ict.test | R Documentation |
Function to conduct a statistical test with the null hypothesis that there is no "design effect" in a list experiment, a failure of the experiment.
ict.test(
y,
treat,
J = NA,
alpha = 0.05,
n.draws = 250000,
gms = TRUE,
pi.table = TRUE
)
y |
A numerical vector containing the response data for a list experiment. |
treat |
A numerical vector containing the binary treatment status for a list experiment. |
J |
Number of non-sensitive (control) survey items. |
alpha |
Confidence level for the statistical test. |
n.draws |
Number of Monte Carlo draws. |
gms |
A logical value indicating whether the generalized moment selection procedure should be used. |
pi.table |
A logical value indicating whether a table of estimated proportions of respondent types with standard errors is displayed. |
This function allows the user to perform a statistical test on data from a list experiment or item count technique with the null hypothesis of no design effect. A design effect occurs when an individual's response to the non-sensitive items changes depending upon the respondent's treatment status.
ict.test
returns a numerical scalar with the
Bonferroni-corrected minimum p-value of the statistical test.
Graeme Blair, UCLA, graeme.blair@ucla.edu and Kosuke Imai, Princeton University, kimai@princeton.edu
Blair, Graeme and Kosuke Imai. (2012) “Statistical Analysis of List Experiments." Political Analysis, Vol. 20, No 1 (Winter). available at http://imai.princeton.edu/research/listP.html
ictreg
for list experiment regression based on the
assumption of no design effect
data(affirm)
data(race)
# Conduct test with null hypothesis that there is no design effect
# Replicates results on Blair and Imai (2012) pg. 69
test.value.affirm <- ict.test(affirm$y, affirm$treat, J = 3, gms = TRUE)
print(test.value.affirm)
test.value.race <- ict.test(race$y, race$treat, J = 3, gms = TRUE)
print(test.value.race)
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