Abstract
Revisionists demonstrate campaigns mobilize, educate, activate predispositions, and change minds. Attention has turned from the “minimum effects” thesis to questions about the conditions under which campaigns matter and questions about which types of people are susceptible to campaign effects. Focusing on whether campaign effects are mediated by chronic political awareness, I find that current scholarship on this question is mixed. Some find that campaigns affect the politically unaware most, some find bigger effects among more aware citizens, and some find similar effects across the awareness distribution. Noting the possibility that awareness mediates different types of campaign effects differently (e.g. priming, persuasion, or learning), Zaller’s Receive–Accept-Sample framework is consulted to develop expectations. I test the RAS generated predictions using the 2004 National Annenberg Election Survey pre/post panel. The results support the theory that awareness mediates different campaign effects differently.
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Notes
Although I detail the procedures used to measure political awareness in what follows, I use the term throughout to indicate what Zaller describes as, “the extent to which an individual pays attention to politics and understands what he or she has encountered” (1992, p. 21, emphasis in original).
Zaller’s conclusions seem to support this statement. He notes,
Thus, most of my evidence for the importance of cueing messages derives from examination of the attitudes of the politically aware, who are extremely responsive to what ideologically congenial elites urge them to believe. In one type of case, however, the entire mass public—and not just the most politically attentive segment—relies heavily on partisan cues. This is the case of contested elections….” (1992, p. 275, emphasis added).
In a campaign in which there is no dominant message (two-sided flow), the more aware will accept messages more selectively—based on congruence with their predispositions (according to the resistance axiom). When confronted with a question about which candidate to support, having a more homogenous set of considerations from which to sample will make it easier for more aware citizens to connect their predispositions and their choice (e.g. their voting behavior will be more structured). By the same logic, lack of structure in the voting decisions of less aware individuals (e.g. difficulty connecting their predispositions and their vote choices) results from their low partisan resistance.
It also bears mentioning that attitude change of the sort described by Lenz is often assumed to be due to persuasion—meaning it is assumed that campaigns cause individuals to change their minds about important issues by presenting them with compelling new information. But some of what appears to be persuasion may reflect attitude changes due to rationalization and projection as respondents alter their issue positions so that their positions are consistent with their candidate preferences (see Brody and Page 1972; Iyengar and Kinder 1987; Rahn et al. 1994). In any case, attitude change—regardless of source—cannot contaminate estimates of priming in this study since attitudes are fixed by design.
Nevertheless, this research design does not entirely eliminate attitude change due to projection and rationalization. The distinction is important in studies of candidate choice because persuasion implies that attitude change affects candidate choice while projection and rationalization imply the opposite (see Markus and Converse 1979; Page and Jones 1979). In the context of modeling attitude change, however, the distinction is far less important because the quantity of interest is not candidate choice. All three processes—persuasion, projection, and rationalization—are in fact associated with attitude change. Thus “persuasion effect” is a bit of a misnomer, but—having defined persuasion as “analogous to what Zaller describes as attitude change” in the background section—I retain the term to describe campaign related attitude change.
Non-major party voters are omitted from the vote report model. This procedure is identical to the one used by Bartels to estimate two probit models of candidate support (an ordered probit model of vote intention and a probit model of vote report). Although the intention parameters are obtained using an ordered probit estimator and the vote parameters are obtained using a probit estimator, the sample is identical in both models and the estimates are comparable because the deletion of a category does not change the structural model.
Long (1997, p. 119): “[Y] could add (or delete) another cutpoint without changing the structural model …. This would correspond to adding [or deleting] another category to [or from] the ordinal scale…. The regression line or y* on x would not be affected.”
Long and Freese (2006, p. 184): “Indeed the [binary regression model] can be viewed as a special case of the ordinal model in which the ordinal outcome has only two categories.”
Although Annenberg poses many issue-related questions, these are among the only ones asked consistently throughout the entire campaign period. Others appear for brief intervals of the rolling cross-section only.
The Cronbach’s Alpha is .57 for low awareness subjects, .68 for moderately aware subjects, and .73 for high awareness subjects. The difference is interesting, but it is only problematic if the key difference between low and high awareness subjects is ability to provide quality survey responses. If low reliability among low awareness subjects is purely a measurement problem (e.g. Achen 1975), then one worries that differences in priming may be statistical artifacts. If, on the other hand, low awareness subjects really have less developed ideologies and issue positions—then the low reliabilities are indicative of interesting and important substantive differences between otherwise similar individuals who differ in their level of political awareness (e.g. Converse 1964). I take the latter position and make the case that the results are better characterized as due to substantive differences—not reliability artifacts.
Zaller makes a similar point,
My position falls somewhere between the Converse and Achen positions. It agrees with Converse that there is a great deal of uncertainly, tentativeness, and incomprehension in the typical mass survey response. The problem, it further contends, is much deeper than vague questions. … My claim is that even when people are temporally unstable, expressing completely opposing positions at different times, they may still, like the vacillating teacher profiled in Chapter 4, be expressing real feelings, in the sense that they are responding to the issue as they see it at the moment of response. Although perceptions of the issue may change over time, the responses they generate are not, for that reason, lacking in authenticity. (1992, p. 94, emphasis added)
Stata’s “set seed” command was used to ensure that comparisons of bootstrapped intention models and bootstrapped vote models involved the same bootstrapped samples.
This indicator has proven to be a valid measure in the context of the National Election Studies and Annenberg’s measure is nearly identical. Bartels used NES interviewer ratings to provide important insights into public opinion toward defense spending (Bartels 1994) and information effects in presidential voting (Bartels 1996). Reporting on his extensive analysis of interviewer ratings in comparison to other indicators of political awareness, Zaller (1985) writes that “… [the] single five-point interviewer rating scale performs about as well as a scale constructed from 10 to 15 direct knowledge tests, where the measure of performance is the ability to predict relevant criteria” (p. 338).
To conserve space, I report only the direct effects for the three primary explanations of voting: issues, party identification, and economic assessments. The full models are available in the Supplementary Online Appendix.
Like Bartels (2006) I report issue priming having aggregated priming on several items. The combined scale captures the overall effect of ideology, gay marriage, and the war in Iraq, but there are some interesting sub-patterns. First, ideological priming appears to drive issue priming among moderately aware voters. Second, priming on the Iraq war is comparatively weak—especially among less aware and moderately aware voters. While these nuances are interesting, the overall effect is captured more clearly when an index is used. After all, the basic pattern is identical to the one obtained using the combined issue index: for each issue, priming effects are weak to nonexistent for less aware subjects and improve with increased awareness. In the end, ideological priming among the moderately aware is offset by comparatively weak priming on gay marriage and the Iraq war. Likewise although priming on the Iraq war is comparatively weak, the awareness-based differences in priming on this issue contribute to the overall priming differences between more and less aware subjects.
Although I limit my analyses to attitude changes that aggregate-up to group persuasion, it is possible that lack of group change masks individual-level changes that cancel-out. Using the post-election wave of the survey, I checked for this possibility and found significant individual-level movement regardless of awareness—but again movement was greatest among the least aware. Nevertheless, the research design employed here captures the quantity that matters most. After all if persuasion in a group adds up to zero then the candidates’ electoral performances will remain unaltered by persuasion for that group. Bartels (2006) makes a similar point, “I shall not explore a variety of potentially interesting changes in the distributions of attitudes and perceptions that leave the average value unchanged…” (p. 94).
This produces two predicted probabilities for each low awareness subject. The difference of these probabilities represents the change in probability associated with priming a given attitude for each low awareness subject. The average of these differences is the effect of priming for low awareness subjects.
Again, this produces two predicted probabilities for each low awareness subject. The difference of these probabilities represents the change in probability associated with persuasion, for a given attitude, for each low awareness subject. The average of these differences is the effect of persuasion for low awareness subjects.
The significant constant in Table 3 indicates that when awareness and period equal zero, the expected value for economic assessments is a robust −.39. Recall that the scale ranges from −1 to +1, where negative values indicate negative economic assessments.
Bartels also finds minimal priming on economic assessments and only in a single election, 1980. Although my intent is a general model of awareness mediated priming, it would not surprise me if the 2008 election produced more priming on economic assessments due to the different circumstances in 2008.
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Claassen, R.L. Political Awareness and Electoral Campaigns: Maximum Effects for Minimum Citizens?. Polit Behav 33, 203–223 (2011). https://doi.org/10.1007/s11109-010-9129-6
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DOI: https://doi.org/10.1007/s11109-010-9129-6