Measuring Exposure to Political Advertising in Surveys - College of ...

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Polit BehavTable 2 Regression of discrepancies between ANES questions on individual level characteristicsVariableCoefficient (standard error)Total daypart estimate .45 (.03)**Political knowledge .96 (.47)*Mobilized by a party/candidate 1.66 (.68)*Strength of party identification .47 (.61)Internal efficacy .21 (.57)External efficacy .39 (.44)Age .05 (.04)Sex .83 (1.18)Constant 10.91 (2.85)**N 555Adjusted R 2 .35** p < .01, * p < .05, # p < .10 (two-tailed)Data from: ANES 1998 pilot studyor that the largest discrepancies are characteristic of those who watch the mosttelevision according to the daypart questions (the coefficient would be positive). 9Table 2 illustrates that several individual characteristics are associated withgreater sensitivity to question format; that is with larger discrepancies in estimatedtelevision watching. In addition, the positive and statistically significant coefficienton the daypart estimate shows that the discrepancies with the ‘‘typical day’’questions are not constant but grow larger as the daypart estimates grow larger. It isthe relationships with political knowledge and mobilization by a party or candidatethat are most interesting, however. Politically knowledgeable individuals and thosesubject to the most intense mobilization efforts, who we also know are likely to havethe greatest resources of time and money and to be politically engaged, are the mostsensitive to question format (i.e., the discrepancies in their answers tend to begreatest). This echoes Chang and Krosnick’s (2003) finding for highly educatedrespondents and the explanation may well be similar: differences in questionwording prompt different memory searches for these individuals but do not for thosewho lack political knowledge or are disengaged. 109 I excluded 12 respondents who, in answer to the typical weekday day, evening, or weekend questions,said they watched more than 10 h a day because they were all coded as an ‘11’ in the ANES survey ratherthan by the exact number of hours. Because the hours they watch may exceed 11, the discrepancy with thedaypart questions could be exaggerated. This is not a conventional case of censoring for which tobitestimation would be appropriate. The censoring affects a component of a dependent variable(discrepancy), stopping us from knowing whether the two methods of self-report offer very similaranswers for these 12 respondents, rather than there being censoring of the dependent variable itself at itsupper or lower levels.10 Indeed, replacing political knowledge with level of education in Table 2 shows the same robust,positive relationship. With the inclusion of both political knowledge and education in the same model,however, the coefficients for each are reduced and political knowledge drifts to statistical insignificance;they share variance because educated individuals tend to be more politically informed. They each indicatethat political sophistication is associated with sensitivity to question wording. In the remainder of thepaper I continue to focus on political knowledge because it is the more common indicator of politicalsophistication in this literature (e.g., Freedman et al., 2004; Kahn & Kenney, 1999)123
Polit BehavMore importantly, however, these nonrandom discrepancies are greatest amongprecisely those individuals most interested in campaigns, most likely to vote, and soon. The daypart questions inflate estimates of television watching generally, butbecause discrepancies with other measures are systematically more pronouncedamong these individuals the relationships between, for example, political knowledge,exposure to negative advertising, and attitudes and behavior will be sensitiveto the questions used to construct the exposure estimates, varying in sign and size(Berry and Feldman 1985). As implied by this evidence, the literature using CMAGdata has been inconsistent, suggesting both that the least politically knowledgeableare unaffected or confused by exposure to (negative) advertising (Stevens, 2005)and that they benefit the most from exposure to advertising (Freedman et al., 2004).In either case the normative implications are profound, either implying that, all elseequal, the modern campaign exacerbates cynicism and inequality in politicalparticipation or that campaign ads ‘‘represent the multivitamins of Americanpolitics’’ (Freedman et al., 2004, 725).The 1998 ANES pilot data allow a more explicit analysis of how questionformats may contribute to the confusion. I estimated identical models of therelationship between exposure to negative advertising, political knowledge and fourdependent variables typical of the literature: engagement with the campaign(frequency of discussing politics during the past week and awareness of the issuesthe candidates for governor had been discussing during the campaign), views ofgovernment (external efficacy), and the probability of voting (see Appendix fordetails). 11 One set of models operationalized total exposure to negative advertisingusing the daypart method, another calculated total exposure from the ‘‘typical day’’questions, while a third constructed total exposure using the shows method. Inaddition I controlled for standard variables in the literature (e.g., Freedman et al.,2004): the total amount of negative advertising in the respondent’s market—ameasure of how intense the campaign was in that locale 12 —dummy variables fortwo of the three states (Georgia and Illinois), strength of party identification, theextent of mobilization from the parties, education, race, age, and income. The keyvariables of interest are exposure to negative advertising and the interaction betweenexposure to negative advertising and political knowledge. Table 3 presents the11 The CMAG data for 1998 do not include information about gubernatorial advertising. However,Stevens (2005) argues that because both the gubernatorial and Senate elections in California, Georgia,and Illinois shared similar characteristics, such as competitiveness, and because candidates tend to air adsat the same time it is a reasonable assumption that exposure to advertising in the gubernatorial race washighly correlated with exposure to the Senate race. In Tables 3 and 4 I include one dependent variable thatis specific to the gubernatorial races in these states, the number of issues that respondents recognize thecandidates have talked about: if exposure to negative advertising increases awareness of issues andindividuals who saw a lot of Senate ads also saw a lot of gubernatorial ads we would expect exposure tonegative advertising to have a positive relationship with recognition of issues.12 Total negative advertising in a television market is arguably a better measure of campaign intensitythan total advertising because we tend to see more advertising, and more negative advertising, incompetitive races. I also estimated all the models in Tables 3 and 4 with total advertising as a proxy forcampaign intensity. It made no difference to the results.123
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