comp.listEndorse {list} | R Documentation |
Comparing List and Endorsement Experiment Data
Description
Function to conduct a statistical test with the null hypothesis that there is no difference between the correlation coefficients between list experiment and endorsement experiment data.
Usage
comp.listEndorse(
y.endorse,
y.list,
treat,
n.draws = 10000,
alpha = 0.05,
endorse.mean = FALSE,
method = "pearson"
)
Arguments
y.endorse |
A numerical matrix containing the response data for the endorsement experiment. |
y.list |
A numerical vector containing the response data for a list experiment. |
treat |
A numerical vector containing the binary treatment status for the experiments. The treatment assignment must be the same for both experiments to compare across experiments. |
n.draws |
Number of Monte Carlo draws. |
alpha |
Confidence level for the statistical test. |
endorse.mean |
A logical value indicating whether the mean endorsement experiment response is taken across questions. |
method |
The method for calculating the correlation, either Pearson's rho or Kendall's tau. |
Details
This function allows the user to calculate the correlation between list and endorsement experiment data within the control group and the treatment group, and to conduct a statistical test with the null hypothesis of no difference between the two correlation coefficients.
Value
comp.listEndorse
returns a list with four elements: the
correlation statistic (rho or tau) for the treatment group as
cor.treat
, the correlation statistic for the control group as
cor.control
, the p.value for the statistical test comparing the two
correlation statistics as p.value
, and the bootstrapped confidence
interval of the difference as ci
.
Author(s)
Graeme Blair, UCLA, graeme.blair@ucla.edu and Kosuke Imai, Princeton University, kimai@princeton.edu
References
Blair, Graeme, Jason Lyall and Kosuke Imai. (2014) “Comparing and Combining List and Experiments: Evidence from Afghanistan." American Journal of Political Science. available at http://imai.princeton.edu/research/comp.html