Summary Method for the Item Count Technique

Function to summarize results from list experiment regression based on the ictreg() function, and to produce proportions of liars estimates.

# S3 method for ictreg
summary(object, boundary.proportions = FALSE,
  n.draws = 10000, ...)

Arguments

object	Object of class inheriting from "ictreg"
boundary.proportions	A switch indicating whether, for models with ceiling effects, floor effects, or both (indicated by the floor = TRUE, ceiling = TRUE options in ictreg), the conditional probability of lying and the population proportions of liars are calculated.
n.draws	For quasi-Bayesian approximation based predictions, specify the number of Monte Carlo draws.
...	further arguments to be passed to or from other methods.

Details

predict.ictreg produces a summary of the results from an ictreg object. It displays the coefficients, standard errors, and fit statistics for any model from ictreg. predict.ictreg also produces estimates of the conditional probability of lying and of the population proportion of liars for boundary models from ictreg() if ceiling = TRUE or floor = TRUE.

The conditional probability of lying for the ceiling model is the probability that a respondent with true affirmative views of all the sensitive and non-sensitive items lies and responds negatively to the sensitive item. The conditional probability for the floor model is the probability that a respondent lies to conceal her true affirmative views of the sensitive item when she also holds true negative views of all the non-sensitive items. In both cases, the respondent may believe her privacy is not protected, so may conceal her true affirmative views of the sensitive item.

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

Imai, Kosuke. (2011) ``Multivariate Regression Analysis for the Item Count Technique.'' Journal of the American Statistical Association, Vol. 106, No. 494 (June), pp. 407-416. available at http://imai.princeton.edu/research/list.html

See also

ictreg for model fitting

Examples

data(race)
# NOT RUN {
# Fit standard design ML model with ceiling effects
# Replicates Table 7 Columns 3-4 in Blair and Imai (2012)

ceiling.results <- ictreg(y ~ age + college + male + south, treat = "treat",
                          J = 3, data = affirm, method = "ml", fit.start = "nls",
                          ceiling = TRUE, ceiling.fit = "bayesglm",
                          ceiling.formula = ~ age + college + male + south)

# Summarize fit object and generate conditional probability 
# of ceiling liars the population proportion of ceiling liars,
# both with standard errors.
# Replicates Table 7 Columns 3-4 last row in Blair and Imai (2012)

summary(ceiling.results, boundary.proportions = TRUE)
# }