baseline_equivalence: Baseline equivalence table for an impact evaluation

In baselinr: WWC-Aligned Baseline Equivalence Tables for Education Impact Evaluations

Baseline equivalence table for an impact evaluation

Description

Builds a report-ready baseline-equivalence table for a set of covariates, reporting group sample sizes, summaries, the appropriate standardized effect size, and the corresponding What Works Clearinghouse (WWC) equivalence category for each covariate. Continuous covariates use Hedges' g; binary covariates use the Cox index.

Usage

baseline_equivalence(data, treatment, covariates = NULL)

Arguments

data	A data frame.
treatment	String naming the column in data that identifies group membership. Must have exactly two unique non-missing values (see hedges_g() for how the treatment group is determined).
covariates	Character vector of column names to evaluate. Defaults to all numeric, logical, and factor columns in data other than treatment.

Details

A covariate with exactly two unique non-missing values is treated as binary; any other numeric covariate is treated as continuous. A non-numeric covariate with more than two categories is not supported and raises an error.

Value

A data frame with one row per covariate and the columns: covariate; type ("continuous" or "binary"); n_treatment, n_comparison; mean_treatment, mean_comparison (group means for continuous covariates, event proportions for binary ones); sd_treatment, sd_comparison; effect_size (Hedges' g or Cox index, per type); and wwc_category.

References

What Works Clearinghouse (2022). Procedures Handbook (Version 5.0). U.S. Department of Education.

Examples

df <- data.frame(
  treat = c(1, 1, 1, 0, 0, 0),
  pretest = c(5, 6, 7, 4, 5, 6),
  female = c(1, 0, 1, 0, 0, 1)
)
baseline_equivalence(df, treatment = "treat")

baselinr documentation built on July 8, 2026, 9:07 a.m.