Abstract
This chapter shows that the frames present in the dialogue in the news media are highly relevant for the vote outcome. We show a framing effect in two of the three cases. These frames remain important for the voting decision when controlling for and in comparison with partisan heuristic. We can state that the more important the topic for a person, the less polarized the context, and the less complex the topic, the more a person relies on the frame-based path (thus, on arguments and less on heuristics like partisan orientation or social identities in general). In this regard, the chapter gives empirical support to the realistic theory of democracy. We do not expect arguments to play a more important role in other types of campaigns or other contexts. What we find here is the most that can be expected from citizens.
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Notes
- 1.
The exact question was: “Which of the ten arguments from the list (shown to the campaigner) do you conceive as the three most important, as the single most important, and the three least important?” I do not use the evaluation of the most and the three most important arguments here.
- 2.
The specific questionnaire item reads as follows (for the humanitarian tradition frame , for example): “On a scale from 0 (completely disagree) to 5 (completely agree), how much do you agree with the argument: The humanitarian tradition must be maintained?”
- 3.
For the vote intentions, the specific questionnaire item reads as follows: “If there were a ballot tomorrow, would you be strongly in favor, rather in favor, rather against or strongly against the toughening of the asylum law?” Being (strongly or rather) in favor of the proposition is coded as 1, and being undecided or (rather or strongly) against as 0. For the vote choice, the questionnaire item reads: “How did you vote? Did you agree with or reject the asylum law?” Again, agreement is coded as 1 and non-decision and rejection as 0.
- 4.
Respondents who do not attribute a utility higher than five to any of the six parties, plus the (very few) respondents who attribute equal preference to all of the parties, are considered not to have any particular partisan predisposition and, accordingly, are dropped from the analysis. I have used MUDFOLD 4.0 for my analysis (see, Van Schuur and Post 1998).
- 5.
The five statements concerned individual safety threat (“I am afraid of increasing violence and vandalism in my neighborhood by foreigners”), individual economic threat (“I am afraid that my economic prospects will get worse because of foreigners”), collective safety threat (“I am afraid of increasing violence and vandalism in Swiss society by foreigners”), collective cultural threat (“These days, I am afraid that the Swiss culture is threatened by foreigners”), and collective economic threat (“I am afraid that the economic prospects of Swiss society will get worse because of foreigners”). With regard to partisan predispositions, these questions were only asked once, at the beginning of the campaign, since my aim is to determine the extent to which voters converge on their pre-campaign issue-specific predispositions.
- 6.
The questions were part of a battery that asked for the kind of country the respondents preferred. Each item asked the recipient to choose between polar contrasts: “Please tell me what kind of Switzerland you prefer: a Switzerland… a) …with more state interventions in the economy, or with more market competition; b)…that places more emphasis on solidarity, or on individual responsibility; c)…with large income differences, or small income differences?” Each item was measured on a five-point scale, ranging from strong agreement with the first part to strong agreement with the second part of the item.
- 7.
- 8.
The exact question was (example for the humanitarian tradition frame ): “There have been different opinions with regard to the asylum law. In the following, I will read aloud some arguments. Could you please indicate with ‘yes’, ‘no’, or ‘don’t know’ whether you have ever heard of them in the current debate: ‘The humanitarian tradition must be maintained’?”
- 9.
These illustrations are based on the model used for Table A.6. I used the variables relevant for the specific time point and set the other variables at their means.
- 10.
The point estimates for the linear combination for the difference in frame strength between two time points provide the following results:
Human. trad. frame: (t2)−(t1): z = −3.24, P > z = 0.001, (t3)−(t1): z = −3.72, P > z = 0.000, (t3)−(t2): z = −0.98, P > z = 0.329.
Abuse frame : (t2)−(t1): z = 1.76, P > z = 0.078, (t3)−(t1): z = 2.64, P > z = 0.008, (t3)−(t2): 1.03, P > z = 0.301.
Efficacy frame : (t2)−(t1): 2.38, P > z=0.017, (t3)−(t1): z = 3.32, P > z = 0.001, (t3)−(t2): 1.24, P > z = 0.215.
- 11.
SME frame : (t2)−(t1): z = 2.3, P > z = 0.022.
- 12.
Tax equity frame : (t2)−(t1): z = 4.35, P > z = 0.000.
- 13.
All campaign arguments were jointly included in one factor analysis.
- 14.
Based on the partisan predisposition scores, I coded left as including Green and Social Democrat voters, moderate right as in-between voters favoring Christian Democrats and Liberals, and conservative right as including voters favoring the People’s Party.
- 15.
The predicted probabilities are calculated for decided voters at the end of the campaign with mean values on remaining variables.
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Hänggli, R. (2020). Role of Dialogue in Public Opinion Formation. In: The Origin of Dialogue in the News Media. Challenges to Democracy in the 21st Century. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-26582-3_8
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