Publications

Free Access and Open Access Articles

2025

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Beblo, T., Pelster, S., Schilling, C., Kleinke, K., Iffland, B., Driessen, M., & Fernando, S. (2018). Breath versus emotions: The impact of different foci of attention during mindfulness meditation on the experience of negative and positive emotions. Behavior Therapy, 49(5), 702–714. https://doi.org/10.1016/j.beth.2017.12.006
Beiner, E., Hermes, M., Reichert, J., Kleinke, K., Vock, S., Löffler, A., Ader, L., Sirazitdinov, A., Keil, S., Schmidt, T., Schick, A., Löffler, M., Hopp, M., Ruckes, C., Hesser, J., Reininghaus, U., Flor, H., Eich, W., & Tesarz, J. (2025). Early-life adversity as a predictor of fibromyalgia syndrome: The central role of perceived stress over endocrine stress indicators. Pain. https://doi.org/10.1097/j.pain.0000000000003527
Bürgler, S., Kleinke, K., & Hennecke, M. (2022). The metacognition in self-control scale (MISCS). Personality and Individual Differences, 199, Article 111841. https://doi.org/10.1016/j.paid.2022.111841
de Haan, A., Kleinke, K., Degen, E., & Landolt, M. A. (2024). Longitudinal relationship between posttraumatic cognitions and internalising symptoms in children and adolescents. European Journal of Psychotraumatology, 15(1), Article 2398357. https://doi.org/10.1080/20008066.2024.2398357
de Haan, A., Landolt, M. A., Fried, E. I., Kleinke, K., Alisic, E., Bryant, R., Salmon, K., Chen, S.-H., Liu, S.-T., Dalgleish, T., McKinnon, A., Alberici, A., Claxton, J., Diehle, J., Lindauer, R., Roos, C. de, Halligan, S. L., Hiller, R., Kristensen, C. H., … Meiser-Stedman, R. (2020). Dysfunctional posttraumatic cognitions, posttraumatic stress and depression in children and adolescents exposed to trauma: A network analysis. Journal of Child Psychology and Psychiatry, 61(1), 77–87. https://doi.org/10.1111/jcpp.13101
Exner, A., Kampa, M., Finke, J. B., Stalder, T., Klapperich, H., Hassenzahl, M., Kleinke, K., & Klucken, T. (2023). Repressive and vigilant coping styles in stress and relaxation: Evidence for physiological and subjective differences at baseline, but not for differential stress or relaxation responses. Frontiers in Psychology, 14, Article 1196481. https://doi.org/10.3389/fpsyg.2023.1196481
Forstmeier, S., Zimmermann, S., van der Hal, E., Auerbach, M., Kleinke, K., Maercker, A., & Brom, D. (2023). Effect of life review therapy for holocaust survivors: A randomized controlled trial. Journal of Traumatic Stress, 36(3), 628–641. https://doi.org/10.1002/jts.22933
Huthsteiner, K., Finke, J. B., Peters, E. M. J., Kleinke, K., Klucken, T., & Stalder, T. (2025). What is the best sampling region for endocrine hair analysis? A comparison between the posterior vertex and occipital region and recommendation for standardization. Psychoneuroendocrinology, 117, Article 107457.
Kaiser, F., Oberwittler, D., Thielmann, I., Kleinke, K., & Greifer, N. (2024). When does criminal victimization undermine generalized trust? A weighted panel analysis of the effects of crime type, frequency, and variety. Social Science Research, 124, Article 103086. https://doi.org/10.1016/j.ssresearch.2024.103086
Kiendl, K., Wenzel, M., Kleinke, K., & Hennecke, M. (2024). Conscientiousness, trait self-control, and neuroticism predict individual differences in the variability of goal dimensions. European Journal of Personality, Article 08902070241295321. https://doi.org/10.1177/08902070241295321
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., Schlüter, E., & Christ, O. (2017). Strukturgleichungsmodelle mit Mplus: Eine praktische Einführung (2. Aufl.). de Gruyter.
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
Machulska, A., Eiler, T. J., Haßler, B., Kleinke, K., Brück, R., Jahn, K., Niehaves, B., & Klucken, T. (2023). Mobile phone-based approach bias retraining for smokers seeking abstinence: A randomized-controlled study. International Journal of Mental Health and Addiction, 1–22. https://doi.org/10.1007/s11469-023-01107-w
Machulska, A., Eiler, T. J., Kleinke, K., Grünewald, A., Brück, R., Jahn, K., Niehaves, B., & Klucken, T. (2021). Approach bias retraining through virtual reality in smokers willing to quit smoking: A randomized-controlled study. Behaviour Research and Therapy, 141, Article 103858. https://doi.org/10.1016/j.brat.2021.103858
Machulska, A., Kleinke, K., Eiler, T. J., Grünewald, A., Brück, R., Jahn, K., Niehaves, B., Gethmann, C. F., & Klucken, T. (2019). Retraining automatic action tendencies for smoking using mobile phone-based approach-avoidance bias training: A study protocol for a randomized controlled study. Trials, 20, Article 720. https://doi.org/10.1186/s13063-019-3835-0
Machulska, A., Kleinke, K., & Klucken, T. (2023). Same same, but different: A psychometric examination of three frequently used experimental tasks for cognitive bias assessment in a sample of healthy young adults. Behavior Research Methods, 55(3), 1332–1351. https://doi.org/10.3758/s13428-022-01804-9
Machulska, A., Rinck, M., Klucken, T., Kleinke, K., Wunder, J.-C., Remeniuk, O., & Margraf, J. (2022). ’Push it!’ or ’hold it!’? A comparison of nicotine-avoidance training and nicotine-inhibition training in smokers motivated to quit. Psychopharmacology, 239, 105–121. https://doi.org/10.1007/s00213-021-06058-5
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.