| campaign_wide {rbw} | R Documentation |
Wide-format Data on Negative Campaign Advertising in US Senate and Gubernatorial Elections
Description
A dataset containing 32 variables and 113 unit records from Blackwell (2013).
Usage
campaign_wide
Format
A data frame with 113 rows and 26 columns:
- demName
name of the Democratic candidate
- camp.length
length of the candidate's campaign (in weeks)
- deminc
whether the candidate was an incumbent.
- base.poll
Democratic share in the baseline polls
- base.und
share of undecided voters in the baseline polls
- office
type of office in contest. 0: governor; 1: senator
- demprcnt
Democratic share of the two-party vote in the election
- year
year of the election
- state
state of the election
- id
candidate id
- dem.polls_1
Democratic share in week 1 polls
- dem.polls_2
Democratic share in week 2 polls
- dem.polls_3
Democratic share in week 3 polls
- dem.polls_4
Democratic share in week 4 polls
- dem.polls_5
Democratic share in week 5 polls
- d.gone.neg_1
whether the candidate went negative in week 1
- d.gone.neg_2
whether the candidate went negative in week 2
- d.gone.neg_3
whether the candidate went negative in week 3
- d.gone.neg_4
whether the candidate went negative in week 4
- d.gone.neg_5
whether the candidate went negative in week 5
- neg.dem_1
the proportion of advertisements that were negative in week 1 polls
- neg.dem_2
the proportion of advertisements that were negative in week 2 polls
- neg.dem_3
the proportion of advertisements that were negative in week 3 polls
- neg.dem_4
the proportion of advertisements that were negative in week 4 polls
- neg.dem_5
the proportion of advertisements that were negative in week 5 polls
- undother_1
share of undecided voters in week 1 polls
- undother_2
share of undecided voters in week 2 polls
- undother_3
share of undecided voters in week 3 polls
- undother_4
share of undecided voters in week 4 polls
- undother_5
share of undecided voters in week 5 polls
- cum_neg
the total number of campaign-weeks in which a candidate went negative
- ave_neg
the average proportion of advertisements that were negative over the final five weeks of the campaign multiplied by ten
References
Blackwell, Matthew. 2013. A Framework for Dynamic Causal Inference in Political Science. American Journal of Political Science 57(2): 504-619.