outcomerate

outcomerate is a lightweight R package that implements the standard outcome rates for surveys, as defined in the Standard Definitions, 10th edition of the American Association for Public Opinion Research (AAPOR).

Although the mathematical formulas are straightforward, it can get tedious and repetitive calculating all the rates by hand, especially for sub-groups of your study. The formulas are similar to one another and so it is also dangerously easy to make a clerical mistake. The outcomerate package simplifies the analytical workflow by defining all formulas as a collection of functions.

The 10th edition separates code 3.20 from UO under the aggregate symbol UR. Legacy UO data remain supported and produce the same rates when the package’s scalar eligibility estimate e is used; newly coded 3.20 cases should use UR.

Installation

Install the package from CRAN:

install.packages("outcomerate")

Alternatively, install the latest development version via github:

#install.packages("devtools")
devtools::install_github("ropensci/outcomerate")

Example

Let’s say you draw a sample of 13 cases. After finishing the fieldwork, you tabulate all your attempts into a table of disposition outcomes:

Using this table, you may wish to report some of the common survey outcome rates, such as:

• Response Rate: The proportion of your sample that results in an interview.
• Cooperation Rate: The proportion of eligible units ever contacted that result in an interview; the package implements AAPOR’s household-level formulas.
• Refusal Rate: The proportion of your sample that refused to participate.
• Contact Rate: The proportion of sampled cases where a responsible member of the housing unit is reached; the package implements AAPOR’s household-level formulas.
• Location Rate: The proportion of cases that you manage to locate. This is a package extension, not an AAPOR-defined rate, and its interpretation depends on how located cases are coded.

Most of these rates come under a number of variants, having definitions that are standardized by AAPOR. The outcomerate function lets you calculate these rates seamlessly:

# load package
library(outcomerate)

# set counts per disposition code (needs to be a named vector)
freq <- c(I = 4, P = 2, R = 1, NC = 1, O = 1,
          UH = 1, UR = 1, UO = 1, NE = 1)

# calculate rates, assuming 90% of unknown cases are eligible
outcomerate(freq, e = eligibility_rate(freq))
#>   RR1   RR2   RR3   RR4   RR5   RR6 COOP1 COOP2 COOP3 COOP4  REF1  REF2  REF3 
#> 0.333 0.500 0.342 0.513 0.444 0.667 0.500 0.750 0.571 0.857 0.083 0.085 0.111
#>  CON1  CON2  CON3  LOC1  LOC2 
#> 0.667 0.684 0.889 0.750 0.769

When the available evidence supports different eligibility estimates for the unknown categories, pass them as a named vector. Each value is the probability that a case in that category is eligible:

e_by_class <- c(UH = 0.4, UR = 0.7, UO = 0.2)
outcomerate(freq, e = e_by_class, rate = c("RR3", "REF2", "CON2"))
#>   RR3  REF2  CON2
#> 0.388 0.097 0.777

A category may be omitted from a non-scalar e only when its aggregate count is zero (after weighting, when weights are supplied). A length-one value—including the result of eligibility_rate()—keeps the original behavior and applies to every unknown category.

Dispositions do not always come in a tabulated format. Survey analysts often work with microdata directly, where each row represents a sampled case. The outcomerate package allows you to obtain rates using such a format as well:

# define a vector of dispositions
x <- c("I", "P", "I", "UO", "R", "I", "NC", "I", "O", "P", "UH", "UR")

# calculate desired rates
outcomerate(x, rate = c("RR2", "CON1"))
#>  RR2 CON1 
#> 0.50 0.67

# obtain a weighted rate using illustrative base weights
w <- c(rep(1.3, 6), rep(2.5, 6))
outcomerate(x, weight = w, rate = c("RR2", "CON1"))
#>  RR2w CON1w 
#>  0.45  0.61