Calculates response style indicators for matrix questions or multi-item scales.
Arguments
- x
A data frame containing survey responses in wide format. For more information see section "Data requirements" below.
- scale_min
numeric. Minimum of scale provided.
- scale_max
numeric. Maximum of scale provided.
- min_valid_responses
numeric between 0 and 1. Defines the share of valid responses a respondent must have to calculate response style indicators.
- normalize
logical. If TRUE, counts of response style indicators will be divided by the number of non-missing responses per respondent. Default is TRUE.
Value
Returns a data frame with response style indicators per respondent.
Rows: Equal to number of rows in x.
Columns: Five, one for each response style indicator.
Details
Response styles capture systematic shifts in respondents response behavior.
resp_styles() is aimed at multi-item scales or matrix questions which use the same number of
response options for many questions.
The following response style indicators are calculated per respondent: Middle response style (MRS), acquiescence response style (ARS), disacquiescence response style (DARS), extreme response style (ERS) and non-extreme response style (NERS).
The response style indicators are calculated in the following way
MRS: Sum of mid point responses.
ARS: Sum of responses larger than midpoint.
DARS: Sum of responses lower than midpoint.
ERS: Sum of lowest or highest category responses.
NERS: Sum of responses between lowest and highest respnose category.
Note that ARS and DRS assume that the polarity of the scale is positive. This means that higher numerical values indicate agreement and lower numerical values indicate disagreement. MRS can only be calculated if the scale has a numeric midpoint.
Also note that the response style literature is fragmented (Bhaktha et al., 2024).
Response styles calculated with resp_styles() are based on van Vaerenbergh & Thomas (2024).
However, we used the name non-extreme response style (NERS) instead of mild response style,
to emphasize that NERS it the inverse of ERS. Both appear in the literature
(for a NERS example see Wetzel et al. (2013)). Consult literature in your field
of research to find appropriate names for the response style indicators calculated here.
Data requirements
resp_styles() assumes that the input data frame is structured in the following way:
The data frame is in wide format, meaning each row represents one respondent, each column represents one variable.
The variables are in same the order as the questions respondents saw while taking the survey.
Reverse keyed variables are in their original form. No items were recoded.
All responses have integer values.
Questions have the same number of response options.
Missing values are set to
NA.
References
Bhaktha, Nivedita, Henning Silber, and Clemens Lechner. 2024. „Characterizing response quality in surveys with multi-item scales: A unified framework“. OSF-preprtint: https://osf.io/9gs67/
van Vaerenbergh, Y., 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. doi: 10.1093/ijpor/eds021.
Wetzel, Eunike, Claus H. Carstensen, und Jan R. Böhnke. 2013. „Consistency of Extreme Response Style and Non-Extreme Response Style across Traits“. Journal of Research in Personality 47(2):178–89. doi: 10.1016/j.jrp.2012.10.010.
See also
resp_distributions() for calculating response distribution indicators.
Examples
# A test data set with ten respondents
# and responses to three survey questions
# with response scales from 1 to 5.
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))
# Calculate response distribution indicators
resp_styles(testdata,
scale_min = 1,
scale_max = 5) |>
round(2) # round to second decimal
#> 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
# Include respondents with NA values by decreasing the
# necessary number of valid responses per respondent.
resp_styles(testdata,
scale_min = 1,
scale_max = 5,
min_valid_responses = 0.2) |>
round(2) # round to second decimal
#> 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 0.50 0.50 0.00 0.50 0.50
#> 5 0.67 0.33 0.00 0.00 1.00
#> 6 0.00 0.33 0.67 0.33 0.67
#> 7 0.50 0.50 0.00 0.00 1.00
#> 8 0.00 0.33 0.67 0.67 0.33
#> 9 1.00 0.00 0.00 0.00 1.00
#> 10 NA NA NA NA NA
# Get counts of responses attributable to response styles.
resp_styles(testdata,
scale_min = 1,
scale_max = 5,
normalize = FALSE)
#> MRS ARS DRS ERS NERS
#> 1 0 0 3 2 1
#> 2 0 2 1 1 2
#> 3 2 0 1 0 3
#> 4 NA NA NA NA NA
#> 5 2 1 0 0 3
#> 6 0 1 2 1 2
#> 7 NA NA NA NA NA
#> 8 0 1 2 2 1
#> 9 NA NA NA NA NA
#> 10 NA NA NA NA NA