He led until a crippling road accident in The relaunched Democratic Party never entertained mainstream politics, and did not participate in Turkey's elections of November From Wikipedia, the free encyclopedia. This article is about the historical Democrat Party in Turkey. For the party established in , see Democratic Party Turkey, For the new party of the name founded in , see Democrat Party Turkey, current. Democrat Party. Politics of Turkey Political parties Elections. Hoover Institution. Beyond Military Tutelage?
Lexington Books. Problematizing the intellectual and political vestiges: From 'welfare' to 'justice and development'. Turkey: The Quest for Identity. Oxford: Oneworld Publications, Popular Movements and Democratization in the Islamic World. Middle Eastern Studies, Vol. Comparative Politics, Vol. Published by: Ph. Cambridge University Press. Historical parties in Turkey.
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click here Ankara , Turkey. Unfortunately, it is unknown how many days a week respondents on average read the newspapers they indicated they read on a regularly basis, and thus, the media content cannot be weighed accordingly. As a result, our main analyses are based on 11,, cases. Appendix C presents an example of the structure of the merged data set.
While the 1VOP data set provides unique opportunities for studying electoral dynamics, it is rather limited in the availability of independent variables.
Van Aelst, P. These scholars suggest that people will use the information available to them, making inferences based on whatever information they have, even if they have next to no information at all. Celal Bayar Adnan Menderes Some have focused on open- ended traits questions Bean ; Butler and Stokes ; Butler and Stokes ; Campbell et al. Previous post Next post.
Ideological positions, issues, and party identification are not included in the data. Since party identification is not a very useful concept in the Dutch case e. However, models without left—right distance and issue positions would normally be considered to be underspecified.
We resolve this problem by including a lagged dependent variable in the model. The lagged dependent variable largely captures the effects of other predictors of the vote that are relatively stable over time, including socioeconomic demographics. Furthermore, we control for respondent-specific features: the total amount of waves of the 1VOP in which the respondents participated, membership in the specific party, voting for the specific party in the previous parliamentary elections, and various demographics gender, age, and level of education.
This merged data set opens up the possibility to test the effects of mediated leadership images on shifts in vote intentions on the individual level. These types of questions are usually difficult to study due to the lack of sufficient statistical power. This data set does not suffer from this problem, with its sizable number of respondents, regular waves, high number of party leaders, and enormous amount of media information.
However, a drawback of this data structure is that the number of days between two waves differs strongly between respondents and within respondents over time; personalized waves are not fixed in time but depend on the participation of the respondent in 1VOP. In this research, we employ logistic regression analyses, which pose some methodological challenges. First, unobserved heterogeneity, that is, the influence of relevant variables that are not included in the model, is mainly addressed by i including the lagged dependent variable, ii including party fixed effects, and iii clustering the standard errors by respondents.
Second, to test whether the unequal time periods between personalized waves affects the results, a robustness check with interaction effects between the media variables and the number of days between waves is performed.
Another solution to the problem of unequal time periods is to include only the media data of a fixed number of days before the 1VOP wave or to use exponential decay on the media variables see, for instance, Kleinnijenhuis and Walter However, the basic assumption underlying this strategy is that voters decide who to vote for or change party preference in the exact moment that they indicated in the survey.
Since we know only whether voters changed their party preferences in the time between two waves but not when they did, we include the averaged media content of the whole period between waves. Table 1 presents the results of the analyses. Model 1 includes the media visibility of party leaders and the general tone in which party leaders are described in terms of their character traits and the control variables. It shows first that the media visibility of party leaders has a small positive effect on vote intentions even when controlling for previous party preference.
We expected to find asymmetrical effects for positive and negative images H3 , where negative images were assumed to exert a stronger effect than positive images. However, the results suggest otherwise. The negative effect of negative images in the media is smaller than the positive effect of positive images. Thus, positive descriptions of party leaders in terms of their leadership qualities are more influential than negative descriptions, contradicting our expectations but in line with the findings of Wattenberg and Aarts and Blais , who also show stronger effects of subjective positive leader evaluations than of negative ones.
The standard errors are clustered by respondent; the models additionally control for the total amount of waves respondents participated in, the number of days in between waves, gender, age and level of education, and party fixed effects were added to the model not shown here. The campaign period of the election campaign is excluded in model 3, as the corresponding routine time was not included in the content analysis.
Another interesting feature of the model concerns the explained variance. The pseudo r -square indicates that approximately 73 percent of the variance in vote intention is explained with the model. This high percentage of explained variance is not caused by the media variables in the models but by the control variables, particularly the lagged dependent variable; a model that includes only the lagged vote intention explains However, this does not mean that the effects of mediated leadership images do not matter.
To grasp their real-life impact, we plotted the predicted probabilities on vote intention for an average party in Figure 1. Although this observation may seem like a limited media effect, we must remember that we are analyzing the influence of the time between two waves of interviews in which the respondents participate.
The average time lag between two waves is three months. Thus, in the unlikely event that a leader received only positive discussions of his or her character traits for a full year, the model predicts an increase in the electoral support for his or her party of 4 percentage points. The electoral effect of negative coverage in terms of leadership images in the media is estimated to be substantially smaller.
However, on trait consistency, we find opposing effects than hypothesized. This effect remains consistent even when excluding parties one by one and when conducting an analysis with just this one trait rather than controlling for the others. We cannot provide an obvious explanation for this unexpected finding. In the Dutch multiparty system, which requires collaboration with political opponents, voters will usually understand that one has to compromise.
The unwillingness to do so to remain consistent is perhaps not valued positively by voters. To test the influence of campaign periods on the effects of mediated leadership images, we added interaction effects between the positive and negative mediated images and a dichotomous variable that measures whether it is a campaign period or a routine period model 3 in Table 1. Campaign period is operationalized very broadly as the period in between the announcement of new elections all cabinets in this time period ended prematurely and Election Day.
For this analysis, the short period before the election is excluded, as the corresponding routine period was not included in the content analysis. The table shows that the effects of leadership images on vote intentions differ substantively in both periods. During times of routine politics, there is a positive effect of positive images and a negative effect of negative images, as predicted.
The results during campaign periods, on the other hand, tell a somewhat different story. The positive effects of positive images increases during campaign periods, supporting our expectation that mediated leader effects are stronger during election campaigns. However, while negative images have a negative effect during routine times, this effect is not augmented during campaign periods.
Rather, the negative effect of negative images disappears during campaign periods. Figure 2 presents these effects graphically. The figure suggest that the effect of negative leadership images in the media would even turn positive during campaign times. However, while the unexpected positive interaction effect is robust against various model specifications, the marginal positive effect of negative news during campaign periods becomes insignificant in various robustness checks see below.
Thus, the evidence to conclude that politicians benefit from negative news during campaign periods is insufficiently robust, although the fact that negative news does not harm them during campaign periods is surprising in and of itself.
The campaign period of the election campaign is excluded in this model, as the corresponding routine time was not included in the content analysis.