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Using Response Propensity Models to Improve the Quality of Response Data in Longitudinal Studies

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Journal of Official Statistics
Special Issue on Responsive and Adaptive Survey Design

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Alberman, E.D. and H. Goldstein. 1970. “The At Risk Register: A Statistical Evaluation.” British Journal of Preventive and Social Medicine 24: 129–135. Doi: http:/dx.doi.org/10.1136/jech.24.3.129.10.1136/jech.24.3.12910592724249594Search in Google Scholar

Behr, A., E. Bellgardt, and U. Rendtel. 2005. “Extent and Determinants of Panel Attrition in the European Community Household Panel.” European Sociological Review 21: 489–512. Doi: http:/dx.doi.org/10.1093/esr/jci037.10.1093/esr/jci037Search in Google Scholar

Blom, A.G. 2014. “Setting Priorities: Spurious Differences in Response Rates.” International Journal of Public Opinion Research 26: 245–255. Doi: http:/dx.doi.org/10.1093/ijpor/edt023.10.1093/ijpor/edt023Search in Google Scholar

Binder, D. 1983. “On the Variances of Asymptotically Normal Estimators from Complex Surveys.” International Statistical Review 51: 279–292. Doi: http:/dx.doi.org/10.2307/1402588.10.2307/1402588Search in Google Scholar

Box, G.P. and D.R. Cox. 1964. “An Analysis of Transformations.” Journal of Royal Statistical Society, Series B 26: 211–252.10.1111/j.2517-6161.1964.tb00553.xSearch in Google Scholar

Browne, W.J. 2009. MCMC Estimation in MLwiN. Centre for Multilevel Modelling, University of Bristol. Available at: http://www.bristol.ac.uk/cmm/software/mlwin/features/mcmc.html (accessed July 17, 2017).Search in Google Scholar

Browne, W.J., F. Steele, M. Golalizadeh, and M.J. Green. 2009. “The Use of Simple Reparameterizations to Improve the Efficiency of Markov Chain Monte Carlo Estimation for Multilevel Models with Applications to Discrete Time Survival Models.” Journal of Royal Statistical Society, Series A 172: 579–598. Doi: http://dx.doi.org/10.1111/j.1467-985X.2009.00586.x.10.1111/j.1467-985X.2009.00586.x271832519649268Search in Google Scholar

Calderwood, L., I. Plewis, S.C. Ketende, and T. Mostafa. 2016. “Evaluating the Immediate and Longer-term Impact of a Refusal Conversion Strategy in a Large Scale Longitudinal Study.” Survey Research Methods 10: 225–236. Doi: http://dx.doi.org/10.18148/srm/2016.v10i3.6275.Search in Google Scholar

Couper, M. and M. Ofstedal. 2009. “Keeping in Contact with Mobile Sample Members.” In Methodology of Longitudinal Surveys, edited by P. Lynn, 183–203. Chichester: John Wiley. Doi: http://dx.doi.org/10.1002/9780470743874.ch11.10.1002/9780470743874.ch11Search in Google Scholar

Durrant, G.B. and F. Steele. 2009. “Multilevel Modelling of Refusal and Non-Contact in Household Surveys: Evidence from six UK Government Surveys.” Journal of the Royal Statistical Society, Series A 172: 361–382. Doi: http://dx.doi.org/10.1111/j.1467-985X.2008.00565.x.10.1111/j.1467-985X.2008.00565.xSearch in Google Scholar

Durrant, G.B., J. D’Arrigo, and F. Steele. 2011. “Using Paradata to Predict Best Times of Contact Conditioning on Household and Interviewer Influences.” Journal of the Royal Statistical Society, Series A 174: 1029–1049. Doi: http://dx.doi.org/10.1111/j.1467-985X.2011.00715.x.10.1111/j.1467-985X.2011.00715.xSearch in Google Scholar

Fumagalli, L., H. Laurie, and P. Lynn. 2013. “Experiments with Methods to Reduce Attrition in Longitudinal Surveys.” Journal of the Royal Statistical Society, Series A 176: 499–519. Doi: http://dx.doi.org/10.1111/j.1467-985X.2012.01051.x.10.1111/j.1467-985X.2012.01051.xSearch in Google Scholar

Groves, R.M. 2006. “Nonresponse Rates and Non-response Bias in Household Surveys.” Public Opinion Quarterly 70: 646–675. Doi: http://dx.doi.org/10.1093/poq/nfl033.10.1093/poq/nfl033Search in Google Scholar

Hawkes, D. and I. Plewis. 2006. “Modelling Non-Response in the National Child Development Study.” Journal of the Royal Statistical Society, Series A 169: 479–491. Doi: http://dx.doi.org/10.1111/j.1467-985X.2006.00401.x.10.1111/j.1467-985X.2006.00401.xSearch in Google Scholar

Lynn, P., O. Kaminska, and H. Goldstein. 2014. “Panel Attrition: How Important is Interviewer Continuity?” Journal of Official Statistics 30: 443–457. Doi: http://dx.doi.org/10.2478/jos-2014-0028.10.2478/jos-2014-0028Search in Google Scholar

Kreuter, F., K. Olson, J. Wagner, T. Yan, T.M. Ezzati-Rice, C. Casas-Cordero, M. Lemay, A. Peytchev, R.M. Groves, and T.E. Raghunathan. 2010. “Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Non-Response: Examples from Multiple Surveys.” Journal of the Royal Statistical Society, Series A 173: 389–408. Doi: http://dx.doi.org/10.1111/j.1467-985X.2009.00621.x.10.1111/j.1467-985X.2009.00621.xSearch in Google Scholar

Krzanowski, W.J. and D.J. Hand. 2009. ROC Curves for Continuous Data. Boca Raton, Fl: Chapman and Hall/CRC. Doi: http://dx.doi.org/10.1201/9781439800225.10.1201/9781439800225Search in Google Scholar

Laurie, H. and P. Lynn. 2009. “The Use of Respondent Incentives on Longitudinal Surveys.” In Methodology of Longitudinal Surveys, edited by P. Lynn, 205–233. Chichester: John Wiley. Doi: http://dx.doi.org/10.1002/9780470743874.ch12.10.1002/9780470743874.ch12Search in Google Scholar

Lynn, P. 2017. “From Standardised to Targeted Survey Procedures for Tackling Nonresponse and Attrition.” Survey Research Methods 11: 93–103. Doi: http://dx.doi.org/10.18148/srm/2017.v11i1.6734.Search in Google Scholar

McGonagle, K., M. Couper, and R. Schoeni. 2009. “An Experimental Text of a Strategy to Maintain Contact with Families Between Waves of a Panel Study.” Survey Practice 2(5).10.29115/SP-2009-0020Search in Google Scholar

Pepe, M.S. 2003. The Statistical Evaluation of Medical Tests for Classification and Prediction. New York: OUP. Doi: http://dx.doi.org/10.1198/jasa.2005.s19.10.1198/jasa.2005.s19Search in Google Scholar

Peytchev, A., S. Riley, J. Rosen, and J. Murphy. 2010. “Reduction of Nonresponse Bias in Surveys Through Case Prioritization.” Survey Research Methods 4: 21–29. Doi: http://dx.doi.org/10.18148/srm/2010.v4i1.3037.Search in Google Scholar

Plewis, I. 2007a. “Non-Response in a Birth Cohort Study: The Case of the Millennium Cohort Study.” International Journal of Social Research Methodology 10: 325–334. Doi: http://dx.doi.org/10.1080/13645570701676955.10.1080/13645570701676955Search in Google Scholar

Plewis, I. (Ed.). 2007b. The Millennium Cohort Study: Technical Report on Sampling (4th Ed.). London: Institute of Education, University of London. Available at: http://www.cls.ioe.ac.uk/library-media/documents/Technical_Report_on_Sampling_4th_Edition.pdf (accessed July 17, 2017).Search in Google Scholar

Plewis, I., S.C. Ketende, H. Joshi, and G. Hughes. 2008. “The Contribution of Residential Mobility to Sample Loss in a Birth Cohort Study: Evidence from the First two Waves of the Millennium Cohort Study.” Journal of Official Statistics 24: 365–385. Doi: http://dx.doi.org/10.1080/13645570701676955.10.1080/13645570701676955Search in Google Scholar

Plewis, I., S.C. Ketende, and L. Calderwood. 2012. “Assessing the Accuracy of Response Propensities in Longitudinal Studies.” Survey Methodology 38: 167–171.Search in Google Scholar

Pudney, S. and N. Watson. 2013. If at First you Don’t Succeed? Fieldwork, Panel Attrition, and Health-Employment Inferences in BHPS and HILDA, Institute for Social and Economic Research Working Papers, No. 2013–27, ISER, University of Essex. Available at: https://www.iser.essex.ac.uk/research/publications/working-papers/iser/2013-27 (accessed July 17, 2017).Search in Google Scholar

Rasbash, J., C. Charlton, W.J. Browne, M. Healy, and B. Cameron. 2009. MLwiN Version 2.1. Centre for Multilevel Modelling, University of Bristol. Available at: http://www.bristol.ac.uk/cmm/software/mlwin/download/manuals.html (accessed July 17, 2017).Search in Google Scholar

SAS Institute Inc. 2011. SAS/STAT® 9.3, User’s Guide. Cary, NC: SAS Institute Inc. Available at: https://support.sas.com/documentation/cdl/en/statug/63962/HTML/default/viewer.htm#titlepage.htm (accessed July 17, 2017).Search in Google Scholar

Schouten, B., F. Cobben, and J. Bethlehem. 2009. “Indicators of the Representativeness of Survey Response.” Survey Methodology 35: 101–113.Search in Google Scholar

Schouten, B., N. Shlomo, and C. Skinner. 2011. “Indicators for Monitoring and Improving Representativeness of Response.” Journal of Official Statistics 27: 231–253.Search in Google Scholar

Schouten, B. and N. Shlomo. 2017. “Selecting Adaptive Survey Design Strata with Partial R-indicators.” International Statistical Review 85: 143–163. Doi: http://dx.doi.org/10.1111/insr.12159.10.1111/insr.12159Search in Google Scholar

Shlomo, N., C. Skinner, and B. Schouten. 2012. “Estimation of an Indicator of the Representativeness of Survey Response.” Journal of Statistical Planning and Inference 142: 201–211. Doi: http://dx.doi.org/10.1016/j/jspi.2011.07.008.10.1016/j.jspi.2011.07.008Search in Google Scholar

Skinner, C. and J. D’Arrigo. 2011. “Inverse Probability Weighting for Clustered Nonresponse.” Biometrika 98: 953–966. Doi: http://dx.doi.org/10.1093/biomet/asr058.10.1093/biomet/asr058Search in Google Scholar

Watson, N. and M. Wooden. 2009. “Identifying Factors Affecting Longitudinal Survey Response.” In Methodology of Longitudinal Surveys, edited by P. Lynn, 157–182. Chichester: John Wiley. Doi: http://dx.doi.org/10.1002/9780470743874.ch10.10.1002/9780470743874.ch10Search in Google Scholar

Watson, N. and M. Wooden. 2014. “Re-Engaging with Survey Non-Respondents: Evidence from Three Household Panels.” Journal of the Royal Statistical Society, Series A 177: 499–523. Doi: http://dx.doi.org/10.1111/rssa.12024.10.1111/rssa.12024Search in Google Scholar

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