rankingQ implements design-based methods for correcting measurement errors in ranking questions due to random responses. rankingQ allows users to estimate various ranking-based quantities of interest both non-parametrically and parametrically. rankingQ also offers practical tools for detecting the bias and assessing the anchor-ranking question.
For the underlying methodology, see Atsusaka and Kim (2025). “Addressing Measurement Errors in Ranking Questions for the Social Sciences.” Political Analysis Volume 33 , Issue 4 , October 2025 , pp. 339 - 360. Please visit the package site for all vignettes and references.
Installation
rankingQ can be installed using the following code:
remotes::install_github(
"sysilviakim/rankingQ",
dependencies = TRUE
)Example
The following is a simplified example of how this package can be used. For a full demonstration, visit https://sysilviakim.com/rankingQ/.
data("identity")
head(identity)
# # A tibble: 6 × 16
# s_weight app_identity app_identity_1 app_identity_2 app_identity_3 app_identity_4
# <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 0.844 1423 1 4 2 3
# 2 0.886 1423 1 4 2 3
# 3 2.96 3412 3 4 1 2
# 4 0.987 1423 1 4 2 3
# 5 1.76 4132 4 1 3 2
# 6 0.469 3124 3 1 2 4
# Perform bias correction via plug-in estimator
out_direct <- imprr_direct(
data = identity,
J = 4,
main_q = "app_identity",
anc_correct = "anc_correct_identity"
)
out_direct$results
# # A tibble: 44 × 6
# # Groups: item, qoi [16]
# item qoi outcome mean lower upper
# <chr> <chr> <chr> <dbl> <dbl> <dbl>
# 1 app_identity_1 average rank Avg: a… 3.27 3.24 3.33
# 2 app_identity_1 marginal rank… Ranked… 0.0407 0.0232 0.0496
# 3 app_identity_1 marginal rank… Ranked… 0.150 0.137 0.163
# 4 app_identity_1 marginal rank… Ranked… 0.305 0.275 0.336
# 5 app_identity_1 marginal rank… Ranked… 0.504 0.475 0.541
# 6 app_identity_1 pairwise rank… v. app… 0.357 0.333 0.374
# 7 app_identity_1 pairwise rank… v. app… 0.108 0.0739 0.136
# 8 app_identity_1 pairwise rank… v. app… 0.262 0.238 0.284
# 9 app_identity_1 top-k ranking Top-1 0.0407 0.0232 0.0496
# 10 app_identity_1 top-k ranking Top-2 0.306 0.291 0.339
# Perform bias correction via IPW
out_weights <- imprr_weights(
data = identity,
J = 4,
main_q = "app_identity",
anc_correct = "anc_correct_identity"
)
head(out_weights$rankings)
# ranking n prop_obs prop_bc weights prop_bc_raw prop_bc_adj
# 1 1234 14 0.012939002 0.000000000 0.0000000 -0.0003526508 0.000000000
# 2 1243 11 0.010166359 0.000000000 0.0000000 -0.0044081345 0.000000000
# 3 1324 14 0.012939002 0.000000000 0.0000000 -0.0003526508 0.000000000
# 4 1342 7 0.006469501 0.000000000 0.0000000 -0.0098154461 0.000000000
# 5 1423 50 0.046210721 0.046944603 1.0158812 0.0483131539 0.048313154
# 6 1432 20 0.018484288 0.007538549 0.4078355 0.0077583167 0.007758317