Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) < https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.
outcomerate is a lightweight R package that implements the standard
outcome rates for surveys, as defined in the Standard
of the American Association of 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 analytically workflow by defining
all formulas as a collection of functions.
The latest development version is available via github:
Let’s say you try to survey 12 people. After finishing the fieldwork, you tabulate all your attempts into a table of disposition outcomes:
|R||Refusal and break-off||1|
|UH||Unknown if household||1|
Using this table, you may wish to report some of the common survey outcome rates, such as:
Most of these rates come under a number of variants, having definitions
that are standardized by AAPOR. The
outcomerate function lets your
calculate these rates seamlessly:
# load packagelibrary(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, UO = 1, NE = 1)# calculate rates, assuming 90% of unknown cases are elligbleoutcomerate(freq, e = eligibility_rate(freq))#> RR1 RR2 RR3 RR4 RR5 RR6 COOP1 COOP2 COOP3 COOP4 REF1 REF2#> 0.364 0.545 0.370 0.556 0.444 0.667 0.500 0.750 0.571 0.857 0.091 0.093#> REF3 CON1 CON2 CON3 LOC1 LOC2#> 0.111 0.727 0.741 0.889 0.818 0.833
Dispositions do not always come in a tabulated format. Survey analysts
often work with microdata directly, where each row represents an
outcomerate package allows you to obtain rates using
such a format as well:
# define a vector of dispositionsx <- c("I", "P", "I", "UO", "R", "I", "NC", "I", "O", "P", "UH")# calculate desired ratesoutcomerate(x, rate = c("RR2", "CON1"))#> RR2 CON1#> 0.55 0.73# obtain a weighted ratew <- c(rep(1.3, 6), rep(2.5, 5))outcomerate(x, weight = w, rate = c("RR2", "CON1"))#> RR2w CON1w#> 0.50 0.69
eligibility_rate()function added to estimate the proportion of eligible cases from the unknowns, based on the known ineligibles (
weightargument no longer accepts scalar inputs.
rate = NULLin the function parameters, the default behavior will be to return all possible rates given the other parameters specified.
outcomerate(), these are largely ignored, but are used by
fmatformula matrix object