ajps: Gaines and Kuklinski (2011) AJPS data

In GK2011: Gaines and Kuklinski (2011) Estimators for Hybrid Experiments

Description

Subset of data from Gaines and Kuklinski (2011)

Usage

ajps

Format

tr

The treatment indicator, where 1=treatment, 2=control, 3=chose treatment, 4=chose control.

therm.obama

A “feeling thermometer” toward John McCain.

therm.mccain

A “feeling thermometer” toward Barack Obama.

pid

An indicator of party identification, where -1=Republican, 0=Independent, 1=Democrat.

Details

This dataset contains a subset of variables, extracted from the dataset used by Gaines and Kuklinski (2011).

Source

Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.

See Also

estimate

Examples

data(ajps)

# replicate Gaines and Kuklinski (2011) Table 2
pmean <- function(x) sprintf("%0.1f", mean(x))
cbind(
  # Democrats
  aggregate(cbind(therm.mccain, therm.obama) ~ tr, 
            data = ajps[ajps$pid == 1, ], FUN = pmean)[, 1:3],
  n_dem = aggregate(therm.obama ~ tr, 
                    data = ajps[ajps$pid == 1, ], FUN = length)[, 2],
  # Republicans
  aggregate(cbind(therm.mccain, therm.obama) ~ tr, 
            data = ajps[ajps$pid == -1, ], FUN = pmean)[, 2:3],
  n_rep = aggregate(therm.obama ~ tr, 
                    data = ajps[ajps$pid == -1, ], FUN = length)[, 2]
)

# effects for McCain among Democrats
with(ajps[ajps$pid == 1, ], {
  estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.mccain)
})
# effects for McCain among Republicans
with(ajps[ajps$pid == -1, ], {
  estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.mccain)
})

# effects for Obama among Democrats
with(ajps[ajps$pid == 1, ], {
  estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.obama)
})
# effects for Obama among Republicans
with(ajps[ajps$pid == -1, ], {
  estimate(rand = tr %in% 1:2, tr = tr %in% c(1,3), y = therm.obama)
})

GK2011 documentation built on May 1, 2019, 10:32 p.m.