campaign_wide: Wide-format Data on Negative Campaign Advertising in US...

In rbw: Residual Balancing Weights for Marginal Structural Models

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.

rbw documentation built on March 18, 2022, 5:35 p.m.