Function to summarize results from list experiment regression based on the ictreg() function, and to produce proportions of liars estimates.
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Object of class inheriting from "ictreg"
A switch indicating whether, for models with
ceiling effects, floor effects, or both (indicated by the
For quasi-Bayesian approximation based predictions, specify the number of Monte Carlo draws.
further arguments to be passed to or from other methods.
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
predict.ictreg also produces estimates of the conditional probability
of lying and of the population proportion of liars for boundary models from
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.
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
ictreg for model fitting
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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) ## End(Not run)
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