Classify a study under the WWC attrition standard — attrition

Given a study's overall and differential attrition, classifies it as low or high attrition against the What Works Clearinghouse (WWC) attrition boundary (Standards Handbook Version 4.1, Table II.1). This is the classification that attrition() deliberately leaves to the user: attrition() reports the rates, attrition_boundary() applies the standard.

Usage

attrition_boundary(
  overall,
  differential,
  assumption = c("cautious", "optimistic")
)

Arguments

overall: Overall attrition, as a proportion in [0, 1] (as returned by attrition()).
differential: Differential attrition, as a proportion (the absolute difference in group attrition rates, as returned by attrition()).
assumption: Which boundary to apply: "cautious" (default) or "optimistic".

Value

A data frame, one row per input, with columns overall, differential, assumption, max_differential (the highest differential attrition still counted as low, as a proportion; NA where the overall rate is beyond the boundary), and attrition ("low" or "high").

Details

The WWC uses one of two boundaries. The cautious boundary is applied when the intervention could plausibly affect attrition (for example, a dropout prevention program); the optimistic boundary when it is unlikely to (for example, a first-grade reading program). The applicable boundary is set by the review protocol, not chosen post hoc; the default here is the more conservative cautious boundary.

References

What Works Clearinghouse (2020). Standards Handbook, Version 4.1, Table II.1. U.S. Department of Education.

Examples

# A study with 8% overall and 3 percentage-point differential attrition:
attrition_boundary(0.08, 0.03) # low under the cautious boundary
#>   overall differential assumption max_differential attrition
#> 1    0.08         0.03   cautious            0.063       low

# 30% overall, 6-point differential: high if cautious, low if optimistic
attrition_boundary(0.30, 0.06, "cautious")
#>   overall differential assumption max_differential attrition
#> 1     0.3         0.06   cautious            0.041      high
attrition_boundary(0.30, 0.06, "optimistic")
#>   overall differential assumption max_differential attrition
#> 1     0.3         0.06 optimistic            0.082       low

# Chained from attrition():
set.seed(1)
g <- rep(c(1, 0), each = 100)
kept <- rbinom(200, 1, ifelse(g == 1, 0.9, 0.82))
a <- attrition(g, kept)
attrition_boundary(a$attrition_overall, a$differential_attrition)
#>   overall differential assumption max_differential attrition
#> 1    0.12         0.12   cautious            0.062      high