Skip to contents

Survey Eligibility Rate

Source: R/eligibility_rate.R
eligibility_rate.Rd

Provides an estimate for the proportion of cases of unknown eligibility that are eligible, as described by (Valliant et al. 2013) . The rate is typically (but not necessarily) calculated on the screener data or other sources depending on the type of survey, and approaches to calculating 'e' may therefore differ from one survey to the next.

Usage

eligibility_rate(x, weight = NULL)

Arguments

x

a character vector of disposition outcomes (I, P, R, NC, O, UH, UR, UO, or NE). Alternatively, a named vector/table of (weighted) disposition counts.

weight

an optional numeric vector that specifies the weight of each element in 'x' if x is a character vector. For probability samples, these will normally be base weights (inverse selection probabilities). If none is provided (the default), an unweighted estimate is returned. Weights cannot be supplied with an already-aggregated named vector or table.

Value

A named numeric vector of length one containing the estimated eligibility rate, named ELR.

Details

The present proportional-allocation implementation follows the default used in the Excel-based AAPOR Outcome Rate Calculator (Version 5.1, April 2023), on the basis of known ineligibles being coded as "NE". It is one accepted estimator of e; researchers should use better design-specific information when available. This function returns one scalar estimate. Separate estimates can be supplied directly to outcomerate() as a named vector such as c(UH = 0.4, UR = 0.7, UO = 0.2); they cannot be inferred from the package's aggregate NE count alone. See (Amaya et al. 2025) for category-specific estimation guidance.

The eligibility rate (ELR) is defined as

  • ELR = (I + P + R + NC + O) / (I + P + R + NC + O + NE)

References

The American Association for Public Opinion Research (2023). “Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys.” https://aapor.org/wp-content/uploads/2024/03/Standards-Definitions-10th-edition.pdf. Amaya A, Marlar J, English N (2025). “Estimating the Eligibility Status of Cases with Unknown Eligibility.” American Association for Public Opinion Research. https://aapor.org/wp-content/uploads/2025/10/Estimating-the-Eligibility-Status-of-Cases-with-Unknown-Eligibility_FINAL.pdf.

Valliant R, Dever JA, Kreuter F (2013). Practical Tools for Designing and Weighting Survey Samples, Statistics for Social and Behavioral Sciences. Springer New York.

See also

outcomerate

Examples

# load the outcomerate package
library(outcomerate)

# Create a vector of survey dispositions
#
# I  = Complete interview
# P  = Partial interview
# R  = Refusal and break-off
# NC = Non-contact
# O  = Other eligible non-interview (2.30, 2.90)
# UH = Unknown if household/occupied housing unit (3.10)
# UR = Unknown if sampled unit is eligible/housing unit contains an eligible
#      respondent (3.20)
# UO = Unknown, other (3.90)
# NE = Not eligible (4.0)
x <- c("I", "P", "I", "NE", "NC", "UH", "I", "R", "UR", "UO", "I", "O",
       "P", "I")

# estimate the eligibility rate
eligibility_rate(x)
#>       ELR 
#> 0.9090909 

# calculate a weighted rate using illustrative base weights
w <- seq(0.5, 1.8, length.out = length(x))
eligibility_rate(x, weight = w)
#>       ELR 
#> 0.9354839 

# alternatively, provide input as counts
freq <- c(I = 6, P = 2, NC = 3, NE = 1)
eligibility_rate(freq)
#>       ELR 
#> 0.9166667