Two-Dimensional Mean Respondent Factor Score and 95% CI, Mean
Unbalanced vs Balanced Data
Unbalanced vs Balanced Data
Unbalanced vs Balanced Data
Treatment Assignments, Contacts, and Turnout
Covariate Balance: Difference in Means Across Treatment and Control
Covariate Balance: Difference in Means Across Realized Contact and
Effect of Contact: Intent-to-Treat Effect and Treatment Effect
Coefficients, Effect of Contact on Vote
Effects of Home-Turf Precinct Canvassing on Vote
Coefficients from Meta-Analysis, Effect of Contact on Vote
Experimental Sessions
Tabulated Error by Type
Logit Coefficients on Error
Logit Coefficients on Candidate Vote
Respondents’ Partisan Majority Network and Vote
Respondents’ Presidential Votes by Ideology and Context
Paired T-Tests On Factor Scores Grouped by Vote and Majority Party
Paired T-Tests On Factor Scores Grouped by Vote and Majority Party
Robust Linear Regression Coefficients, Dependent Variable: Factor Scores139
Logit Coefficients, Dependent Variable: Vote for Republican Presiden-
Logit Coefficients on Balanced Data, Dependent Variable: Vote for Re-
Original Logit Coefficients, Dependent Variable: Vote for Republican
Respondent Party Identification and Discussant Party Identification
Summary of Variables
Respondent Relationship with Discussants
Treatment Effects
Respondent Party Identification and Discussant Party Identification Raw
Percent Improvement in Covariate Means From Matching
Respondent Party Change, November 04 to November 06
Summary of Variables
Discussion Network Disagreement: November 04 and November 06
Logit Coefficients, Effect of Disagreement in 04 on Change
Ordered Logit Coefficients, Effect of Change on Disagreement in 06
Summary
In the model, correlation in voter information together with a valence component in the voter's utility function leads to varying degrees of candidate polarization. Two possible behavioral assumptions for voters are examined, one where they communicate their competence signal truthfully and another where they communicate their most preferred candidate.
Introduction
Then, regardless of the voter's preferences, she is likely to have the same beliefs about the candidates as her friends and family. Political scientists are also interested in the shape of the social network as it relates to the flow of political information.
The Basic Model
Candidates and Voters
Candidates get ideal points xc where xA = 0 and xB = 1 and choose policy platforms pc in the ideology space [0,1]. Although much work has been done in the social network literature to estimate the likely structure of these networks (especially a small world, see Rogers and Jackson 2005 for a review of the literature), in this case we only consider this circle that nearest neighbors connected.
Signals and Communication
Preliminaries
The utility of the contribution from competence is γ2ni2ni+1 where the probability that candidate c is competent is P(θ = A) = 2ni2ni+1. Since this is an increasing function of ni, its expectation increases with panel S by the definition of first-order stochastic dominance.
Correlation Between Proximity and Votes
In the next phase, and in each subsequent phase, either i or j (but never both) communicates with a selector with which the other has already communicated, generating the claimed expressions. So given conclusion (a), which shows that pi(θ =A) and pj(θ = A) are less likely to be different the more signals are common to i and j, it is clear that the probability of them voting for the same candidate increases in the number of shared signals.
Candidate Polarization
Comparative Statics
In figure 2.3 the difference in probabilities of three different strategies is shown as a function of λ. However, the middle strategy (.50±λ) is played with decreasing probability, and the strategy representing the preferences of the median voter is played with increasing probability (.50).
Uniqueness
The Roles of Limited Information and Truth-Telling
Candidate Polarization with Limited Information
In Figure 2.5, the competence component of utility changes as a function of S + 1, the number of signals to which each voter has access. As the number of signals increases, the voter's expected benefit from competence converges to γ, as expected, since the beliefs about competence converge to the truth as the number of signals grows.
Truth Telling
If is an ideological voter, then the message mi will be based on i's ideological preference. If i is an indifferent voter, then the message mi is as likely to be based on i's ideological preference as it is to be based on her information.
Candidate Polarization with Most-Preferred Communication . 32
Thus, the only informational message that can be sent to j is if I is a competent voter, in which case her most preferred candidate is based on her signal. Here again it is possible to refer to previous work on the comparative statics of λ and candidate locations.
Conclusions and Future Research
In all cases, data are presented on voter behavior; the empirical analysis only discusses the actions that would occur as a result of the communication between voters. Each of these sections contains findings that are consistent with the assumptions about voter behavior in the theoretical model – that individuals are Bayesians (laboratory experiments section), that they are likely to be connected to individuals of similar types (survey data sections), and that they communicate about politics (survey data chapters).
Summary
Introduction
A significant body of research has documented that face-to-face mobilization is likely to be most effective (Green and Gerber 2001; Green and Gerber 2000; Green, Gerber and Nickerson 2003) - here we extend that research to examine the effects of mobilization using pollsters most likely to fit the Downsian "reliable source" mold and applying this treatment to a group of voters whose voting history is such that they are unlikely to participate without site contact.1 We expect significant effects of contacts however, as these opportunistic voters are also most likely to be persuaded to participate (Niven 2004), and we believe that they are most likely to be persuaded by someone from their environment. We hypothesize that, in accordance with the theoretical framework outlined in the first chapter, that individuals are more likely to be persuaded by an individual with whom they are socially connected.
SCOPE
SCOPE has spent years recruiting volunteers from the South Los Angeles area who are willing to walk the precincts before the election. One of the other unique aspects of this campaign is the population of the canvassers - the walkers are often residents of the areas where SCOPE is campaigning.
Experimental Setup and Data
Based on records kept by the reviewers, we can distinguish which of the individuals in the sample received either one or two contacts. Of the individuals designated to receive the second contact, 481 were successfully contacted, and 11 individuals were accidentally contacted.
Methods and Results
We observe a slightly higher (and statistically different) rate of contact for areas with native grass and a slightly higher (but not statistically significant) rate of participation for areas with native grass. In the zip code level model, we observe a positive and statistically significant effect of home search.
Conclusions
Here we have also seen a significant increase in participation as a result of home field research. This also suggests that home ground canvases are more convincing (and a quick look at the fixed foot effects table shows that it's not just that there were more convincing footmen walking on their home ground as well, but rather that pedestrians on their home turf are most effective) which is consistent with the theoretical model that individuals are better able to communicate with individuals with whom they are socially connected (and that within those social connections, they are likely to there is correlation of preferences).
Future Work
This increase (an additional 4% above the baseline increase) is dramatic – when presented with a graph showing that with additional contact between home and turf, turnout continues to increase, this provides compelling evidence for community mobilization campaigns. This argues in favor of the resurgence of "machine" politics as a way to increase voter turnout and suggests that the accumulation of social capital (community campaigning) is key to building political participation.
Tables and Figures
N in treatment group actually treated 5341 N in control group inadvertently treated 2 N who voted in treatment group 4324. N in treatment group actually treated 548 N in control group inadvertently treated 5.
Introduction
However, the focus of the Republican GOTV campaign was on connecting with already existing social ties (church congregations, neighborhood parties), in contrast to the focus of the Democratic GOTV campaign on recruiting recruits or students to the core states (Cornfield 2004). . Variance in the type of agitators makes it possible to compare the success of different types of agitators.
Data and Methods
These recruitment agencies are sometimes assigned to their home territory (hereinafter referred to as “home territory” recruitment) and some of the recruitment agencies are paid employees. Paid research was conducted in almost all districts, while only six districts were visited by a resident.
Results
Here too there is no effect of contact, but the coefficient on home fishing is positive and statistically significant. We find that this is positive (as hypothesized) but not statistically significant at traditional values.
Conclusions
Tables
Individuals are then divided by Paid District Contact and Home-Turf District Contact among those successfully contacted. We test a number of hypotheses about the amount of information the uninformed voters will glean from the results of the straw polls, including the convergence result presented in the McKelvey and Ordeshook paper.
Introduction
McKelvey and Ordeshook (1985) first realized that by giving a group of uninformed voters a historical partisanship primary (in an experimental setting, determined by which candidate was further along than the other) it would be possible for a series of primaries . polls to reveal the candidate's midpoint for the uninformed voter. We base our experimental setup on McKelvey and Ordeshook (1985), but make slight modifications that enable us to test whether or not uninformed voters are able to have converging beliefs about the true candidate after a series of polls.
Experimental Setup
In the experiment, the behavior of the candidates and the informed voters is completely controlled by the experimenter. In the second treatment, a small bonus is given for voting correctly in the polls prior to the election.
Data
The sessions are described in Table 6.1; the entire experiment produces a total of 5,136 instances where an uninformed voter is asked to "vote" in a poll or election. First, we produce a variable describing the distance between the voter and the candidate's midpoint.
Results
In addition, the coefficient for the variable describing the distance between the candidate center and point 50 is negative and statistically significant. We are also interested in whether the voter is located to the left or right of point 50 or not.
Conclusion
The largest coefficient is the one for the indicator of whether the voter is to the left of the candidate's center, as stated in the poll—this suggests that voters are in fact Bayesian. Evidence suggesting that the voters are Bayesian is that the Bayesian prediction model has the smallest number of errors and that the coefficient on the Bayesian indicator is the largest (compared to the other two possible models).
Tables
Written Instructions
You will be given limited information about the positions of the candidates and the other voters. Study to determine whether the discussant's party identification plays a role in determining the respondent's choice of party identification.
Summary
Introduction
The political behavior literature has shown that while it is possible to fit a traditional calculation of the voting model that considers only the ideological distance between the voter and the candidate (Downs 1957), an improvement of this model includes control variables such as the social class of the voter, income, or race which are also good predictors of a respondent's vote choice (Alvarez and Nagler 1998). A description of the data used and the construction of a latent utility score is then presented.
A Respondent and Her Network
The categories are described at the bottom of each bar: the respondent's ideology is listed first (liberal or conservative), followed by the majority party category (Democratic or Republican). However, we see a large shift in the percentage of Bush voters once the congressional district or discussion network changes majority party.
Data and Variable Construction
Voters
These questions include the respondent's marital status, income, age, race, gender, highest level of education attained, current employment status, length of residence in current community, whether or not the respondent has children, and whether the respondent has a good current financial standing. situation. The average individual in this data set is married, has some college education, has children, is female, has good financial status, is white, is ideologically moderate, has lived in her community for 27 years, has a moderate income, and is 47 years old.
Latent Utility and Thermometer Scores
Initially, factor scores are drawn only for those who live in a majority Democratic congressional district and who voted for the Democratic presidential candidate (blue), as well as for those who live in a majority Republican congressional district and who voted for the Republican presidential candidate (red ). The contextual variables have small coefficients, and only the percentage Democratic of the congressional district is statistically significant for the second factor.
Methods
Calculus of Voting with Contextual Effects
Respondents are asked to state their race, age, gender, marital status and a host of other variables that are likely to be correlated with their ideology and thus influence their voting choices. Including these variables has improved the ability of political scientists to predict an individual's vote choice beyond the simple vote count (Alvarez and Nagler 1998), and should be included as control variables in any analysis.
Matching with Multi-valued Treatment
Then the best observation in the second treatment is found to minimize the distance between the two observations in terms of the propensity vectors. 13 Graphs of the residual covariate values and 1/2 standard deviation intervals are included in the appendix in Figure 8.9 and Figure 8.10.
Results
The results from using the balanced data are that the influence on the debate network is much smaller and that the influence on the congressional district is slightly larger than seen using the unbalanced data. As before, the percentage of Democrats in the respondent's discussion network is significant and has the expected sign.
Conclusions
Previous research showed that there was a link between the partisanship of the respondent and her interlocutors. It is more likely that individuals choose their social context for many of the other variables controlled for in this analysis, and that some of these are in fact correlated but not entirely driven by partisanship.
Tables and Figures
Covariates: Ideological self-placement; Two-dimensional factor scores based on thermometer scoring questions: marriage, education, employment status, length of residence in the community, presence of children, gender, financial status, income, race, and age. All other covariates are included (congressional district returns; ideological self-placement; two-dimensional factor scores based on thermometer score questions; marriage, education, employment status, length of residence in the community, presence of children, gender, financial status, income, race, and age), but their coefficients are not shown here.
Appendix
Based on the survey data, each respondent must identify two debaters with whom she conducts political conversations. It is also seen that respondents are most influenced by debaters with whom they often talk and are geographically close.
Introduction
By controlling for respondent characteristics and the nature of the relationship with the debater using propensity score matching, each respondent's partisanship is affected by the debater's party identification choices. Another group of political scientists have considered party identification as a direct product of an individual's opinions about the current political framework (Downs 1957, Fiorina 1981, Green and Gerber 1998, Meier 1975).
Data
Based on each respondent's description of the partisanship of their discussants, each respondent is assigned a binary partisan discussion treatment variable. The primary method of communication with discussants was in person (66%), followed by telephone (22%), and 56% of discussants spoke to the respondent daily.
Method
However, the ordered logit is actually more appropriate because the respondent's party identification is not in fact a continuous variable. However, analysis calculated using the three-point party identifier (Democratic, Republican, and Independent) yields similar results.
Results
It appears that the identification of the discussant affects the choice of the respondent. A variable documenting the amount of relationship disagreement is then calculated by determining the distance between the respondent's three-point identification of the client and that of the average discussant.
Conclusions
Future Research
Appendix
Almost every day, At least once a week, At least once a month, Less than once a month.
Tables
Additional Tables
Summary
Introduction
This result contributes to the literature on stability and adoption of party identification and helps to tie party identification as part of social group membership. This chapter contributes to this literature by analyzing party identification change on a new data set that incorporates peer pressure.
Data
Methods and Hypotheses
Results
Conclusions
Appendix
Tables
