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2023
  • Irimata KE, He Y, Parsons VL, Shin H-C, Zhang G. Calibration weighting methods for the National Center for Health Statistics Research and Development Survey. National Center for Health Statistics. Vital Health Stat 2(199). 2023.
  • He Y, Zhang G. Multiple imputation analysis of missing complex survey data using SAS®: A brief overview and an example based on the Research and Development Survey (RANDS). The Survey Statistician 87. 2023. Available from: http://isi-iass.org/home/wp-content/uploads/Survey_Statistician_2023_January_N87.pdf.

2022
  • Irimata KE, Pleis JR, Heslin KC, He Y. Reduced access to preventive care due to the COVID-19 pandemic, by chronic disease status and race and Hispanic origin, United States, 2020–2021. Public Health Rep 0(0):1-8. DOI: 1177/00333549221138855.
  • Shin H-C, Parker J, Parsons V, He Y, Irimata K, Cai B, Beresovsky V. Propensity-score adjusted estimates for selected health outcomes from the Research and Development Survey. National Center for Health Statistics. Vital Health Stat 2(196). 2022. DOI: https://dx.doi.org/10.15620/cdc:121708.
  • Li Y, Irimata K, He Y, Parker J. Variable inclusion strategies through directed acyclic graphs to adjust health surveys subject to selection bias for producing national estimates. J Off Stat 38(3):875–900. 2022.
  • Irimata KE, Scanlon PJ. The Research and Development Survey (RANDS) during COVID-19. Stat J IAOS 38(1):13–21. 2022.

2020
  • Irimata KE, He Y, Cai B, Shin H-C, Parsons VL, Parker JD. Comparison of quarterly and yearly calibration data for propensity score adjusted web survey estimates. Surv Methods Insights Field. 2020. DOI: 10.13094/SMIF-2020-00018.
  • Parker J, Miller K, He Y, Scanlon P, Cai B, Shin H-C, et al. Overview and initial results of the National Center for Health Statistics’ Research and Development Survey. Stat J IAOS 36(4):1199–1211. 2020. DOI: 3233/SJI-200678.

 

2019
  • Scanlon P. The effects of embedding closed-ended cognitive probes in a web survey on survey response. Field Methods 31(4):328–43. 2019. DOI: 10.1177/1525822X19871546.
  • Scanlon P. Using targeted embedded probes to quantify cognitive interviewing findings. In: Beatty PC, Collins D, Kaye L, Padilla JL, Willis GB, Wilmot A, editors. Advances in questionnaire design, development, evaluation, and testing. Hoboken, NJ: John Wiley & Sons, 427–50. 2019. DOI: 10.1002/9781119263685.ch17.

2018
  • Beresovsky V. Comparing random and nonrandom samples using model-implied randomization. In: 2018 Proceedings of the Federal Committee on Statistical Methodology Research Conference. Washington, DC: Federal Committee on Statistical Methodology. 2018.
  • He Y, Shin H-C, Cai B, Beresovsky V, Scanlon P, Parsons V, et al. The utility of using web surveys to provide official estimates for major health outcomes: A pilot study. In: 2018 Statistics Canada International Methodology Symposium Proceedings. Ottawa, Canada: Statistics Canada. 2018. Available from: https://www.statcan.gc.ca/eng/conferences/symposium2018/program/08b1_he-eng.pdf.

2017
  • Scanlon P. Cognitive evaluation of the 2015–2016 National Center for Health Statistics’ Research and Development Survey. National Center for Health Statistics Q-Bank. 2017. Available from: https://wwwn.cdc.gov/QBANK/report/Scanlon_2017_NCHS_RANDS.pdf.
  • Beresovsky V. Imputation classes as a framework for inferences from nonrandom samples. In: 2017 Proceedings of the Joint Statistical Meetings. Alexandria, VA: American Statistical Association, 2918–32. 2017.
  • Singh AC, Beresovsky V, Ye C. Estimation from purposive samples with the aid of probability supplements but without data on the study variable. In: 2017 Proceedings of the Joint Statistical Meetings. Alexandria, VA: American Statistical Association, 3324–45. 2017.