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baselinr

R-CMD-check Lifecycle: experimental

baselinr builds report-ready baseline equivalence tables for impact evaluations in education research, following the conventions of the What Works Clearinghouse (WWC). Given a treatment indicator and a set of covariates, it reports the appropriate standardized effect size — Hedges’ g for continuous covariates, the Cox index for binary ones — and the WWC equivalence category for each.

It is a thin, education-specific reporting layer. For general-purpose covariate balance assessment, see cobalt; baselinr focuses narrowly on the WWC equivalence categories that education evaluation reports are required to state.

Installation

# install.packages("remotes")
remotes::install_github("zl1212-ship-it/baselinr")

Example

library(baselinr)

study <- data.frame(
  treat   = c(1, 1, 1, 0, 0, 0),
  pretest = c(5, 6, 7, 4, 5, 6), # continuous -> Hedges' g
  female  = c(1, 0, 1, 0, 0, 1)  # binary     -> Cox index
)

knitr::kable(baseline_equivalence(study, treatment = "treat"), digits = 3)
covariate type n_treatment n_comparison mean_treatment mean_comparison sd_treatment sd_comparison effect_size wwc_category
pretest continuous 3 3 6.000 5.000 1.000 1.000 0.80 not_satisfied
female binary 3 3 0.667 0.333 0.577 0.577 0.84 not_satisfied

The WWC categories are:

Effect size (absolute) Category Meaning
<= 0.05 satisfied Baseline equivalence holds.
0.050.25 satisfied_with_adjustment Holds only if the covariate is adjusted for in the impact model.
> 0.25 not_satisfied Cannot establish equivalence.

Visualise and format

love_plot() shows the standardized effect size of every covariate against the WWC thresholds (requires ggplot2):

love_plot(baseline_equivalence(study, treatment = "treat"))

Love plot of standardized effect sizes by covariate

gt_baseline() renders the same table as a formatted gt table for reports and Quarto/HTML (requires gt):

gt_baseline(baseline_equivalence(study, treatment = "treat"))

Scope

Continuous covariates use Hedges’ g (with the WWC small-sample correction); binary covariates (numeric 0/1, logical, or two-level factor) use the WWC Cox index. wwc_summary() collapses the table into an overall verdict, and attrition() reports overall and differential attrition. See NEWS.md for the roadmap.

License

MIT © Yuxia Liang

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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