Getting started with resquin

Example dataset of survey responses

Consider the following (fake) data set of survey responses.

# 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

The data set contains responses to three survey questions (var_a,var_b and var_c) from ten respondents. All three survey question allow responses on a scale from 1 to 5. Some respondents have missing values, which are set to NA.

Lets use this data set to calculate response quality indicators.

resp_styles(): Response style indicators

Response styles capture systematic shifts in respondents response behavior. For example, respondents with an extreme response style may only choose the lowest and highest categories (in our example 1 and 5) while mid-point responder only choose the midpoint of a scale (in our example 3).

To calculate response styles we can use the resp_styles() function. First, we need to specify our data argument x. Then, we need to specify the minimum and maximum of the scales used in our questionnaire (scale_min and scale_max respectively). Remember that all questions included must have the same number of response options. We will discuss the arguments min_valid_responses and normalize later.

library(resquin)
# Calculating response style indicators for all respondents with no missing values
results_response_styles <- resp_styles(
  x = testdata,
  scale_min = 1,
  scale_max = 5,
  min_valid_responses = 1, # Excludes respondents with less than 100% valid responses
  normalize = T)  # Presents results in percent of all responses

round(results_response_styles,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

The resulting data frame contains five columns corresponding to the middle response style (MRS), acquiescence response style (ARS), disaquiescence response style (DRS), extreme response style (ERS), and non-extreme response style (NERS) - you can learn more about the response styles in the help file of the function using ?resp_styles.

Each respondent receives one value for each indicator, given that they can be calculated. Because normalize is set to TRUEthe values are expressed as the share of responses of a respondent that can be attributed to a response style. For example, respondent one has an ERS value of 0.67 meaning that two out of three responses can be identified as extreme responses. On the other hand, respondent one does not have any mid-point response, leading to a value of 0 in the MRS column.

Instead of calculating proportions, we can extract the counts of responses that can be attributed to a response option by setting normalize to FALSE.

Finally, we can decide to include or exclude respondents from receiving response style values by setting min_valid_responses, which can take values from 0 to 1. min_valid_responses sets the share of valid responses (i.e. non-missing responses) a respondent must have to receive response style values. A value of 0 indicates that response style values should be calculated for all respondents, regardless of whether or not they have missing values. A value of 1 indicates that response styles should only be calculated for respondents who have valid responses on all variables. Values between 0 and 1 indicate the share of responses that need to be valid to be included in the response style calculations.

resp_distributions(): Intra-individual response distribution indicators

resp_distributions() calculates indicators which reflect the location and variability of responses within a respondent. resp_distributions() works similar to resp_styles(): We need to specify the data argument and we can include or exclude respondents from the calculations based on amount of missing data they exhibit (for an explanation see paragraph above).

# Calulating response distribution indicators for all respondents with no missing values
results_resp_distributions <- resp_distributions(
  x = testdata,
  min_valid_responses = 1) # Excludes respondents with less than 100% valid responses

round(results_resp_distributions,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    NA

The resulting data frame contains eight columns:

• n_na: number of intra-individual missing answers

• prop_na: proportion of intra-individual missing responses

• ii_mean: intra-individual mean

• ii_median: intra-individual median

• ii_sd: intra-individual standard deviation

• mahal: Mahalanobis distance per respondent.

You can learn more about the response distribution indicators using `?resp_distributions``