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 [email protected]. 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() 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.
Bhaktha, Nivedita, Henning Silber, and Clemens Lechner. 2024. “Characterizing Response Quality in Surveys with Multi-Item Scales: A Unified Framework.” https://osf.io/9gs67/.
Curran, Paul G. 2016. “Methods for the Detection of Carelessly Invalid Responses in Survey Data.” Journal of Experimental Social Psychology 66 (September): 4–19. https://doi.org/10.1016/j.jesp.2015.07.006.
Vaerenbergh, Y. van, and T. D. Thomas. 2013. “Response Styles in Survey Research: A Literature Review of Antecedents, Consequences, and Remedies.” International Journal of Public Opinion Research 25 (2): 195–217. https://doi.org/10.1093/ijpor/eds021.