I am analysing survey data, whose questions are in the form of the Likert Scale. I used auxiliary census data to calculate weights for different age groups within my sample. I would now like to use these weights to correct my sample data and then display the distribution for each question differentiated for each age group.
Any help is appreciated!
The conventional way of doing this in R would be to use the survey package and/or the srvyr package which allows you to use dplyr-style syntax while relying on the survey package to correctly handle weighting and complex survey designs.
Below is a small example of analyzing Likert data using the srvyr package.
# Create example data ----
library(survey)
library(srvyr)
set.seed(1999)
## Data frame of responses and grouping information
likert_response_options <- c("1 - Strongly Disagree", "2", "3", "4", "5 - Strongly Agree")
data_df <- data.frame(
group_vbl = factor(sample(LETTERS[1:4], 20, replace = TRUE), LETTERS[1:4]),
likert_item = factor(x = sample(likert_response_options, 20, replace = TRUE),
levels = likert_response_options),
weights = rnorm(20, mean = 1, sd = 0.1)
)
## Create a survey design object from the data frame
my_survey_design <- as_survey_design(data_df, weights = weights)
# Create weighted summaries ----
my_survey_design %>%
group_by(group_vbl, likert_item) %>%
summarize(proportion = survey_mean())
#> # A tibble: 20 x 5
#> group_vbl likert_item proportion proportion_low proportion_upp
#> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 A 1 - Strongly Disagree 0.300 -0.252 0.851
#> 2 A 2 0 0 0
#> 3 A 3 0.700 0.149 1.25
#> 4 A 4 0 0 0
#> 5 A 5 - Strongly Agree 0 0 0
#> 6 B 1 - Strongly Disagree 0.127 -0.130 0.384
#> 7 B 2 0.146 -0.143 0.435
#> 8 B 3 0.259 -0.0872 0.606
#> 9 B 4 0.468 0.0577 0.878
#> 10 B 5 - Strongly Agree 0 0 0
#> 11 C 1 - Strongly Disagree 0 0 0
#> 12 C 2 0.241 -0.213 0.696
#> 13 C 3 0.292 -0.221 0.804
#> 14 C 4 0.250 -0.216 0.716
#> 15 C 5 - Strongly Agree 0.217 -0.205 0.639
#> 16 D 1 - Strongly Disagree 0 0 0
#> 17 D 2 0.529 0.0906 0.967
#> 18 D 3 0 0 0
#> 19 D 4 0.159 -0.156 0.474
#> 20 D 5 - Strongly Agree 0.312 -0.0888 0.713