Missing Data and Multiple Imputation Related Publications
Featured free and open access articles
All MI-related publications
Kleinke, K. (2017). Multiple imputation under violated distributional assumptions – a systematic evaluation of the assumed robustness of predictive mean matching. Journal of Educational and Behavioral Statistics, 42(4), 371–404. https://doi.org/10.3102/1076998616687084
Kleinke, K. (2018). Multiple imputation by predictive mean matching when sample size is small. Methodology, 14(1), 3–15. https://doi.org/10.1027/1614-2241/a000141
Kleinke, K. (2021). Estimation of partially observed non-linear terms in a multilevel model: An evaluation of the robustness of ad hoc and state-of-the-art missing data methods. Psychological Test and Assessment Modeling, 63(3), 432–455. https://www.psychologie-aktuell.com/fileadmin/Redaktion/Journale/ptam-2021-3/PTAM__3-2021_6_kor.pdf
Kleinke, K., Fritsch, M., Stemmler, M., & Lösel, F. (2024). Multiple imputation of longitudinal data – a comparison of robust imputation methods regarding sample size requirements, with an application to corporal punishment data. In M. Stemmler, W. Wiedermann, & F. Huang (Eds.), Dependent data in social sciecnes research – forms, issues and methods of analysis (second edition) (pp. 565–588). Springer Nature. https://doi.org/10.1007/978-3-031-56318-8_23
Kleinke, K., Fritsch, M., Stemmler, M., Reinecke, J., & Lösel, F. (2021). Quantile regression-based multiple imputation of missing values – an evaluation and application to corporal punishment data. Methodology, 17(3), 205–230. https://doi.org/https://doi.org/10.5964/meth.2317
Kleinke, K., Jong, R. de, Spiess, M., & Reinecke, J. (2011). Multiple imputation of incomplete ordinary and overdispersed count data [Technical Report]. University of Bielefeld, Faculty of Sociology. https://kkleinke.de/static/pdf/2011_technical.pdf
Kleinke, K., & Reinecke, J. (2013a). countimp 1.0 – A multiple imputation package for incomplete count data (Technical Report 01-2013). University of Bielefeld, Faculty of Sociology. https://doi.org/10.13140/RG.2.1.3889.3286
Kleinke, K., & Reinecke, J. (2013b). Multiple imputation of incomplete zero-inflated count data. Statistica Neerlandica, 67(3), 311–336. https://doi.org/10.1111/stan.12009
Kleinke, K., & Reinecke, J. (2014). Multiple imputation of zero-inflated and overdispersed multilevel count data [Technical Report]. University of Bielefeld, Faculty of Sociology & Centre for Statistics. https://kkleinke.de/static/pdf/2014_technical.pdf
Kleinke, K., & Reinecke, J. (2015a). Multiple imputation of multilevel count data. In U. Engel, B. Jann, P. Lynn, A. Scherpenzeel, & P. Sturgis (Eds.), Improving Survey Methods: Lessons from Recent Research (pp. 381–396). Routledge, Taylor & Francis.
Kleinke, K., & Reinecke, J. (2015b). Multiple imputation of overdispersed multilevel count data. In U. Engel (Ed.), Survey Measurements. Techniques, Data Quality and Sources of Error (pp. 209–226). Campus/The University of Chicago Press.
Kleinke, K., & Reinecke, J. (2019). Countimp version 2 – A multiple imputation package for incomplete count data [Technical Report]. University of Siegen, Department of Education Studies; Psychology. https://kkleinke.de/countimp
Kleinke, K., & Reinecke, J. (2022). How to and how not to impute incomplete count data. In A. Hernández & I. Tomás (Eds.), Proceedings from the 9th European Congress of Methodology (pp. 86–92). Universitat de València. https://doi.org/10.7203/PUV-OA-438-5
Kleinke, K., & Reinecke, J. (2024). Multiple imputation of incomplete panel data based on a piecewise growth curve model – an evaluation and application to juvenile delinquency data. In M. Stemmler, W. Wiedermann, & F. Huang (Eds.), Dependent data in social sciecnes research – forms, issues and methods of analysis (second edition) (pp. 589–615). Springer Nature. https://doi.org/10.1007/978-3-031-56318-8_24
Kleinke, K., Reinecke, J., Salfrán, D., & Spiess, M. (2020). Applied multiple imputation. Advantages, pitfalls, new developments and applications in R. Springer Nature.
Kleinke, K., Reinecke, J., & Weins, C. (2021). The development of delinquency during adolescence: A comparison of missing data techniques revisited. Quality & Quantity, 55(3), 877–895. https://doi.org/10.1007/s11135-020-01030-5
Kleinke, K., Stemmler, M., Reinecke, J., & Lösel, F. (2011). Efficient ways to impute incomplete panel data. Advances in Statistical Analysis, 95, 351–373. https://doi.org/10.1007/s10182-011-0179-9
Spiess, M., Kleinke, K., & Reinecke, J. (2021). Proper multiple imputation of clustered or panel data. In P. Lynn (Ed.), Advances in longitudinal survey methodology (pp. 424–446). John Wiley & Sons.