Add Estimates of Leader Willingness to Use Force to Leader-Year Data
Source:R/add_lwuf.R
add_lwuf.Rd
add_lwuf()
allows you to add estimates of leader
willingness to use force to leader-year data or leader-dyad-year data.
Arguments
- data
a leader-year or leader dyad-year data frame as generated in peacesciencer
- keep
an optional argument, specified as a character vector, of the variables from the
lwuf
data frame the user wants in their data. See thelwuf
data and its documentation for more. If the argument is unspecified, the function will return all measures of leader willingness to use force as generated by Carter and Smith.
Value
add_lwuf()
takes a leader-year or leader-dyad-year data
frame and adds estimates of leader willingness to use force, as
generated by Carter and Smith (2020).
Details
See lwuf
for more information, but I'll copy-paste it here
too.
The letter published by Carter and Smith (2020) contains more information as to what these thetas refer. The "M1" theta is a variation of the standard Rasch model from the boilerplate information in the LEAD data. The authors consider this to be "theoretically relevant" or "risk-related" as these all refer to conflict or risk-taking. The "M2" theta expands on "M1" by including political orientation and psychological characteristics. "M3" and "M4" expand on "M1" and "M2" by considering all 36 variables in the LEAD data.
The authors construct and include all these measures, though their analyses
suggest "M2" is the best-performing measure. You should probably consider
using theta2_mean
as your default estimate of leader willingness
to use force in leader-year analyses.
References
Carter, Jeff and Charles E. Smith, Jr. 2020. "A Framework for Measuring Leaders' Willingness to Use Force." American Political Science Review 114(4): 1352--1358.
Examples
# \donttest{
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
create_leaderyears() %>% add_lwuf()
#> Joining with `by = join_by(obsid)`
#> # A tibble: 17,686 × 15
#> obsid leader gwcode gender leaderage year yrinoffice theta1_mean theta1_sd
#> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 USA-18… Grant 2 M 47 1869 1 1.01 0.509
#> 2 USA-18… Grant 2 M 48 1870 2 1.01 0.509
#> 3 USA-18… Grant 2 M 49 1871 3 1.01 0.509
#> 4 USA-18… Grant 2 M 50 1872 4 1.01 0.509
#> 5 USA-18… Grant 2 M 51 1873 5 1.01 0.509
#> 6 USA-18… Grant 2 M 52 1874 6 1.01 0.509
#> 7 USA-18… Grant 2 M 53 1875 7 1.01 0.509
#> 8 USA-18… Grant 2 M 54 1876 8 1.01 0.509
#> 9 USA-18… Grant 2 M 55 1877 9 1.01 0.509
#> 10 USA-18… Hayes 2 M 55 1877 1 0.464 0.545
#> # ℹ 17,676 more rows
#> # ℹ 6 more variables: theta2_mean <dbl>, theta2_sd <dbl>, theta3_mean <dbl>,
#> # theta3_sd <dbl>, theta4_mean <dbl>, theta4_sd <dbl>
# }