Henninger, M., & Strobl, C. (2024). Local interpretation techniques for machine learning methods: Theoretical background, pitfalls and interpretation of LIME and shapley values. In PsyArXiv.
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Rothacher, Y., & Strobl, C. (2023). Identifying informative predictor variables with random forests. Journal of Educational and Behavioral Statistics.
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Debelak, R., & Strobl, C. (2024). Violations of unidimensionality and differential item functioning. In S. Greiff, K. Schweizer, & S. Troche (Eds.), Method effects in the psychological measurement. Hogrefe.
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Henninger, M., Debelak, R., & Strobl, C. (2023). A new stopping criterion for Rasch trees based on the Mantel-Haenszel effect size measure for differential item functioning. Educational and Psychological Measurement, 83(1), 181–212.
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Henninger, M., Debelak, R., Rothacher, Y., & Strobl, C. (2023). Interpretable machine learning for psychological research: Opportunities and pitfalls. Psychological Methods.
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Fellinghauer, C., Debelak, R., & Strobl, C. (2023). What Affects the Quality of Score Transformations? Potential issues in True-Score Equating using the Partial Credit model. Educational and Psychological Measurement, 83(6), 1249–1290.
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Strobl, C., & Leisch, F. (2022). Against the "one method fits all data sets" philosophy for comparison studies in methodological research. Biometrical Journal, 1–8.
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Debelak, R., Strobl, C., & Zeigenfuse, M. D. (2022). An introduction to the Rasch model with examples in R (p. 322). Chapman & Hall/CRC.
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Schneider, L., Strobl, C., Zeileis, A., & Debelak, R. (2022). An R toolbox for score–based measurement invariance tests in IRT models. Behavior Research Methods, 54, 2101–2113.
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Hothorn, T., Hornik, K., Strobl, C., & Zeileis, A. (2022). party: A laboratory for recursive part(y)itioning.
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Handing, E. P., Strobl, C., Jiao, Y., Feliciano, L., & Aichele, S. (2022). Predictors of depression among middle-aged and older men and women in europe: A machine learning approach. Lancet Regional Health-Europe.
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Zeileis, A., Strobl, C., Wickelmaier, F., Komboz, B., Kopf, J., Schneider, L., & Debelak, R. (2022). psychotools: Infrastructure for psychometric modeling.
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Zeileis, A., Strobl, C., Wickelmaier, F., Kopf, J., Dreifuss, D., & Debelak, R. (2022). psychotree: Recursive partitioning based on psychometric models.
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Debelak, R., Pawel, S., Strobl, C., & Merkle, E. C. (2022). Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theory. British Journal of Mathematical and Statistical Psychology, 75(3), 728–752.
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Philipp, M., Strobl, C., Zeilei, A., Rusch, T., Hornik, K., & Schneider, L. (2022). stablelearner: Stability assessment of statistical learning methods.
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Strobl, C., Kopf, J., Kohler, L., von Oertzen, T., & Zeileis, A. (2021). Anchor point selection: Scale alignment based on an inequality criterion. Applied Psychological Measurement, 45(3), 214–230.
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Debeer, D., Hothorn, T., & Strobl, C. (2021). permimp: Conditional permutation importance.
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Schweinsberg, M., Feldman, M., Staub, N., van den Akker, O., van Aert, R., van Assen, M., Liu, Y., Althoff, T., Heer, J., Kale, A., Strobl, C., & Uhlmannn, E. L. (2021). Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis. Organizational Behavior and Human Decision Processes.
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Strobl, C., Kopf, J., Zeileis, A., & Schneider, L. (2021). Using the raschtree function for detecting differential item functioning in the Rasch model.
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Huelmann, T., Debelak, R., & Strobl, C. (2020). A comparison of aggregation rules for selecting anchor items in multi group DIF analysis. Journal of Educational Measurement, 57(2), 185–215.
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Neve-Seyfarth, T., Löwe, C., & Strobl, C. (2020). Best practice for a good life balance. In feminno – Successful innovation. A guideline for female scientists in the life sciences at Swiss universities. (pp. 42–55). Zurich-Basel Plant Science Center.
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Debeer, D., & Strobl, C. (2020). Conditional permutation importance revisited. BMC Bioinformatics, 21(1), 307.
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Schneider, L., Zeileis, A., & Strobl, C. (2020). Descriptive and graphical analysis of the variable and cutpoint selection inside random forests.
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Gloor, J., Carolin, S., & Debelak, R. (2020). DSI insights: Wege aus der Angst vor Algorithmen. Inside IT Kolumne.
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Fokkema, M., & Strobl, C. (2020). Fitting prediction rule ensembles to psychological research data: An introduction and tutorial. Psychological Methods, 25(5), 636–652.
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Hundt, M., Rautionaho, P., & Strobl, C. (2020). Progressive or simple? A corpus-based study of aspect in World Englishes. Corpora, 15(1), 77–106.
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Frick, H., Strobl, C., Leisch, F., Zeileis, A., & Wickelmaier, F. (2020). psychomix: Psychometric mixture models.
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Debelak, R., & Strobl, C. (2019). Investigating measurement invariance by means of parameter instability tests for 2PL and 3PL models. Educational and Psychological Measurement, 79(2), 385–398.
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Philipp, M., Rusch, T., Hornik, K., & Strobl, C. (2018). Measuring the stability of results from supervised statistical learning. Journal of Computational and Graphical Statistics, 27(4), 685–700.
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Philipp, M., Strobl, C., Torre, J. de la, & Zeileis, A. (2018). On the estimation of standard errors in cognitive diagnosis models. Journal of Educational and Behavioral Statistics, 43(1), 88–115.
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Meiser, T., Eid, M., Carstensen, C., Erdfelder, E., Gollwitzer, M., Pohl, S., Steyer, R., & Strobl, C. (2018). Positionspapier zur Rolle der Psychologischen Methodenlehre in Forschung und Lehre. Psychologische Rundschau, 69, 325–331.
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Wang, T., Strobl, C., Zeileis, A., & C. Merkle, E. (2018). Score-based tests of differential item functioning via pairwise maximum likelihood estimation. Psychometrika, 132–155.
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Meiser, T., Eid, M., Carstensen, C., Erdfelder, E., Gollwitzer, M., Pohl, S., Steyer, R., & Strobl, C. (2018). Stellungnahme zum Diskussionsforum. Psychologische Rundschau, 69, 362–365.
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Bollmann, S., Cook, D., Dumas, J., Fox, J., Josse, J., Keyes, O., Strobl, C., Turner, H., & Debelak, R. (2017). A first survey on the diversity of the R community. The R Journal, 9(2), 541–552.
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Komboz, B., Strobl, C., & Zeileis, A. (2017). Tree-based global model tests for polytomous Rasch models. Educational and Psychological Measurement, 78(1), 128–166.
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Philipp, M., Zeileis, A., & Strobl, C. (2016). A toolkit for stability assessment of tree-based learners. In I. A. Colubi, A. Blanco, & C. Gatu (Eds.), Proceedings of COMPSTAT 2016 – 22nd international conference on computational statistics (pp. 315–325). The International Statistical Institute/International Association for Statistical Computing. https://doi.org/
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Kopf, J., Zeileis, A., & Strobl, C. (2015). A framework for anchor methods and an iterative forward approach for DIF detection. Applied Psychological Measurement, 39(2), 83–103.
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Kopf, J., Zeileis, A., & Strobl, C. (2015). Anchor selection strategies for DIF analysis: Review, assessment, and new approaches. Educational and Psychological Measurement, 75(1), 22–56.
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Mueller, J., Wende, B., Strobl, C., Eugster, M., Gallenberger, I., Floren, A., Steffan-Dewenter, I., Linsenmair, K. E., Weisser, W. W., & Gossner, M. M. (2015). Forest management and regional tree composition drive the host preference of saproxylic Beetle communities. Journal of Applied Ecology, 52(3), 753–762.
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Frick, H., Strobl, C., & Zeileis, A. (2015). Rasch mixture models for DIF detection: A comparison of old and new score specifications. Educational and Psychological Measurement, 75(2), 208–234.
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Strobl, C., Kopf, J., & Zeileis, A. (2015). Rasch trees: A new method for detecting differential item functioning in the Rasch model. Psychometrika, 80(2), 289–316.
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Eugster, M. J. A., Leisch, F., & Strobl, C. (2014). (Psycho-)analysis of benchmark experiments – A formal framework for investigating the relationship between data sets and learning algorithms. Computational Statistics & Data Analysis, 71(SI), 986–1000.
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Hapfelmeier, A., Hothorn, T., Ulm, K., & Strobl, C. (2014). A new variable importance measure for random forests with missing data. Statistics and Computing, 24(1), 21–34.
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Strobl, C. (2014). Discussion to Wei-Yin Lohs "Fifty years of classification and regression trees". International Statistical Review, 82(3), 349–352.
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Boulesteix, A.-L., Janitza, S., Hapfelmeier, A., Van Steen, K., & Strobl, C. (2014). Letter to the editor: On the term ’interaction’ and related phrases in the literature on random forests. Briefings in Bioinformatics, 16(2), 338–345.
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Frick, H., Strobl, C., & Zeiles, A. (2014). To split or to mix? Tree vs. Mixture models for detecting subgroups. In M. Gilli, G. González-Rodríguez, & A. Nieto-Reyes (Eds.), COMPSTAT 2014 – proceedings in computational statistics (pp. 379–386). The International Statistical Institute/International Association for Statistical Computing.
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Janitza, S., Strobl, C., & Boulesteix, Anne-Laure. (2013). An AUC-based permutation variable importance measure for random forests. BMC Bioinformatics, 14, 119.
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Strobl, C. (2013). Data mining. In T. Little (Ed.), The oxford handbook on quantitative methods (pp. 678–700). Oxford University Press USA.
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Sauer, S., Strobl, C., Walach, H., & Kohls, N. (2013). Rasch-Analyse des Freiburger Fragebogens zur Achtsamkeit. Diagnostica, 59(2), 86–99.
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Kopf, J., Augustin, T., & Strobl, C. (2013). The potential of model-based recursive partitioning in the social sciences: Revisiting Ockam’s Razor. In J. McArdle & G. Ritschard (Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (pp. 75–95). Routledge.
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Wickelmaier, F., Strobl, C., & Zeileis, A. (2012). Psychoco: Psychometric computing in R. Journal of Statistical Software, 48(7), 1–5.
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Boulesteix, A.-L., Bender, A., Lorenzo Bermejo, J., & Strobl, C. (2012). Random forest Gini importance favours SNPs with large minor allele frequency: Impact, sources and recommendations. Briefings in Bioinformatics, 13(3), 292–304.
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Strobl, C., Wickelmaier, F., & Zeileis, A. (2011). Accounting for individual differences in Bradley-Terry models by means of recursive partitioning. Journal of Educational and Behavioral Statistics, 36(2), 135–153.
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Strobl, C. (2011). Contributions to psychometric computing and machine learning [Habilitation Thesis in Statistics]. Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
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Carolin, S. (2010). Advances in social science research using R (book review). Journal of Statistical Software, 34(2), 1–2.
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Rieger, A., Hothorn, T., & Strobl, C. (2010). Random forests with missing values in the covariates (Technical Report 79). Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
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Nicodemus, K. K., Malley, J. D., Strobl, C., & Ziegler, A. (2010). The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics, 11, 110.
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Strobl, C., Kopf, J., & Zeileis, A. (2010). Wissen Frauen weniger oder nur das Falsche? – Ein statistisches Modell für unterschiedliche Aufgaben-Schwierigkeiten in Teilstichproben. In S. Trepte & M. Verbeet (Eds.), Allgemeinbildung in Deutschland – Erkenntnisse aus dem SPIEGEL Studentenpisa-Test (pp. 255–272). VS Verlag.
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Strobl, C., & Augustin, T. (2009). Adaptive selection of extra cutpoints – An approach towards reconciling robustness and interpretability in classification trees. Journal of Statistical Theory and Practice, 3(1), 119–135.
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Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychological Methods, 14(4), 323–348.
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Tutz, G., & Strobl, C. (2009). Generalisierte lineare Modelle. In H. Holling & B. Schmitz (Eds.), Handbuch der Psychologie, Band 13: Handbuch Statistik, Methoden und Evaluation (pp. 461–472). Hogrefe.
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Strobl, C., Dittrich, C., Seiler, C., Hackensperger, S., & Leisch, F. (2009). Measurement and predictors of a negative attitude towards statistics among LMU students. In T. Kneib & G. Tutz (Eds.), Statistical modelling and regression structures (Festschrift in honour of Ludwig Fahrmeir) (pp. 217–230). Springer.
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Boulesteix, A.-L., & Strobl, C. (2009). Optimal classifier selection and negative bias in error rate estimation: An empirical study on high-dimensional prediction. BMC Medical Research Methodology, 9(85), 1471–2288.
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Strobl, C., Hothorn, T., & Zeileis, A. (2009). Party on! A new, conditional variable importance measure for random forests available in the party package. The R Journal, 1(2), 14–17.
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Strobl, C., Weidinger, S., Baurecht, H., Wagenpfeil, S., Henderson, J., Novak, N., Sandilands, A., Chen, H., Rodriguez, E., O’Regan, G. M., Watson, R., Liao, H., Zhao, Y., Barker, J. N. W. N., Allen, M., Reynolds, N., Meggit, S., Northstone, K., & Smith, G. D. (2008). Analysis of the individual and aggregate genetic contributions of previously identified SPINK5 , KLK7 and FLG polymorphisms to eczema risk. The Journal of Allergy and Clinical Immunology, 122(3), 560–568.
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Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(307), 1471–2105.
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Strobl, C., & Zeileis, A. (2008). Danger: High power! – Exploring the statistical properties of a test for random forest variable importance. In P. Brito (Ed.), Proceedings of the 18th international conference on computational statistics, porto, portugal (CD-ROM) (pp. 59–66). Springer.
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Boulesteix, A.-L., Strobl, C., Augustin, T., & Daumer, M. (2008). Evaluating microarray-based classifiers: An overview. Cancer Informatics, 6, 77–97.
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Strobl, C. (2008). Statistical issues in machine learning – Towards reliable split selection and variable importance measures [Dissertation Thesis in Statistics]. Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
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Boulesteix, A.-L., & Strobl, C. (2007). Maximally selected chi-square statistics and non-monotonic associations: An exact approach based on two cutpoints. Computational Statistics & Data Analysis, 51(12), 6295–6306.
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Boulesteix, A.-L., Strobl, C., Weidinger, S., Wichmann, H.-E., & Wagenpfeil, S. (2007). Multiple testing for SNP-SNP interactions. Statistical Applications in Genetics and Molecular Biology, 6(1), 37.
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Strobl, C., Boulesteix, A.-L., & Augustin, T. (2007). Unbiased split selection for classification trees based on the Gini index. Computational Statistics & Data Analysis, 52(1), 483–501.
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Augustin, T., & Strobl, C. (2006). Interactive statistics for the behavioral sciences (book review). Biometrics, 62, 625–626.
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Strobl, C. (2005). Statistical sources of variable selection bias in classification trees based on the Gini index (Sfb386-Discussion Paper 420). Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
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Strobl, C. (2005). Variable selection in classification trees based on imprecise probabilities. In F. Cozman, R. Nau, & T. Seidenfeld (Eds.), Proceedings of the fourth international symposium on imprecise probabilities and their applications.
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