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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(), resp_distributions(),
resp_nondifferentiation() and resp_patterns()
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 four 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).resp_nondifferentiation() - Calculates response
nondifferentiation indicators. Nondifferentiation indicators primarily
measure straightlining. The indicators differ in how straightlining is
operationalized.resp_patterns() - Calculates response pattern
indicators (e.g. long string analysis).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!
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")To use resquin, supply a data frame containing survey
responses in wide format to either resp_styles(),
resp_distributions(),
resp_nondifferentiation(),
resp_patterns().
# 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)
#> # Number of missings due to min_valid_responses equal to 1: 4
#> # A data frame: 10 × 6
#> id MRS ARS DRS ERS NERS
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0 1 0.67 0.33
#> 2 2 0 0.67 0.33 0.33 0.67
#> 3 3 0.67 0 0.33 0 1
#> 4 4 NA NA NA NA NA
#> 5 5 0.67 0.33 0 0 1
#> 6 6 0 0.33 0.67 0.33 0.67
#> 7 7 NA NA NA NA NA
#> 8 8 0 0.33 0.67 0.67 0.33
#> 9 9 NA NA NA NA NA
#> 10 10 NA NA NA NA NA
# Calculate response distribution indicators per respondent
resp_distributions(x = testdata) |>
round(2)
#> # Number of missings due to min_valid_responses equal to 1: 4
#> # A data frame: 10 × 7
#> id n_na prop_na ii_mean ii_sd ii_median mahal
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 0 1.33 0.58 1 2.04
#> 2 2 0 0 3.67 1.53 4 1.6
#> 3 3 0 0 2.67 0.58 3 1.38
#> 4 4 1 0.33 NA NA NA NA
#> 5 5 0 0 3.33 0.58 3 0.97
#> 6 6 0 0 2.33 1.53 2 1.38
#> 7 7 1 0.33 NA NA NA NA
#> 8 8 0 0 2.67 2.08 2 1.88
#> 9 9 2 0.67 NA NA NA NA
#> 10 10 3 1 NA NA NA NA
# Calculate response nondifferentiation indicator per respondent
resp_nondifferentiation(x = testdata) |>
round(2)
#> # Number of missings due to min_valid_responses equal to 1: 4
#> # A data frame: 10 × 5
#> id simple_nondifferentiation mean_root_pairs max_identical_rating
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 1 0.67
#> 2 2 0 0.21 0.33
#> 3 3 0 1 0.67
#> 4 4 NA NA NA
#> 5 5 0 1 0.67
#> 6 6 0 0.21 0.33
#> 7 7 NA NA NA
#> 8 8 0 0 0.33
#> 9 9 NA NA NA
#> 10 10 NA NA NA
#> # ℹ 1 more variable: scale_point_variation <dbl>
# Calculate response pattern indicators
resp_patterns(x = testdata) |>
round(2)
#> # Number of missings due to min_valid_responses equal to 1: 4
#> # A data frame: 10 × 4
#> id n_transitions mean_string_length longest_string_length
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2 1 1
#> 2 2 2 1 1
#> 3 3 2 1 1
#> 4 4 NA NA NA
#> 5 5 2 1 1
#> 6 6 2 1 1
#> 7 7 NA NA NA
#> 8 8 2 1 1
#> 9 9 NA NA NA
#> 10 10 NA NA NAFor a more information on how to use resquin see the
vignettes Getting
started with resquin and resquin
in practice.
Researchers have developed a large number of response quality
indicators. resquin only covers a few of them. The
following list contains packages which provide alternative
implementations of the indicators used in resquin or
provide other indicators not used in resquin:
Implementation of (Kim et al. 2019) measures: straightliner
Implementation of indices from the careless responding literature: careless
Autocorrelation and pattern indices: responsePatterns
Below are packages which use IRT to model and estimate response styles and individual response quality:
Finally, there are packages for pattern checking:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.