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")
devtools::install_github("mattblackwell/prepost", build_vignettes = TRUE)
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) out <- pre_bounds( 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
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