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  • Doebler, P., Doebler, A., Buczak, P., & Groll, A. (online first). Interactions of Scores Derived from Two Groups of Variables: Alternating Lasso Regularization Avoids Overfitting and Finds Interpretable Scores. Psychological Methods.[DOI].


  • Buczak, P., Huang, H., Forthmann, B., & Doebler, P. (2023). The Machines Take Over: A Comparison of Various Supervised Learning Approaches for Automated Scoring of Divergent Thinking Tasks. The Journal of Creative Behavior, 57, 17–36. [DOI].


  • Brunn, G., Freise, F., & Doebler, P. (2022). Modeling a smooth course of learning and testing individual deviations from a global course. Journal for educational research online, 14, 89–121. [DOI].
  • Raykov, T., Doebler, P., & Marcoulides, G. A. (2022). Applications of Bayesian Confirmatory Factor Analysis in Behavioral Measurement: Strong Convergence of a Bayesian Parameter Estimator. Measurement: Interdisciplinary Research and Perspectives, 20(4), 215–227.
  • Welz, T., Doebler, P., & Pauly, M. (2022). Fisher transformation based Confidence Intervals of Correlations in Fixed- and Random-Effects Meta-Analysis. British Journal of Mathematical and Statistical Psychology, 75(1). [DOI].
  • Doebler, A., & Doebler, P. (2022). Rotate and Project: Measurement of the Intended Concept with Unidimensional Item Response Theory from Multidimensional Ordinal Items. Multivariate Behavioral Research, 57(1), 40–56. [DOI].


  • Stadler, M., Doebler, P., Mertins, B., & Delucchi Danhier, R. (2021). Statistical Modeling of Dynamic Eye Tracking Experiments: Relative Importance of Visual Stimulus Elements for Gaze Behavior in the Multi-Group Case. Behavior Research Methods, 53(6), 2650–2667. [DOI].
  • Forthmann, B. & Doebler, P. (2021). Reliability of Researcher Capacity Estimates and Count Data Dispersion: A Comparison of Poisson, Negative Binomial, and Conway-Maxwell Poisson Models. Scientometrics, 126, 3337–3354. [DOI].
  • DeVries, J. M., Szardenings, C., Doebler, P. & Gebhardt, M. (2021). Subject-Specific Self-Concept and Global Self-Esteem Mediate Risk Factors for Lower Competency in Mathematics and Reading. Social Sciences, 10(1), 11p. [DOI].


  • Forthmann, B., Gühne, D. & Doebler, P. (2020). Revisiting Dispersion in Count Data Item Response Theory Models: The Conway-Maxwell-Poisson Counts Model. British Journal of Mathematical and Statistical Psychology, 73(S1), 32–50. [DOI].
  • Beisemann, M., Doebler, P. & Holling, H. (2020). Comparison of random-effects metaanalysis models for the relative risk in the case of rare events: A simulation study. Biometrical Journal, 62(7), 1597–1630. [DOI].
  • Beisemann, M.,Wartlick, O. & Doebler, P. (2020). Comparison of Recent Acceleration Techniques for the EM algorithm in One-and Two-Parameter Logistic IRT models. Psych, 2(4), 209–252. [DOI]
  • Ravand, H., Baghaei, P. & Doebler, P. (2020). Examining Parameter Invariance in a General Diagnostic Classification Model. Frontiers in Psychology, 10, 10 p. [DOI].
  • Büscher, R., Beisemann, M., Doebler, P., Steubl, L., Domhardt, M., Cuijpers, P., Kerkhof, A. & Sander, L. B. (2020). Effectiveness of Internet- and Mobile-Based Cognitive Behavioral Therapy to Reduce Suicidal Ideation and Behaviours: Protocol for a Systematic Review and Meta-Analysis of Individual Participant Data. International Journal of Environmental Research and Public Health, 17(14), 11p. [DOI].
  • Domhardt, M., Letsch, J., Kybelka, J., Königsbauer, J., Doebler, P. & Baumeister, H. (2020). Are Internet- and mobile-based interventions effective in adults with diagnosed panic disorder and/or agoraphobia? A systematic review and meta-analysis. Journal of Affective Disorders, 276, 169–182. [DOI].
  • DeVries, J. M., Szardenings, C., Doebler, P. & Gebhardt, M. (2020). Individualized Assignments, Group Work and Discussions: How They InteractWith Class Size, Low Socioeconomic Status, and Second Language Learners. Frontiers in Education, 5(65), 13p. [DOI].
  • Forthmann, B., Grothjahn, R., Doebler, P. & Baghaei, P. (2020). A Comparison of Different Item Response Theory Models for Scaling Speeded C-Tests. Journal of Psychoeducational Assessment, 38. [DOI].
  • Rupp, C., Gühne, D., Falke, C., Doebler, P., Andor, F. & Buhlmann, U. (2020). Comparing effects of detached mindfulness and cognitive restructuring in obsessivecompulsive disorder using ecological momentary assessment. Clinical Psychology & Psychotherapy, 27, 193–202. [DOI].
  • Nelson, J., Klumparendt, A., Doebler, P. & Ehring, T. (2020). Everyday Emotional Dynamics in Major Depression. Emotion, 20(2), 179–191. [DOI].


  • Baghaei, P. & Doebler, P. (2019). Introduction to the Rasch Poisson Counts Model: An R Tutorial. Psychological Reports, 122(5). [DOI].
  • Rupp, C., Jürgens, C., G¨uhne, D., Doebler, P., Andor, F. & Buhlmann, U. (2019). A study on treatment sensitivity of ecological momentary assessment in obsessive-compulsive disorder. ClinicalPsychology&Psychotherapy, 26, 695– 706. [DOI].
  • Rupp, C., Jürgens, C., Doebler, P., Andor, F. & Buhlmann, U. (2019). A randomized waitlist-controlled trial comparing detached mindfulness and cognitive restructuring in obsessive-compulsive disorder. PLoS ONE, 14(3), e0213895. [DOI].
  • Jürgens,C.,Rupp,C.,Doebler,P.,Andor,F.&Buhlmann,U.(2019).Metacognitionin obsessive-compulsive disorder symptom dimensions: Role of fusion beliefs, beliefs about rituals and stop signals. Journal of Obsessive-Compulsive and Related Disorders, 21, 102–111. [DOI].
  • Olderbak, S., Semmler, M. & Doebler, P. (2019). Four-Branch Model of Ability Emotional Intelligence with Fluidand Crystallized Intelligence:AMeta-Analysis of Relations. Emotion Review, 11(2), 166–183. [DOI].
  • Forthmann, B., Wilken, A., Doebler, P. & Holling, H. (2019). Strategy Induction Enhances Creativity in Figural Divergent Thinking. Journal of Creative Behavior, 53(1), 18–29. [DOI].
  • Doebler, P., Brunn, G. & Freise, F. (2019). Das Unnmessbare messen: Statistik, Intelligenz und Bildung. In W. Krämer & C. Weihs (Hrsg.), Faszination Statistik (S. 170–176). Berlin, Springer.


  • Hartung, J., Doebler, P., Schroeders, U. & Wilhelm, O. (2018). Dedifferentiation and Differentiation of Intelligence in Adults Across Age and Years of Education. Intelligence, 69, 37–49. [DOI].
  • Loe, B. S., Sun, L., Simonfy, F. & Doebler, P. (2018). Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models. Journal of Intelligence, 6(20), 25p. [DOI].
  • Doebler, P., Bürkner, P.-C. & R¨ucker, G. (2018). Statistical Packages for Diagnostic Meta-Analysis and Their Application. In DiagnosticMeta-Analysis: A Useful Tool for Clinical Decision-Making (S. 161–181). New York, Springer.


  • Bürkner, P.-C., Doebler, P. & Holling, H. (2017). Optimal design of the Wilcoxon-Mann-Whitney-test. Biometrical Journal, 59 (1), 25–40. [DOI].
  • Königbauer, J., Letsch, J., Doebler, P., Ebert, D. & Baumeister, H. (2017). Internet-and Mobile-based Depression Interventions for People with Diagnosed Depression: A Systematic Review and Meta-analysis. Journal of Affective Disorders,223, 28–40. doi:10.1016/j.jad.2017.07.021.
  • Kröll, C., Doebler, P., & Nüesch, S. (2017). Meta-analytic evidence of the effectiveness of stress management at work. European Journal of Work and Organizational Psychology, 26, 677--693. [DOI]
  • Nelson, J., Klumparendt, A., Doebler, P. & Ehring, T. (2017). Childhood maltreatment and characteristics of adult depression: a meta-analysis. British Journal of Psychiatry. 210, 96–104. [DOI]
  • Rupp, C., Doebler, P., Ehring, T. & Voßbeck-Elsebusch, A. (2017). Emotional Processing Theory put to test: A meta-analysis on the association between process and outcome measures in exposure therapy. Clinical Psychology & Psychotherapy, 24 (3), 697–711. doi:10.1002/cpp.2039.
  • Schwenk, C., Kuhn, J., Gühne, D., Doebler, P. & Holling, H. (2017). Auf Goldmünzenjagd: Psychometrische Kennwerte verschiedener Scoringansätze bei computergestützter Lernverlaufsdiagnostik im Bereich Mathematik. Empirische Sonderpädagogik, 2, 123–142.
  • Schwenk, C., Sasanguie, D., Kuhn, J.-T., Kempe, S., Doebler, P., & Holling, H. (2017). (Non-)symbolic magnitude processing in children with mathematical difficulties: a meta-analysis. Research in Developmental Disabilities.64, 152–167 [DOI]


  • Doebler, P. & Scheffler, B. (2016). The relationship of choice reaction time variability and intelligence: a meta-analysis. Learning and Individual Differences, 52, 157–166. [DOI]


  • Alavash, M., Doebler, P., Holling, H., Thiel, C. & Giessing, C. (2015). Is functional integration of resting state brain networks an unspecific biomarker for working memory performance? NeuroImage, 108, 182–193. [DOI]
  • Biehler, M., Holling, H. & Doebler, P. (2015). Saddlepoint approximations of the distribution of the person parameter in the two parameter logistic model. Psychometrika, 80, 665–688. [DOI]
  • Doebler, P., Alavash, M. & Giessing, C. (2015). Adaptive experiments with a multi-variate Elo-type algorithm. Behavior Research Methods, 47 (2), 384–394. [DOI]
  • Doebler, P. & Holling, H. (2015). Meta-analysis of diagnostic accuracy and ROC curves with covariate adjusted semiparametric mixtures. Psychometrika, 80, 1084–1104. [DOI]


  • Bürkner, P. C. & Doebler, P. (2014). Testing for publication bias in diagnostic meta-analysis: a simulation study. Statistics in Medicine, 33, 3061–3077. [DOI]
  • Doebler, A., Doebler, P. & Holling, H. (2014). A latent ability model for count data and an application to processing speed. Applied Psychological Measurement, 38, 587–598. [DOI]


  • Doebler, A., Doebler, P. & Holling, H. (2013). Optimal and most exact confidence intervals for person parameters in item response theory models. Psychometrika, 78 (1), 98–115. [DOI]
  • Doebler, P. (2013). Rado’s conjecture implies that all stationary set preserving forcings are semiproper. Journal of Mathematical Logic, 13 (1), 8 pages. [DOI]
  • Doebler, P. & Schindler, R. (2013). The extender algebra and vagaries of Σ2 absoluteness. Münster Journal of Mathematics, 6 (1), 117–166.


  • Doebler, P., Holling, H. & Böhning, D. (2012). A mixed model approach to meta-analysis of diagnostic studies with binary test outcome. Psychological Methods, 17 (3), 418–436. [DOI]


  • Doebler, P. & Schindler, R. (2009). Π2 consequences of BMM + NSω1 is precipitous and the semiproperness of stationary set preserving forcings. Mathematical Research Letters, 16 (5), 797–815.