Stewart and Reynolds (1990). On issues, campaigns, and voting, see Abbe et al. (2003); Ansolabehere et al. (2008); Erikson and Wright (1989); Herrnson and Curry (2011); Page and Jones (1979).

  5. 5.

    Aldrich (1993); Alvarez (1998); Bartels (2002); Downs (1957); Carson et al. (2010); Canes-Wrone et al. (2002); Jessee (2009, 2010, 2012); Enelow and Hinich (1984); Lenz (2009, 2011); Tomz and Van Houweling (2009); Rogowski and Tucker (2018); Cahill and Stone (2018); Gelman and King (1993); Henderson (2014, 2015).

  6. 6.

    Panelists were asked the same battery of questions in October of each campaign year. For ease of analysis, we set aside the October responses to focus on the difference between responses at the start of the general election campaign and responses after a vote choice has been made.

  7. 7.

    We also estimated models using the scaled sophistication scores that were not quadratically transformed, as well as three-category ordinal measures of sophistication. Those results may be found in the Online Appendix.

  8. 8.

    While not the main issue of interest in this report, we note the high prevalence of undecideds at the beginning of campaigns. As an ancillary analysis, we investigate what predicts an undecided voter at the beginning of the general election. The results and a brief discussion may be found in Online Appendix Section SI-2.

  9. 9.

    Changes in knowledge during the campaign in these states are consistent with the rest of the sample. See Online Appendix Table A15.

  10. 10.

    2016 political peculiarities may have created many late-switching voters. See Richard Cowan, “Democrats See FBI Controversy Hurting Chances in U.S. Congress Races,” Reuters, November 7, 2016 (https://www.reuters.com/article/us-usa-elections-congress/democrats-see-fbi-controversy-hurting-chances-in-u-s-congress-races-idUSKBN13218S), and Alex Seitz-Wald, “Democrats Fear Senate Majority Quest May Be Killed by Comey,” NBC News, November 1, 2016 (https://www.nbcnews.com/politics/2016-election/democrats-fear-path-senate-majority-getting-sidetracked-comey-s-email-n675981).

  11. 11.

    See Online Appendix Tables A1–A3.

  12. 12.

    At the race level, we also examined if candidate spending was associated with transitions in support. While we found no evidence of an association in 2014, we found a significant relationship in both Senate and House elections in 2016: a candidate who had a spending advantage over their opponent was significantly more likely to pick up the support among undecideds, initial Republican voters, and initial Democratic voters in Senate elections. See Online Appendix Tables A12 and A13.

  13. 13.

    We also estimated more saturated models with controls for income, race, ideology, rural residence, and gender. The main results of Tables 3 and 4 hold. We find strong support that conservatives are less likely to transition to Democrats over the campaign, which is largely consistent with the approval of Obama and partisan findings presented in Tables 3 and 4. See Tables A17 and A18 in the Online Appendix.

  14. 14.

    Issue item data were only available for the 2016 waves of the panel. For brevity, we present the predicted probabilities of the estimations in the main text. The estimated tables may be found in the Online Appendix Table A6.

  15. 15.

    We also examined the possibility of an indirect relationship of closeness of the election on transitions in support by investigating whether close races were more likely to be associated with greater learning. As the logistic regressions in Online Appenidx Table A21 demonstrate, we were unable to find such an effect. At the same time, however, we did find evidence that moderately sophisticated citizens were the most likely to increase their knowledge over the course of the presidential campaign.

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Tucker, P.D., Smith, S.S. Changes in Candidate Evaluations over the Campaign Season: A Comparison of House, Senate, and Presidential Races. Polit Behav (2020). https://doi.org/10.1007/s11109-020-09603-8

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Keywords

  • Campaigns
  • Elections
  • Panel data
  • Legislative elections
  • Learning