Package: nrba 0.3.1

nrba: Methods for Conducting Nonresponse Bias Analysis (NRBA)

Facilitates nonresponse bias analysis (NRBA) for survey data. Such data may arise from a complex sampling design with features such as stratification, clustering, or unequal probabilities of selection. Multiple types of analyses may be conducted: comparisons of response rates across subgroups; comparisons of estimates before and after weighting adjustments; comparisons of sample-based estimates to external population totals; tests of systematic differences in covariate means between respondents and full samples; tests of independence between response status and covariates; and modeling of outcomes and response status as a function of covariates. Extensive documentation and references are provided for each type of analysis. Krenzke, Van de Kerckhove, and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf> and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR> provide an overview of the methods implemented in this package.

Authors:Ben Schneider [aut, cre], Jim Green [aut], Shelley Brock [aut], Tom Krenzke [aut], Michael Jones [aut], Wendy Van de Kerckhove [aut], David Ferraro [aut], Laura Alvarez-Rojas [aut], Katie Hubbell [aut], Westat [cph]

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NEWS

# Install 'nrba' in R:
install.packages('nrba', repos = c('https://westat-schneider.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.82 score 2 packages 22 scripts 169 downloads 15 exports 36 dependencies

Last updated 12 months agofrom:60c8caa611. Checks:OK: 6 ERROR: 1. Indexed: yes.

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Doc / VignettesOKOct 29 2024
R-4.5-winERROROct 29 2024
R-4.5-linuxOKOct 29 2024
R-4.4-winOKOct 29 2024
R-4.4-macOKOct 29 2024
R-4.3-winOKOct 29 2024
R-4.3-macOKOct 29 2024

Exports:%>%assess_range_of_biascalculate_response_rateschisq_test_ind_responsechisq_test_vs_external_estimateget_cumulative_estimatespredict_outcome_via_glmpredict_response_status_via_glmrake_to_benchmarksstepwise_model_selectiont_test_of_weight_adjustmentt_test_resp_vs_eligt_test_resp_vs_fullt_test_vs_external_estimatewt_class_adjust

Dependencies:backportsbroomclicpp11DBIdplyrfansigenericsgluelatticelifecyclemagrittrMatrixminqamitoolsmvtnormnumDerivpillarpkgconfigpurrrR6RcppRcppArmadillorlangsrvyrstringistringrsurveysurvivalsvreptibbletidyrtidyselectutf8vctrswithr

Analysis of Response Rates and Response Propensity

Rendered fromv1_analysis-of-response-rates-and-response-propensity.Rmdusingknitr::rmarkdownon Oct 29 2024.

Last update: 2023-11-22
Started: 2023-03-23

Readme and manuals

Help Manual

Help pageTopics
Assess the range of possible bias based on specified assumptions about how nonrespondents differ from respondentsassess_range_of_bias
Calculate Response Ratescalculate_response_rates
Test the independence of survey response and auxiliary variableschisq_test_ind_response
Test of differences in survey percentages relative to external estimateschisq_test_vs_external_estimate
Calculate cumulative estimates of a mean/proportionget_cumulative_estimates
Parent involvement survey: population datainvolvement_survey_pop
Parent involvement survey: simple random sampleinvolvement_survey_srs
Parent involvement survey: stratified, two-stage sampleinvolvement_survey_str2s
Fit a regression model to predict survey outcomespredict_outcome_via_glm
Fit a logistic regression model to predict response to the survey.predict_response_status_via_glm
Re-weight data to match population benchmarks, using raking or post-stratificationrake_to_benchmarks
Select and fit a model using stepwise regressionstepwise_model_selection
t-test of differences in means/percentages between responding sample and full sample, or between responding sample and eligible samplet_test_by_response_status t_test_resp_vs_elig t_test_resp_vs_full
t-test of differences in estimated means/percentages from two different sets of replicate weights.t_test_of_weight_adjustment
t-test of differences in means/percentages relative to external estimatest_test_vs_external_estimate
Adjust weights in a replicate design for nonresponse or unknown eligibility status, using weighting classeswt_class_adjust