ictreg.joint: Item Count Technique: Outcome Models

In list: Statistical Methods for the Item Count Technique and List Experiment

Item Count Technique: Outcome Models

Description

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.

Usage

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.

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.

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.

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)

## End(Not run)

list documentation built on May 29, 2024, 11:57 a.m.