This package contains code and sample data to implement the non-parametric bounds and Bayesian methods for assessing priming and post-treatment bias in experimental studies under various assumptions.
To get started, please see the article that developed these methods:
## Install developer version
## install.packages("devtools")
::install_github("mattblackwell/prepost", build_vignettes = TRUE) devtools
Both the nonparametric and Bayesian estimators all have prefixes that indicate what type of experimental design being used.
pre_
functions can analyze data from a pre-test
design where the moderator is measured pre-treatment.post_
functions can analyze data from a
post-test design where the moderator is measured
post-treatment.prepost_
functions can analyze data from a
random placement design, in which the moderator is
randomly assigned to be measured before or after treatment.Most functions can be specified with a formula to identify the outcome and treatment and another one-sided formula for the moderator:
library(prepost)
data(delponte)
<- pre_bounds(
out formula = angry_bin ~ t_commonality,
data = delponte,
moderator = ~ itaid_bin
) out
## $lower
##
## -0.5923203
##
## $upper
##
## 0.3221525
##
## $ci_lower
## [1] -0.6875343
##
## $ci_upper
## [1] 0.4035053
##
## $pre_est
## [1] -0.2701678