list (version 9.2.6)

ict.test: Item Count Technique

Description

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.

Usage

ict.test(
  y,
  treat,
  J = NA,
  alpha = 0.05,
  n.draws = 250000,
  gms = TRUE,
  pi.table = TRUE
)

Value

ict.test returns a numerical scalar with the Bonferroni-corrected minimum p-value of the statistical test.

Arguments

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.

Author

Graeme Blair, UCLA, and Kosuke Imai, Princeton University,

Details

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.

References

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

See Also

ictreg for list experiment regression based on the assumption of no design effect

Examples

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)