A two-wave survey experiment on Amazon Mechanical Turk, replicating Levendusky and Malhotra (2016) and reported as the application in Coppock, Gerber, Green, and Kern (2017). Subjects read a news article describing the electorate either as sharply divided (the polarized condition) or as focused on common ground (the moderate condition), or read nothing on the topic (the placebo condition). Outcomes were measured immediately in Wave 1 and again in a Wave 2 survey ten days later.
Format
A data frame with 2,955 rows and 12 columns:
- Z_lev
Treatment assignment:
Placebo,Moderate, orPolarized.- Z1
Polarized (1) versus moderate (0);
NAin the placebo condition. The contrast analyzed in the paper.- Z2
Polarized (1) versus placebo (0);
NAin the moderate condition.- Z3
Moderate (1) versus placebo (0);
NAin the polarized condition.- R1
Responded in the Wave 2 initial sample.
- Attempt
Selected for the follow-up sample and offered the higher incentive.
- R2
Responded to the follow-up attempt.
- pid_3_recoded
Party identification:
Dem,Ind, orRep. The poststratification variable used in Table 3.- L_dif
Perceived polarization at Wave 1, from 0 to 6.
- L_dif_w2
Perceived polarization at Wave 2, from 0 to 6. The dependent variable throughout, and the one with missing values.
- L_ex
Perceived extremity at Wave 1, from 0 to 3.
- L_ex_w2
Perceived extremity at Wave 2, from 0 to 3.
Perceived polarization is built from a battery of policy questions. Subjects gave their own view and then guessed how a typical Democratic voter and a typical Republican voter would answer. The outcome is the average absolute difference between the two guesses.
Source
Coppock, Alexander, Alan S. Gerber, Donald P. Green, and Holger L. Kern (2016). Replication Data for: Combining double sampling and bounds to address non-ignorable missing outcomes in randomized experiments. Harvard Dataverse. doi:10.7910/DVN/AQB4MP
Details
Wave 2 is where the attrition happens, and it is what makes the data useful here. Of the 1,980 subjects in the polarized and moderate conditions, 1,444 responded in Wave 2. Exactly 50 nonrespondents were then drawn at random from each condition and offered $4.00 rather than the original $1.00 to participate. Of those 100 subjects, 72 completed the survey. The follow-up sample is small, and that is the point: because it is a random sample of the nonrespondents, the outcomes it recovers stand in for the outcomes of every nonrespondent, and the worst-case bounds narrow sharply.
The placebo condition is not analyzed in the paper. Every published result
uses subset(levendusky, !is.na(Z1)), the polarized-versus-moderate
contrast.
References
Coppock, Alexander, Alan S. Gerber, Donald P. Green, and Holger L. Kern (2017). Combining Double Sampling and Bounds to Address Nonignorable Missing Outcomes in Randomized Experiments. Political Analysis 25(2):188-206. doi:10.1017/pan.2016.6
Levendusky, Matthew, and Neil Malhotra (2016). Does Media Coverage of Partisan Polarization Affect Political Attitudes? Political Communication 33(2):283-301.
Examples
dat <- subset(levendusky, !is.na(Z1))
# Table 1: attrition by condition
with(dat, table(Z_lev, R1))
#> R1
#> Z_lev 0 1
#> Placebo 0 0
#> Moderate 264 731
#> Polarized 272 713
# Table 3, column 2
estimator_ds(L_dif_w2, Z1, R1, Attempt, R2, minY = 0, maxY = 6, data = dat)
#> ci_lower ci_upper low_est upp_est low_var upp_var
#> -0.52830967 0.74517483 -0.34174538 0.57181573 0.01286479 0.01110807