Item Count Technique: Outcome Models

Function to conduct multivariate regression analyses of survey data with the item count technique, also known as the list experiment, using predicted responses from list experiments as predictors in outcome regression models.

ictreg.joint(formula, data = parent.frame(), treat = "treat", J,
  outcome = "outcome", outcome.reg = "logistic", constrained = FALSE,
  maxIter = 1000)

Arguments

formula	An object of class "formula": a symbolic description of the model to be fitted.
data	A data frame containing the variables in the model
treat	Name of treatment indicator as a string. For single sensitive item models, this refers to a binary indicator, and for multiple sensitive item models it refers to a multi-valued variable with zero representing the control condition. This can be an integer (with 0 for the control group) or a factor (with "control" for the control group).
J	Number of non-sensitive (control) survey items.
outcome	Name of outcome indicator as a string.
outcome.reg	Model for outcome regression. Options are "logistic" or "linear;" default is "logistic".
constrained	A logical value indicating whether the control group parameters are constrained to be equal. Default is FALSE.
maxIter	Maximum number of iterations for the Expectation-Maximization algorithm of the ML estimation. The default is 1000.

Value

ictreg.joint returns an object of class "ictreg.joint". The function summary is used to obtain a table of the results. The object ictreg.joint is a list that contains the following components.

par.treat

point estimate for effect of covariate on item count fitted on treatment group

se.treat

standard error for estimate of effect of covariate on item count fitted on treatment group

par.control

point estimate for effect of covariate on item count fitted on control group

se.control

standard error for estimate of effect of covariate on item count fitted on control group

par.outcome

point estimate for effect of covariate and sensitive item on outcome

se.outcome

standard error for estimate of effect of covariate and sensitive item on outcome

coef.names

variable names as defined in the data frame

constrained

call indicating whether the constrained model is used

call

the matched call

data

the data argument

outcome.reg

the outcome.reg argument

x

the design matrix

y

the response vector

treat

the vector indicating treatment status

J

Number of non-sensitive (control) survey items set by the user or detected.

treat.labels

a vector of the names used by the treat vector for the sensitive item or items. This is the names from the treat indicator if it is a factor, or the number of the item if it is numeric.

control.label

a vector of the names used by the treat vector for the control items. This is the names from the treat indicator if it is a factor, or the number of the item if it is numeric.

Details

This function allows the user to perform regression analysis on survey data with the item count technique, also known as the list experiment, using predicted responses from list experiments as predictors in outcome regression models.

References

Imai, Kosuke, Bethany Park, and Kenneth F. Greene. (2014) ``Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models.'' available at http://imai.princeton.edu/research/files/listExp.pdf

Examples

# NOT RUN {
data(mexico)
loyal <- mexico[mexico$mex.loyal == 1,]
notloyal <- mexico[mexico$mex.loyal == 0,]

## Logistic outcome regression
## (effect of vote-selling on turnout)
## This replicates Table 4 in Imai et al. 2014

loyalreg <- ictreg.joint(formula = mex.y.all ~ mex.male + mex.age + mex.age2 + mex.education +
                         mex.interest + mex.married +
                         mex.wealth + mex.urban + mex.havepropoganda + mex.concurrent, data = loyal,
                         treat = "mex.t", outcome = "mex.votecard", J = 3, constrained = TRUE,
                         outcome.reg = "logistic", maxIter = 1000)
summary(loyalreg)

## Linear outcome regression
## (effect of vote-selling on candidate approval)
## This replicates Table 5 in Imai et al. 2014

approvalreg <- ictreg.joint(formula = mex.y.all ~ mex.male + mex.age + mex.age2 +
                            mex.education +
                            mex.interest + mex.married +
                            mex.urban +
                            mex.cleanelections + mex.cleanelectionsmiss +
                            mex.havepropoganda +
                            mex.wealth + mex.northregion +
                            mex.centralregion + mex.metro + mex.pidpriw2 +
                            mex.pidpanw2 + mex.pidprdw2,
                            data = mexico, treat = "mex.t", outcome = "mex.epnapprove",
                            J = 3, constrained = TRUE,
                            outcome.reg = "linear", maxIter = 1000)


summary(approvalreg)
# }