About
resquin (response quality indicators) provides functions to calculate survey data quality indicators to help identifying low-quality responses (Bhaktha, Silber, and Lechner 2024; Curran 2016; Vaerenbergh and Thomas 2013). resp_styles() and resp_distributions() provide response quality indicators geared towards multi-item scales or matrix questions. Both multi-item scales and matrix questions present survey respondents with multiple questions which have the same response format, meaning the same number and labeling of response options.
At the moment, resquin provides two functions:
-
resp_styles()- Calculates response style indicators (e.g. extreme response style or middle response style). -
resp_distributions()- Calculates response distribution indicators (e.g. intra-individual mean and standard deviation over a set of survey questions).
Two more functions are planned:
-
resp_patterns- Calculates response pattern indicators (e.g. straightlining) -
resp_times- Calculates response time indicators (e.g. median item response time)
For information on how to use resquin see the vignettes Getting started with resquin and resquin in practice.
resquin is still under active development. Please use github issues to file questions and bug reports or send them directly to matthias.roth@gesis.org. We are happy to receive feedback!
Installation
resquin is available via CRAN and github. To install resquin from CRAN or github, you can use one of the following commands:
# Install resquin via CRAN
install.packages("resquin")
# Install development version of resquin with devtools
devtools::install_github("https://github.com/MatRoth/resquin")
# Install development version of resquin with pak
pak::pak("https://github.com/MatRoth/resquin")Getting started
To use resquin, supply a data frame containing survey responses in wide format to either resp_styles() or resp_distributions().
# load resquin
library(resquin)
# A test data set with three items and ten respondents
testdata <- data.frame(
var_a = c(1,4,3,5,3,2,3,1,3,NA),
var_b = c(2,5,2,3,4,1,NA,2,NA,NA),
var_c = c(1,2,3,NA,3,4,4,5,NA,NA))
testdata
#> var_a var_b var_c
#> 1 1 2 1
#> 2 4 5 2
#> 3 3 2 3
#> 4 5 3 NA
#> 5 3 4 3
#> 6 2 1 4
#> 7 3 NA 4
#> 8 1 2 5
#> 9 3 NA NA
#> 10 NA NA NA
# Calculate response style indicators per respondent
resp_styles(x = testdata,
scale_min = 1,
scale_max = 5) |> # Specify scale minimum and maximum
round(2)
#> MRS ARS DRS ERS NERS
#> 1 0.00 0.00 1.00 0.67 0.33
#> 2 0.00 0.67 0.33 0.33 0.67
#> 3 0.67 0.00 0.33 0.00 1.00
#> 4 NA NA NA NA NA
#> 5 0.67 0.33 0.00 0.00 1.00
#> 6 0.00 0.33 0.67 0.33 0.67
#> 7 NA NA NA NA NA
#> 8 0.00 0.33 0.67 0.67 0.33
#> 9 NA NA NA NA NA
#> 10 NA NA NA NA NA
# Calculate response distribution indicators per respondent
resp_distributions(x = testdata) |>
round(2)
#> n_na prop_na ii_mean ii_sd ii_median mahal
#> 1 0 0.00 1.33 0.58 1 2.04
#> 2 0 0.00 3.67 1.53 4 1.60
#> 3 0 0.00 2.67 0.58 3 1.38
#> 4 1 0.33 NA NA NA NA
#> 5 0 0.00 3.33 0.58 3 0.97
#> 6 0 0.00 2.33 1.53 2 1.38
#> 7 1 0.33 NA NA NA NA
#> 8 0 0.00 2.67 2.08 2 1.88
#> 9 2 0.67 NA NA NA NA
#> 10 3 1.00 NA NA NA NAFor a more information on how to use resquin see the vignettes Getting started with resquin and resquin in practice.