Analyzing the Association of Objective Burden Measures to Perceived Burden with Regression Trees

Abayomi, E.J., S. Maliszewski, L. Kreiner, and T. Ballard. 2018. “They spoke, we listened: Reducing respondent burden using previously reported data. In proceedings of the Section on Government Statistics of the 2018 Joint Statistical Meetings, July 28–August 2, Vancouver, British Columbia, Canada. Available at: https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/JSM-2018/They_Spoke,_We_Listened-_Reducing_Respondent_Burden_Using_Previously_Reported_Data.pdf. (accessed May 2022). Search in Google Scholar

Ashmead, R., E. Slud, and T. Hughes. 2017. “Adaptive Intervention Methodology for Reduction of Respondent Contact Burden in the American Community Survey.” Journal of Official Statistics 33(4): 901–919. DOI: https://doi.org/10.1515/jos-2017-0043. Search in Google Scholar

Baird, S., J. Hamory, and E.M. Miguels. 2008. Tracking, attrition and data quality in the kenyan life panel survey round 1 (klps-1). Institute of Business and Economic Research Center for International and Development Economics Research University of California, Berkeley Working Paper. Available at: http://emiguel.econ.berkeley.edu/assets/miguel_research/71/attrition_paper_FINAL-CIDER_aug08.pdf (accessed May 2022). Search in Google Scholar

Bogen, K. 1996. “The effect of questionnaire length on response rates: a review of the literature.” In Proceedings of the Survey Research Methods Section of the 1996 Joint Statistical Meetings, August 4–8, Chicago, Illinois, USA. U.S. Bureau of the Census. Available at: www.asasrms.org/Proceedings/papers/1996_177.pdf. (accessed May 2022). Search in Google Scholar

Bollen, K.A., J.L. Glanville, and G. Stecklov. 2001. “Socioeconomic status and class in studies of fertility and health in developing countries.” Annual Review of Sociology 27: 153–185. Available at: http://www.jstor.org/stable/2678618 (accessed May 2022).10.1146/annurev.soc.27.1.153 Search in Google Scholar

Bollen, K.A., J.L. Glanville, and G. Stecklov. 2002. “Economic status proxies in studies of fertility in developing countries: Does the measure matter?” Population Studies, 56(1): 81–96. Available at: http://www.jstor.org/stable/3092943 (accessed May 2022).10.1080/0032472021379622010844 Search in Google Scholar

Bradburn, N.M. 1978. “Respondent burden.” In Proceedings of the Survey Research Methods Section of the American Statistical Association: 35: 35–40. American Statistical Association, Alexandria, Virginia, USA. Available at: http://www.asasrms.org/Proceedings/papers/1978_007.pdf. (accessed May 2022). Search in Google Scholar

Cannell, C.F., P.V. Miller, and L. Oksenberg. 1981. “Research on interviewing techniques.” Sociological Methodology 12: 389–437. DOI: https://doi.org/10.2307/270748. Search in Google Scholar

Cohen, S.B., J.W. Cohen, and K. Davis. 2013 Longitudinal design options for the medical expenditure panel survey insurance component. Agency for Healthcare Research and Quality Working Paper. Available at: https://meps.ahrq.gov/data_files/publications/workingpapers/wp_13003.pdf (accessed May 2022). Search in Google Scholar

Connelly, N.A., T.L. Brown, and D.J. Decker. 2003. “Factors affecting response rates to natural resource-focused mail surveys: Empirical evidence of declining rates over time”. Society & Natural Resources 16(6): 541–549. DOI: https://doi.org/10.1080/08941920309152. Search in Google Scholar

Czajka, J.L., and A. Beyler. 2016. “Background paper declining response rates in federal surveys: Trends and implications.” Mathematica policy research 1: 1–86. Available at: https://aspe.hhs.gov/sites/default/files/private/pdf/255531/Decliningresponserates.pdf (accessed May 2022). Search in Google Scholar

Earp, M., D. Toth, P. Phipps, and C. Oslund. “Assessing Nonresponse in a Longitudinal Establishment Survey Using Regression Trees.” Journal of Official Statistics 34(2): 463–481. DOI: https://doi.org/10.2478/jos-2018-0021. Search in Google Scholar

Edgar, J., B. McBride, and A. Safir. 2013a. “Research highlights of the consumer expenditure survey redesign.” Monthly Labor Review 136:1. Available at: https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/month136&section=74 (accessed May 2022).10.21916/mlr.2013.29 Search in Google Scholar

Edgar, J. D.V. Nelson, L. Paszkiewicz, and A. Safir. 2013b. The gemini project to redesign the consumer expenditure survey: redesign proposal. CE Gemini Project materials, U.S. Bureau of Labor Statistics. Available at: https://stats.bls.gov/cex/ce_gemini_redesign.pdf (accessed May 2022).10.21916/mlr.2013.29 Search in Google Scholar

Fekete, C., H. Tough, J. Siegrist, and M.W.G. Brinkhof. 2017. “Health impact of objective burden, subjective burden and positive aspects of caregiving: An observational study among caregivers in switzerland.” BMJ (British Medical Journal) 7 (9). DOI: http://dx.doi.org/10.1136/bmjopen-2017-017369.10.1136/bmjopen-2017-017369577095329275339 Search in Google Scholar

Fricker, S., T. Yan, and S. Tsai. 2014. Response burden: What predicts it and who is burdened out. In Proceedings of the American Association for Public Opinion Research (AAPOR) Annual Conference, May 15–18, 4568–4577, Anaheim, California, USA. Available at: http://www.asasrms.org/Proceedings/y2014/files/400298_500838.pdf (accessed May 2022). Search in Google Scholar

Galesic, M., and M. Bosnjak. 2009. “Effects of questionnaire length on participation and indicators of response quality in a web survey.” Public Opinion Quarterly 73(2): 349–360. Available at: http://www.jstor.org/stable/25548084 (accessed May 2022).10.1093/poq/nfp031 Search in Google Scholar

Groves, R.M. 2006. “Nonresponse rates and nonresponse bias in household surveys.” Public Opinion Quarterly 70(5): 646–675. Available at: http://www.jstor.org/stable/4124220 (accessed May 2022).10.1093/poq/nfl033 Search in Google Scholar

Groves, R.M., D.A. Dillman, J.L. Eltinge and R.J.A. Little. 2001. Survey Nonresponse. New York: Wiley Inter-science. Search in Google Scholar

Groves, R.M., E. Singer, and A. Corning. 2000. “Leverage-saliency theory of survey participation: Description and an illustration.” The Public Opinion Quarterly 64(3): 299–308. Available at: http://www.jstor.org/stable/3078721 (accessed May 2022).10.1086/31799011114270 Search in Google Scholar

Gustavson, K., T. von Soest, E. Karevold, and E Røysamb. 2012. “Attrition and generalizability in longitudinal studies: Findings from a 15-year population-based study and a monte carlo simulation study.” BMC Public Health 12(918): 1–11. DOI: https://doi.org/10.1186/1471-2458-12-918.350374423107281 Search in Google Scholar

Hothorn, T., K. Hornik, and A. Zeileis. 2016. “Unbiased recursive partitioning: A conditional inference framework.” Journal of Computational and Graphical Statistics 15 (3): 651–674. Available at: http://www.jstor.org/stable/27594202 (accessed May 2022).10.1198/106186006X133933 Search in Google Scholar

Kashihara, D., and T.M. Ezzati-Rice. 2004. “Characteristics of survey attrition in the household component of the medical expenditure panel survey (meps).” In Proceedings of the Survey Research Methods Section of the 2004 Joint Statistical Meetings, August 8–12: 3758–3765, Toronto, Ontario, Canada. Available at: http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000706.pdf. (accessed May 2022). Search in Google Scholar

Kolenikov, S., and G. Angeles. 2004. The use of discrete data in pca: Theory, simulations, and applications to socioeconomic indices. Chapel Hill: Carolina Population Center, University of North Carolina: 20: 1–59. Available at: https://www.measureevaluation.org/resources/publications/wp-04-85 (accessed May 2022). Search in Google Scholar

Kolenikov, S., and G. Angeles. 2009. “Socioeconomic status measurement with discrete proxy variables: Is principal component analysis a reliable answer?” Review of Income and Wealth 55(1): 128–165. DOI: https://doi.org/10.1111/j.1475-4991.2008.00309.x. Search in Google Scholar

Lynn, P. 2014. “Longer interviews may not affect subsequent survey participation propensity.” Public Opinion Quarterly 78(2): 500–509. Available at: http://www.jstor.org/stable/24545936 (accessed May 2022).10.1093/poq/nfu015 Search in Google Scholar

McDermott, N., and L. Tan. 2008. “The effect of refusal conversion on data quality in the consumer expenditure interview survey.” Consumer Expenditure Survey Anthology: 23–32. Available at: https://stats.bls.gov/cex/anthology08/csxanth4.pdf (accessed May 2022). Search in Google Scholar

U.S. Bureau of Labor Statistics. 2017. Consumer expenditure survey interview questionnaire (ceq). Technical report, U.S. Bureau of Labor Statistics. Available at: https://www.bls.gov/cex/capi/2017/2017-CEQ-CAPI-instrument-specifications.pdf (accessed May 2022). Search in Google Scholar

U.S. Bureau of Labor Statistics. 2018. Handbook of methods. Technical report, U.S. Bureau of Labor Statistics. Available at: https://www.bls.gov/opub/hom/cex/home.htm (accessed May 2022). Search in Google Scholar

Olsson, U. 1979. “Maximum likelihood estimation of the polychoric correlation coefficient.” Psychometrika 44(4): 443–460. DOI: http://dx.doi.org/10.1007/BF02296207.10.1007/BF02296207 Search in Google Scholar

Pearson, K., and E.S. Pearson. 1922. “On polychoric coefficients of correlation.” Biometrika 14(1/2): 127–156. DOI: https://doi.org/10.2307/2331858. Search in Google Scholar

Pfeffermann, D. 1996. The use of sampling weights for survey data analysis. Statistical Methods in Medical Research 5(3): 239–261. DOI: https://doi.org/10.1177/096228029600500303.8931195 Search in Google Scholar

Pfeffermann, D., and M. Sverchkov. 1999. “Parametric and semi-parametric estimation of regression models fitted to survey data.” Sankhyā 61(1): 166–186. Available at: http://www.jstor.org/stable/25053074 (accessed May 2022). Search in Google Scholar

Phipps, P., and D. Toth. 2012. “Analyzing establishment nonresponse using an interpretable regression tree model with linked administrative data.” The Annals of Applied Statistics 6 (2): 772–794. Available at: http://www.jstor.org/stable/41713473 (accessed May 2022).10.1214/11-AOAS521 Search in Google Scholar

R Core Team. 2020. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/. (accessed May 2022). Search in Google Scholar

Rolstad, S., J. Adler, and A Rydén. 2011. “Response burden and questionnaire length: is shorter better? a review and meta-analysis.” Value in Health 14(8): 1101–1108. DOI: https://doi.org/10.1016Zj.jval.2011.06.003.10.1016/j.jval.2011.06.00322152180 Search in Google Scholar

Sharp, L.M., and J. Frankel. 1983. “Respondent burden: A test of some common assumptions.” The Public Opinion Quarterly 47(1): 36–53. Available at: https://www.jstor.org/stable/2748704 (accessed May 2022).10.1086/268765 Search in Google Scholar

Toth, D., and P. Phipps. 2014. “Regression tree models for analyzing survey response.” In Proceedings of the Section on Government Statistics of the 2014 Joint Statistical Meetings: 339 – 351, Boston, Massachusetts, USA. Citeseer. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.696.8138&rep=rep1&type=pdf (accessed May 2022). Search in Google Scholar

Toth, D. 2020. “rpms: Recursive Partitioning for Modeling Survey Data.” Available at: https://cran.r-project.org/web/packages/rpms/rpms.pdf. R package version 0.4.0. Search in Google Scholar

Yang, D.K. 2018. “Evaluating perceived burden of household survey respondents.” In Presentation of the American Association for Public Opinion Research (AAPOR) Annual Conference, May 16–19, and DC AAPOR and Washington Statistical Society (WSS) Summer Conference July 16, Denver, Colorado, and Washington D.C. USA. Available at: http://app.core-apps.com/aapor_2018/abstract/e28a28d3-f1e7-40e9-b39b-a2cbc76e519,files.dc-aapor.org/slides/summer2018/Yang.pdf. (accessed May 2022). Search in Google Scholar

Yang, D.K. 2019. “Assessing how a household survey is perceived by respondents.” In Proceedings of the Section on Government Statistics of the 2019 Joint Statistical Meetings, July 27–August 1. 1–19. Denver, Colorado, USA. Available at: https://www.bls.gov/osmr/research-papers/2019/st190130.htm (accessed May 2022). Search in Google Scholar

Young, A.F., J.R. Powers, and S.L. Bell. 2006. “Attrition in longitudinal studies: Who do you lose.” Australian and New Zealand Journal of Public Health 30(4): 353–361. DOI: https://doi.org/10.1111/j.1467-842X.2006.tb00849.x. Search in Google Scholar