[
American Association for Public Opinion Research. 2016. Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys, (9th edition). Lanexa: AAPOR. Available at: https://www.aapor.org/AAPOR_Main/media/publications/Standard-Definitions20169theditionfinal.pdf (accessed October 2021).
]Search in Google Scholar
[
Antoni, M., and A. Bethmann. 2019. “PASS-ADIAB – Linked Survey and Administrative Data for Research on Unemployment and Poverty.” Journal of Economics and Statistics 239(4): 747–756. DOI: https://doi.org/10.1515/jbnst-2018-0002.10.1515/jbnst-2018-0002
]Search in Google Scholar
[
Antoni, M., K. Drasch, C. Kleinert, B. Matthes, M. Ruland, and A. Trahms. 2010. Working and Learning in a Changing World, part I: Overview of the study, FDZ Methodenreport No. 5/2010 (en). Nürnberg: Forschungsdatenzentrum (FDZ) der Bundesagentur für Arbeit im Institut für Arbeitsmarkt- und Berufsforschung (IAB). Available at: http://doku.iab.de/fdz/reporte/2010/MR_05-10_EN.pdf (accessed October 2021).
]Search in Google Scholar
[
Antoni, M., and S. Seth. 2011. ALWA-ADIAB – Linked individual Survey and Administrative Data for Substantive and Methodological Research, FDZ Methodenre-port No. 12/2011 (en). Nürnberg: Forschungsdatenzentrum (FDZ) der Bundesagentur für Arbeit im Institut für Arbeitsmarkt- und Berufsforschung (IAB). Available at: http://doku.iab.de/fdz/reporte/2011/MR_12-11_EN.pdf (accessed October 2021).
]Search in Google Scholar
[
Bee, C.A., G.M.R. Gathright, and B.D. Meyer. 2015. “Bias from Unit Non-Response in the Measurement of Income in Household Surveys.” Paper presented at the Joint Statistical Meetings of the American Statistical Association, August 9, 2015, Seattle, USA. Available at: http://www.solejole.org/16068.pdf (accessed February 2020).
]Search in Google Scholar
[
Behrens, K., and K. Wiese. 2019. “BIK-Regionen.” In Regionale Standards: Ausgabe 2019, edited by K. Beckmann, K. Behrens, H. Hoffmann, M. Pfister, K. Wiese, J.H.P. Hoffmeyer-Zlotnik, G. Rösch, P. Siegers, W. Sodeur, U. Hanefeld, R. Herter-Eschweiler, and E. Krack-Roberg: 114–126. Cologne, Germany: GESIS – Leibniz-Institut für Sozialwissenschaften.
]Search in Google Scholar
[
Blossfeld, H.P., H.G. Roßbach, and J. von Maurice (Eds.). 2011. Education as a Lifelong Process – The German National Educational Panel Study (NEPS), Zeitschrift für Erziehungswissenschaft: Sonderheft 14. Wiesbaden: Springer VS.
]Search in Google Scholar
[
Burgette, L.F., and J.P. Reiter. 2010. “Multiple Imputation for Missing Data via Sequential Regression Trees.” American Journal of Epidemiology 172(9): 1070–1076. DOI: https://doi.org/10.1093/aje/kwq260.10.1093/aje/kwq26020841346
]Search in Google Scholar
[
Burkam, D.T., and V.E. Lee. 1998. “Effects of Monotone and Nonmonotone Attrition on Parameter Estimates in Regression Models with Educational Data.” The Journal of Human Resources 33(2): 555–574. DOI: https://doi.org/10.2307/146441.10.2307/146441
]Search in Google Scholar
[
Calderwood, L., and C. Lessof. 2009. “Enhancing Longitudinal Surveys by Linking to Administrative Data.” In Methodology of Longitudinal Surveys, edited by P. Lynn: 55–72. West Sussex, England: John Wiley & Sons.10.1002/9780470743874.ch4
]Search in Google Scholar
[
Cheshire, H., D. Hussey, J. Medina, K. Pickering, N. Wood, K. Ward, K. Taylor, and C. Lessof. 2012. Financial circumstances, health and well-being of the older population in England: The 2008 English Longitudinal Study of Ageing: Wave 4 Technical Report. London: National Centre for Social Research. Available at: https://ifs.org.uk/elsa/report10/w4_tech.pdf (accessed October 2021).
]Search in Google Scholar
[
Christen. 2012. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Berlin, Germany: Springer. DOI: https://doi.org/10.1007/978-3-642-31164-2_2.10.1007/978-3-642-31164-2_2
]Search in Google Scholar
[
Cohen, S.B. 2005. “Integrated survey designs: A framework for nonresponse bias reduction.” Journal of Economic and Social Measurement 30(2–3): 101–114. DOI: https://doi.org/10.3233/JEM-2005-0244.10.3233/JEM-2005-0244
]Search in Google Scholar
[
Couper, M.P., and M.B. Ofstedal. 2009. “Keeping in Contact with Mobile Sample Members.” In Methodology of Longitudinal Surveys, edited by P. Lynn: 183–203. West Sussex, England: John Wiley & Sons.10.1002/9780470743874.ch11
]Search in Google Scholar
[
Couper, M.P. 2017. “New Developments in Survey Data Collection.” Annual Review of Sociology 43(1): 121–145. DOI: https://doi.org/10.1146/annurev-soc-060116-053613.10.1146/annurev-soc-060116-053613
]Search in Google Scholar
[
Davern, M.E., B.D. Meyer, and N.K. Mittag. 2019. “Creating Improved Survey Data Products Using Linked Administrative-Survey Data.” Journal of Survey Statistics and Methodology 7(3): 440–463. DOI: https://doi.org/10.1093/jssam/smy017.10.1093/jssam/smy017
]Search in Google Scholar
[
Edwards, B., L. Branden, and M. Stange. 2011. “Piggyback Survey Respondents and Mode: Lessons Learned from Design and Operations.” In JSM Proceedings, Survey Research Methods Section, August 1, 2011: 5009–5021. Alexandria, VA: American Statistical Association. Available at: http://www.asasrms.org/Proceedings/y2011/Files/302992_69483.pdf (accessed October 2021).
]Search in Google Scholar
[
Ezzati-Rice, T.M., F. Rohde, and J. Greenblatt. 2008. Sample Design of the Medical Expenditure Panel Survey Household Component, 1998–2007. Methodology Report No. 22. March 2008. Rockville, MD, USA: Agency for Healthcare Research and Quality. Available at: http://www.meps.ahrq.gov/mepsweb/data_files/publications/mr22/mr22.shtml (accessed February 2020).
]Search in Google Scholar
[
Freedman, V.A., K. McGonagle, and P. Andreski. 2014. The Panel Study of Income Dynamics: Linked Medicare Claims Data. PSID Technical Series Paper No. 14-01. University of Michigan. Available at: https://psidonline.isr.umich.edu/publications/Papers/tsp/2014-01_PSIDMedicare.pdf (accessed October 2021).
]Search in Google Scholar
[
Groves, R., F.J. Fowler, M.P. Couper, J.M. Lepkowski, E. Singer, and R. Tourangeau. 2009. Survey Methodology, (2nd ed.). Hoboken, NJ: John Wiley & Sons.
]Search in Google Scholar
[
Hammon, A., S. Zinn, C. Aßmann, and A. Würbach. 2016. Samples, Weights, and Nonresponse: The Adult cohort of the National Educational Panel Study (Wave 2 to 6). Bamberg, Germany: Leibniz Institute for Educational Trajectories, National Educational Panel Study. NEPS Survey Paper No. 7. DOI: https://doi.org/10.5157/NEPS:SP07:1.0.
]Search in Google Scholar
[
Hammon, A. 2018. Samples, Weights, and Nonresponse: The Adult Cohort of the National Educational Panel Study (Wave 7 to 9). Bamberg, Germany: Leibniz Institute for Educational Trajectories, National Educational Panel Study. Available at: https://www.neps-data.de/Portals/0/NEPS/Datenzentrum/Forschungsdaten/SC6/9-0-0/SC6_9-0-0_W.pdf (accessed October 2021).
]Search in Google Scholar
[
Hoonhout, P., and G. Ridder. 2019. “Nonignorable Attrition in Multi-Period Panels With Refreshment Samples.” Journal of Business & Economic Statistics 37(3): 377–390. DOI: https://doi.org/10.1080/07350015.2017.1345744.10.1080/07350015.2017.1345744
]Search in Google Scholar
[
Jacobebbinghaus, P., and S. Seth. 2007. “The German Integrated Employment Biographies Sample IEBS.” Schmollers Jahrbuch: Journal of Applied Social Science Studies 127: 335 – 342. Available at: https://www.ratswd.de/download/schmollers/2007_127/Schmollers_2007_2_S335.pdf (accessed October 2021).10.3790/schm.127.2.335
]Search in Google Scholar
[
Korbmacher, J., and C. Czaplicki. 2013. “Linking SHARE Survey Data with Administrative Records: First Experiences from SHARE-Germany.” In SHARE Wave 4: Innovations & Methodology, edited by F. Malter and A. Börsch-Supan: 47–53. Munich: MEA, Max Planck Institute for Social Law and Social Policy.
]Search in Google Scholar
[
Knies, G., J. Burton, and E. Sala. 2012. “Consenting to Health Record Linkage: Evidence from a Multipurpose Longitudinal Survey of a General Population.” BMC Health Services Research 12(52). DOI: https://doi.org/10.1186/1472-6963-12-52.10.1186/1472-6963-12-52333001422390416
]Search in Google Scholar
[
Knies, G., and J. Burton. 2014. “Analysis of four studies in a comparative framework reveals: health linkage consent rates on British cohort studies higher than on UK household panel surveys.” BMC Medical Research Methodology 14(125). DOI: https://doi.org/10.1186/1471-2288-14-125.10.1186/1471-2288-14-125428070125430545
]Search in Google Scholar
[
Kreuter, F., and U. Kohler. 2009. “Analyzing Contact Sequences in Call Record Data: Potential and Limitations of Sequence Indicators for Nonresponse Adjustments in the European Social Survey.” Journal of Official Statistics 25(2): 203–226.
]Search in Google Scholar
[
Kreuter, F., G. Müller, and M. Trappmann. 2010a. “Nonresponse and Measurement Error in Employment Research: Making Use of Administrative Data.” Public Opinion Quarterly 74: 880–906. DOI: https://doi.org/10.1093/poq/nfq060.10.1093/poq/nfq060
]Search 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. 2010b. “Using Proxy Measures and Other Correlates of Survey Outcomes to Adjust for Nonresponse: Examples from Multiple Surveys.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 173: 389–407. DOI: https://doi.org/10.1111/j.1467-985X.2009.00621.x.10.1111/j.1467-985X.2009.00621.x
]Search in Google Scholar
[
Kreuter, F. 2013. Improving Surveys with Paradata: Analytic Uses of Process Information. Hoboken, NJ: Wiley.10.1002/9781118596869
]Search in Google Scholar
[
Kroh, M. 2010. DIW Documentation of Sample Sizes and Panel Attrition in the German Socio Economic Panel (SOEP) (1984 until 2009). DIW Data Documentation 50. Berlin: German Institute for Economic Research. Available at: https://www.diw.de/documents/publikationen/73/diw_01.c.359697.de/diw_datadoc_2010-050.pdf (accessed October 2021).
]Search in Google Scholar
[
Lemay, M. 2009. Understanding the Mechanisms of Panel Attrition. PhD Dissertation. University of Maryland, College Park. Available at: http://hdl.handle.net/1903/9631 (accessed October 2021).
]Search in Google Scholar
[
Lepkowski, J.M., and M.C. Couper. 2002. “Nonresponse in the Second Wave of Longitudinal Household Surveys.” In Survey Nonresponse, edited by R.M. Groves, D.A. Dillman, J.L. Eltinge, and R.J.A. Little: 259–272. New York, NY: John Wiley & Sons.
]Search in Google Scholar
[
Lin, I., and N.C. Schaeffer. 1995. “Using Survey Participants to Estimate the Impact of Nonparticipation.” Public Opinion Quarterly 59: 236–258. DOI: https://doi.org/10.1086/269471.10.1086/269471
]Search in Google Scholar
[
Little, R.J.A., and S. Vartivarian. 2005. “Does Weighting for Nonresponse Increase the Variance of Survey Means?” Survey Methodology 31: 161–168. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2005002/article/9046-eng.pdf?st=3epbdduy (accessed October 2021).
]Search in Google Scholar
[
Meyer, B.D., and N. Mittag. 2019. “Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net.” American Economic Journal: Applied Economics 11(2): 176–204. DOI: https://doi.org/10.1257/app.20170478.10.1257/app.20170478
]Search in Google Scholar
[
Neukirch, T. 2002. “Nonignorable Attrition and Selectivity Biases in the Finnish Subsample of the ECHP: an Empirical Study Using Additional Register Information.” Chintex Working Paper 5. Frankfurt a. M.: J.W. Goethe-Universität. Available at: http://hdl.handle.net/10068/92455 (accessed October 2021).
]Search in Google Scholar
[
Olsen, R. 2018. “Panel Attrition.” In The Palgrave Handbook of Survey Research, edited by D. Vannette and J. Krosnick: 509–517. DOI: https://doi.org/10.1007/978-3-319-54395-6_59.10.1007/978-3-319-54395-6_59
]Search in Google Scholar
[
Olson, J.A. 1999. “Linkages with Data from Social Security Administrative Records in the Health and Retirement Study.” Social Security Bulletin 62(2): 73–85. Available at: https://www.ssa.gov/policy/docs/ssb/v62n2/v62n2p73.pdf (accessed October 2021).
]Search in Google Scholar
[
Pankowska, P., B. Bakker, D.L. Oberski, and D. Pavlopoulos. 2018. “Reconciliation of two data sources by correction for measurement error: the feasibility of parameter reuse.” Statistical Journal of the IAOS 34(3): 317–329. DOI: https://doi.org/10.3233/SJI-170368.10.3233/SJI-170368
]Search in Google Scholar
[
Pavlopoulos, D., and J.K. Vermunt. 2015. “Measuring temporary employment. Do survey or register data tell the truth?” Survey Methodology 41(1): 197–214. Available at: https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2015001/article/14151-eng.pdf?st=tAsC-b20 (accessed October 2021).
]Search in Google Scholar
[
Sakshaug, J.W., M. Antoni, and R. Sauckel. 2017. “The Quality and Selectivity of Linking Federal Administrative Records to Respondents and Nonrespondents in a General Population Survey in Germany.” Survey Research Methods 1: 63–80. DOI: https://doi.org/10.18148/srm/2017.v11i1.6718.10.23889/ijpds.v1i1.189
]Search in Google Scholar
[
Sakshaug, J.W., and M. Antoni. 2019. “Evaluating the Utility of Indirectly Linked Federal Administrative Records for Nonresponse Bias Adjustment.” Journal of Survey Statistics and Methodology 7: 227–249. DOI: https://doi.org/10.1093/jssam/smy009.10.1093/jssam/smy009
]Search in Google Scholar
[
Sakshaug, J.W., and M. Huber. 2016. “An Evaluation of Panel Nonresponse and Linkage Consent Bias in a Survey of Employees in Germany.” Journal of Survey Statistics and Methodology 4(1): 71–93. DOI: https://doi.org/10.1093/jssam/smv034.10.1093/jssam/smv034
]Search in Google Scholar
[
Sakshaug, J.W., and F. Kreuter. 2012. “Assessing the Magnitude of Non-Consent Biases in Linked Survey and Administrative Data.” Survey Research Methods 6(2): 113–122. DOI: https://doi.org/10.18148/srm/2012.v6i2.5094.
]Search in Google Scholar
[
Schaurer, I., and K. Weyandt. 2018. GESIS Panel Technical Report: Recruitment 2016 (Wave d11 and d12). Cologne, Germany: GESIS – Leibniz-Institut für Sozialwissenschaften.
]Search in Google Scholar
[
Scholz, J., A. Seshadri, and S. Khitatrakun. 2006. “Are Americans Saving ‘Optimally’ for Retirement?” Journal of Political Economy 114: 607–643. DOI: https://doi.org/10.1086/506335.10.1086/506335
]Search in Google Scholar
[
Short, K., and E. McArthur. 1986. “Life Events and Sample Attrition in the Survey of Income and Program Participation.” In Proceedings of the Survey Research Methods Section of the American Statistical Association: 200–205. Available at: http://www.asasrms.org/Proceedings/y1986f.html (accessed October 2021).
]Search in Google Scholar
[
Sin, C.H. 2006. “The feasibility of using national surveys to derive samples of older people from different ethnic groups in Britain: Lessons from ‘piggy-backing’ on the Family Resources Survey.” International Journal of Social Research Methodology 9(1): 15–28. DOI: https://doi.org/10.1080/13645570500435264.10.1080/13645570500435264
]Search in Google Scholar
[
Sinibaldi, J., M. Trappmann, and F. Kreuter. 2014. “Which is the better investment for nonresponse adjustment: Purchasing commercial auxiliary data or collecting interviewer observations?” Public Opinion Quarterly 78(2): 440–473. DOI: https://doi.org/10.1093/poq/nfu003.10.1093/poq/nfu003
]Search in Google Scholar
[
Smith, T.W. 2011. “The Report of the International Workshop on Using Multi-Level Data from Sample Frames, Auxiliary Databases, Paradata and Related Sources to Detect and Adjust for Nonresponse Bias in Surveys.” International Journal of Public Opinion Research 23: 389–402. DOI: https://doi.org/10.1093/ijpor/edr035.10.1093/ijpor/edr035
]Search in Google Scholar
[
Smith, T.W., and J. Kim. 2013. “An Assessment of the Multi-Level Integrated Database Approach.” The Annals of the American Academy of Political and Social Science 645: 185–221. DOI: https://doi.org/10.1177/0002716212463340.10.1177/0002716212463340
]Search in Google Scholar
[
Spieß, M., and M. Kroh. 2004. “Documentation of Sample Sizes and Panel Attrition in the German Socio Economic Panel (GSOEP).” Research Note No. 28a. Berlin, Germany: German Institute for Economic Research (DIW). Available at: https://www.diw.de/documents/publikationen/73/diw_01.c.43407.de/diw_datadoc_2004-001.pdf (accessed October 2021).
]Search in Google Scholar
[
StataCorp. 2017. Stata 15 Base Reference Manual. College Station, TX: Stata Press.
]Search in Google Scholar
[
Stöckinger, C., S. Kretschmer, and C. Kleinert. 2018. Panel Attrition in NEPS Starting Cohort 6: A Description of Attrition Processes in Waves 2 to 7 with Regard to Nonresponse Bias, NEPS Survey Paper No. 35. Bamberg, Germany: Leibniz Institute for Educational Trajectories, National Educational Panel Study.
]Search in Google Scholar
[
Taylor, M.F., Brice, J., Buck, N., and Prentice-Lane, E. (Eds.). 2010. British Household Panel Survey User Manual Volume A: Introduction, Technical Report and Appendices. Colchester: University of Essex.
]Search in Google Scholar
[
Taylor, R., L. Conway, L. Calderwood, C. Lessof, H. Cheshire, K. Cox, and S. Scholes. 2007. Health, wealth and lifestyles of the older population in England: The 2002 English Longitudinal Study of Ageing: Technical Report. London: Institute of Fiscal Studies. Available at: https://ifs.org.uk/elsa/report03/w1_tech.pdf (accessed October 2021).
]Search in Google Scholar
[
Trappmann, M., T. Gramlich, and A. Mosthaf. 2015. “The Effect of Events Between Waves on Panel Attrition.” Survey Research Methods 9(1): 31–43. DOI: https://doi.org/10.18148/srm/2015.v9i1.5849.
]Search in Google Scholar
[
UNECE (United Nations Economic Commission for Europe). 2007. Register-Based Statistics in the Nordic Countries: Review of Best Practices with Focus on Population and Social Statistics. Technical Report E.07.II.E.11. New York/Geneva: United Nations. Available at: http://www.unece.org/fileadmin/DAM/stats/publications/Register_based_statistics_in_Nordic_countries.pdf (accessed February 2020).
]Search in Google Scholar
[
Valliant, R., J.A. Dever, and F. Kreuter. 2013. Practical Tools for Designing and Weighting Survey Samples. New York: Springer. DOI: https://doi.org/10.1007/978-3-319-93632-1_13.10.1007/978-3-319-93632-1_13
]Search in Google Scholar
[
Watson, N., and M. Wooden. 2009. “Identifying Factors Affecting Longitudinal Survey Response.” In Methodology of Longitudinal Surveys, edited by P. Lynn: 157–181. West Sussex, England: John Wiley & Sons.10.1002/9780470743874.ch10
]Search in Google Scholar
[
Weinert, S., C. Artelt, M. Prenzel, M. Senkbeil, T. Ehmke, C.H. Carstensen, and K. Lockl. 2019. “Development of Competencies Across the Life Course.” In Education as a Lifelong Process: The German National Educational Panel Study (NEPS), Edition ZfE Vol. 3, edited by H.P. Blossfeld and H.G. Roßbach: 57–81. Wiesbaden: Springer VS. DOI: https://doi.org/10.1007/978-3-658-23162-0.10.1007/978-3-658-23162-0
]Search in Google Scholar
[
West, B.T., F. Kreuter, and M. Trappmann. 2014. “Is the Collection of Interviewer Observations Worthwhile in an Economic Panel Survey? New Evidence from the German Labor Market and Social Security (PASS) Study.” Journal of Survey Statistics and Methodology 2: 159–181. DOI: https://doi.org/10.1093/jssam/smu002.10.1093/jssam/smu002
]Search in Google Scholar
[
West, B.T., J. Wagner, F. Hubbard, and H. Gu. 2015. “The Utility of Alternative Commercial Data Sources for Survey Operations and Estimation: Evidence from the National Survey of Family Growth.” Journal of Survey Statistics and Methodology 3: 240–264. DOI: https://doi.org/10.1093/jssam/smv004.10.1093/jssam/smv004
]Search in Google Scholar
[
Young, A.F., A.J. Dobson, and J.E. Byles. 2001. “Health Services Research Using Linked Records: Who Consents and What is the Gain?” Australian and New Zealand Journal of Public Health 25(5): 417–420. DOI: https://doi.org/10.1111/j.1467-842X.2001.tb00284.x.10.1111/j.1467-842X.2001.tb00284.x
]Search in Google Scholar
