[
Adigüzel, F., and M. Wedel. 2008. “Split questionnaire design for massive surveys.” Journal of Marketing Research 45(5): 608–617. DOI: https://doi.org/10.1509/jmkr.45.5.608.
]Search in Google Scholar
[
Andreadis, I., and E. Kartsounidou. 2020. “The impact of splitting a long onlin equestionnaire on data quality.” Survey Research Methods 14(1): 31–42. DOI: https://doi.org/10.18148/srm/2020.v14i1.7294.
]Search in Google Scholar
[
Beichelt, T., I. Hahn, F. Schimmelfennig, and S. Worschech. 2014. Civil society and democracy promotion. Springer. DOI: https://doi.org/10.1057/9781137291097.
]Search in Google Scholar
[
Best, H. and, C. Wolf. 2013. The SAGE handbook of regression analysis and causal inference. Sage. DOI: https://doi.org/10.4135/9781446288146.
]Search in Google Scholar
[
Cai, J., E.J. Candès, and Z. Shen. 2010. “A singular value thresholding algorithm for matrix completion.” SIAM Journal on Optimization 20(4): 1956–1982. DOI: https://doi.org/10.1137/080738970.
]Search in Google Scholar
[
Chipperfield, J.O., M.L. Barr, and D.G. Steel. 2018. “Split questionnaire designs: collecting only the data that you need through MCAR and MAR designs.” Journal of Applied Statistics 45(8): 1465–1475. DOI: https://doi.org/10.1080/02664763.2017.1375085.
]Search in Google Scholar
[
Chipperfield, J.O., and D.G. Steel. 2009. Design and Estimation for Split Questionnaire Surveys. Journal of Official Statistics 25(2): 227–244. Available at: https://ro.uow.edu.au/infopapers/3334/.
]Search in Google Scholar
[
Chipperfield, J.O., and D.G. Steel. 2011. “Efficiency of split questionnaire surveys.” Journal of Statistical Planning and Inference 141(5): 1925–1932. DOI: https://doi.org/10.1016/j.jspi.2010.12.003.
]Search in Google Scholar
[
Chipperfield, J.O., and D.G. Steel. 2012. “Multivariate random effect models with complete and incomplete data.” Journal of Multivariate Analysis 109: 146–155. DOI: https://doi.org/10.1016/j.jmva.2012.02.014.
]Search in Google Scholar
[
Davidov, E., J. Cieciuch, and P. Schmidt. 2018. “The cross-country measurement comparability in the immigration module of the European Social Survey 2014-15.” 12(1): 15–27. DOI: https://doi.org/10.18148/srm/2018.v12i1.7212.
]Search in Google Scholar
[
Dziura, J.D., L.A. Post, Q. Zhao, Z. Fu, and P. Peduzzi. 2013. “Strategies for dealing with missing data in clinical trials: from design to analysis.” The Yale journal of biology and medicine 86(3): 343. Available at: https://pubmed.ncbi.nlm.nih.gov/24058309/.
]Search in Google Scholar
[
Early, K. 2016. Dynamic question ordering: obtaining useful information while reducing user burden proposal. Ph. D. thesis, Carnegie Mellon University Pittsburgh, PA. DOI: https://doi.org/10.1184/R1/6716123.v1.
]Search in Google Scholar
[
Fang, F., W. Lan, J. Tong, and J. Shao. 2019. “Model averaging for prediction with fragmentary data.” Journal of Business & Economic Statistics 37(3): 517–527. DOI: https://doi.org/10.1080/07350015.2017.1383263.
]Search in Google Scholar
[
Hoerl, A.E. and R.W. Kennard (1970). “Ridge regression: biased estimation for nonorthogonal problems.” Technometrics 12(1): 55–67. DOI: Technometrics 12(1): 55–67. DOI: https://doi.org/10.1080/00401706.1970.10488634.
]Search in Google Scholar
[
Ioannidis, E., T. Merkouris, L.-C. Zhang, M. Karlberg, M. Petrakos, F. Reis, and P. Stavropoulos. 2016. “On a Mmodular Approach to the Design of Integrated Social Surveys.” Journal of Official Statistics 32(2): 259–286. DOI: https://doi.org/10.1515/-jos-2016-0013.
]Search in Google Scholar
[
Ledoit, O., and M. Wolf. 2004. “A well-conditioned estimator for large-dimensional covariance matrices.” Journal of Multivariate Analysis 88(2): 365–411. DOI: https://doi.org/10.1016/S0047-259X(03)00096-4.
]Search in Google Scholar
[
Lesperance, M.L., and J.D. Kalbfleisch. 1992. “An algorithm for computing the nonparametric MLE of a mixing distribution.” Journal of the American Statistical Association 87(417): 120–126. DOI: https://doi.org/10.1080/01621459.1992.10475182.
]Search in Google Scholar
[
Little, R.J. 1992. “Regression with missing X’s: a review.” Journal of the American statistical association 87(420): 1227–1237. DOI: https://doi.org/10.1080/01621459.1992.10476282.
]Search in Google Scholar
[
Little, R.J., and M.D. Schluchter. 1985. “Maximum likelihood estimation for mixed continuous and categorical data with missing values.” Biometrika 72(3): 497–512. DOI: https://doi.org/10.1093/biomet/72.3.497.
]Search in Google Scholar
[
Liu, M., and L. Wronski. 2018. “Examining completion rates in web surveys via over 25,000 real-world surveys.” Social Science Computer Review 36(1): 116–124. DOI: https://doi.org/10.1177/0894439317695581.
]Search in Google Scholar
[
Mazumder, R., T. Hastie, and R. 2010. “Spectral regularization algorithms for learning large incomplete matrices.” Journal of Machine Learning Research 11: 2287–2322. DOI: https://dl.acm.org/doi/10.5555/1756006.1859931.
]Search in Google Scholar
[
Merkouris, T. 2015. “An efficient estimation method for matrix survey sampling.” Survey Methodology 41(1): 237–262. DOI: https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201500114174.
]Search in Google Scholar
[
Neidorf, T., and M. Sheehan. 2014. “National Assessment of Educational Progress (NAEP).” In Encyclopedia of Science Education., edited by R. Gunstone. Dordrecht: Springer. DOI: https://doi.org/10.1007/978-94-007-6165-0_67-2.
]Search in Google Scholar
[
Peytchev, A., and E. Peytcheva. 2017. “Reduction of measurement error due to survey length: evaluation of the split questionnaire design approach.” Survey Research Methods 11(4): 361–368. DOI: https://doi.org/10.18148/srm/2017.v11i4.7145.
]Search in Google Scholar
[
Raghunathan, T.E., and J.E. Grizzle. 1995. “A split questionnaire survey design.” Journal of the American Statistical Association 90(429): 54–63. DOI: https://doi.org/10.1080/01621459.1995.10476488.
]Search in Google Scholar
[
Rhemtulla, M., and T.D. Little. 2012. “Planned missing data designs for research in cognitive development.” Journal of Cognition and Development 13(4): 425–438. DOI: https://doi.org/10.1080/15248372.2012.717340.
]Search in Google Scholar
[
Rust, K.F., and E.G. Johnson. 1992. “Chapter 2: Sampling and weighting in the national assessment.” Journal of Educational Statistics 17(2): 111–129. DOI: https://doi.org/10.3102/10769986017002111.
]Search in Google Scholar
[
Schnaudt, C., M. Weinhardt, R. Fitzgerald, and S. Liebig. 2014. “The European Social Survey: contents, design, and research potential.” Journal of Contextual Economics: Schmollers Jahrbuch 134(4): 487–506. DOI: https://doi.org/10.3790/schm.134.4.487.
]Search in Google Scholar
[
Skinner, C.J., and O. Coker. 1996. “Regression analysis for complex survey data with missing values of a covariate.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 159(2): 265–274. DOI: https://doi.org/10.2307/2983173.
]Search in Google Scholar
[
Van Ham, C., J.J. Thomassen, K. Aarts, and R.B. Andeweg. 2017. Myth and reality of the legitimacy crisis: explaining trends and cross-national differences in established democracies. Oxford University Press. DOI: https://doi.org/10.1093/oso/9780198793717.001.0001.
]Search in Google Scholar
[
Vonneilich, N., D. Lüdecke, and O. von dem Knesebeck. 2019. “Educational inequalities in self-rated health and social relationships–analyses based on the European Social Survey 2002–2016.” Social Science and Medicine. DOI: https://doi.org/10.1016/j.-socscimed.2019.112379.
]Search in Google Scholar
[
Wu, H., and S.-O. Leung. 2017. “Can likert scales be treated as interval scales? – a simulation study.” Journal of Social Service Research 43(4): 527–532. DOI: https://doi.org/10.1080/01488376.2017.1329775.
]Search in Google Scholar
[
Xiang, S., L. Yuan, W. Fan, Y. Wang, P.M. Thompson, and J. Ye. 2014. “Bi-level multi-source learning for heterogeneous block-wise missing data.” NeuroImage 102: 192–206. DOI: https://doi.org/10.1016/j.neuroimage.2013.08.015.
]Search in Google Scholar
[
Yang, S., and J.K. Kim 2017. “A semiparametric inference to regression analysis with missing covariates in survey data.” Statistica Sinica 27: 261–285. DOI: https://doi.org/10.5705/ss.2014.174.
]Search in Google Scholar
[
Yu, G., Q. Li, D. Shen, and Y. Liu. 2020. “Optimal sparse linear prediction for blockmissing multi-modality data without imputation.” Journal of the American Statistical Association 115(531): 1406–1419. DOI: https://doi.org/10.1080/01621459.2019.1632079.
]Search in Google Scholar
[
Yuan, K.-H., and P.M. Bentler. 2010. “Consistency of normal-distribution-based pseudo maximum likelihood estimates when data are missing at random.” American Statistician 64(3): 263–267. DOI: https://doi.org/10.1198/tast.2010.09203.
]Search in Google Scholar