PsychAuthors

Literaturliste von Prof. Dr. Carolin Strobl

letzte Aktualisierung: 04.12.2023

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, 1-42.

Henninger, M., Debelak, R., Rothacher, Y. & Strobl, C. (2023). Interpretable machine learning for psychological research: Opportunities and pitfalls. Psychological Methods, 1-35.

Rothacher, Y. & Strobl, C. (2023). Identifying informative predictor variables with random forests. Journal of Educational and Behavioral Statistics.

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, 1-25.

Henninger, M., Debelak, R. & Strobl, C. (2022). A new stopping criterion for Rasch trees based on the Mantel-Haenszel effect size measure for differential item functioning. Educational and Psychological Measurement, 1-32.

Strobl, C. & Leisch, F. (2022). Against the "one method fits all data sets" philosophy for comparison studies in methodological research. Biometrical Journal, 1-8.

Schneider, L., Strobl, C., Zeileis, A. & Debelak, R. (2021). An R toolbox for score-based measurement invariance tests in IRT models. Behavior Research Methods, 54(5), 2101-2113.

Schweinsberg, M., Feldman, M., Staub, N., van den Akker, O. R., van Aert, R. C. M., van Assen, M. A. L. M., Liu, Y., Althoff, T., Heer, J., Kale, A., Mohamed, Z., Amireh, H., Prasad, V. V., Bernstein, A., Robinson, E., Snellman, K., Sommer, S. A., Otner, S. M. G., Robinson, D., Madan, N., Silberzahn, R., Goldstein, P., Tierney, W., Murase, T., Mandl, B., Viganola, D., Strobl, C., Schaumans, C. B. C., Kelchtermans, S., Naseeb, C., Garrison, S. M., Yarkoni, T., Chan, C. S. R., Adie, P., Alaburda, P., Albers, C., Alspaugh, S., Alstott, J., Nelson, A. A., de la Rubia, E. A., Arzi, A., Bahník, ^., Baik, J., Balling, L. W., Banker, S., Baranger, D. A.A., Barr, D. J., Barros-Rivera, B., Bauer, M., Blaise, E., Boelen, L., Carbonell, K. B., Briers, R. A., Burkhard, O., Canela, M.-A., Castrillo, L., Catlett, T., Chen, O., Clark, M., Cohn, B., Coppock, A., Cugueró-Escofet, N., Curran, P. G., Cyrus-Lai, W., Dai, D., Dalla Riva, G. V., Danielsson, H., Russo, R. d. F. S. M., de Silva, N., Derungs, C., Dondelinger, F., de Souza, C. D., Dube, B. T., Dubova, M., Dunn, B. M., Edelsbrunner, P. A., Finley, S., Fox, N., Gnambs, T., Gong, Y., Grand, E., Greenawalt, B., Han, D., Hanel, P. H. P., Hong, A. B., Hood, D., Hsueh, J., Huang, L., Hui, K. N., Hultman, K. A., Javaid, A., Jiang, L. J., Jong, J., Kamdar, J., Kane, D., Kappler, G., Kaszubowski, E., Kavanagh, C. M., Khabsa, M., Kleinberg, B., Kouros, J., Krause, H., Krypotos, A.-M., Lavbi^D%c, D., Lee, R. L., Leffel, T., Lim, W. Y., Liverani, S., Loh, B., Lønsmann, D., Low, J. W., Lu, A., MacDonald, K., Madan, C. R., Madsen, L. H., Maimone, C., Mangold, A., Marshall, A., Matskewich, H. E., Mavon, K., McLain, K. L., McNamara, A. A., McNeill, M., Mertens, U., Miller, D., Moore, B., Moore, A., Nantz, E., Nasrullah, Z., Nejkovic, V., Nell, C. S., Nelson, A. A., Nilsonne, G., Nolan, R., O'Brien, C. E., O'Neil, P., O'Shea, K., Olita, T., Otterbacher, J., Palsetia, D., Pereira, B., Pozdniakov, I., Protzko, J., Reyt, J.-N., Riddle, T., Ali, A., Ropovik, I., Rosenberg, J. M., Rothen, S., Schulte-Mecklenbeck, M., Sharma, N., Shotwell, G., Skarzynski, M., Stedden, W., Stodden, V., Stoffel, M. A., Stoltzman, S., Subbaiah, S., Tatman, R., Thibodeau, P. H., Tomkins, S., Valdivia, A., van de Woestijne, G. B., Viana, L., Villesèche, F., Wadsworth, W. D., Wanders, F., Watts, K., Wells, J. D., Whelpley, C. E., Won, A., Wu, L., Yip, A., Youngflesh, C., Yu, J.-C., Zandian, A., Zhang, L., Zibman, C. & Uhlmann, 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, 165, 228-249.

Strobl, C., Kopf, J., Kohler, L., Oertzen, T. v. & Zeileis, A. (2021). Anchor point selection: Scale alignment based on an inequality criterion. Applied Psychological Measurement, 45(3), 214-230.

Debeer, D. & Strobl, C. (2020). Conditional permutation importance revisited. BMC Bioinformatics, 21(1), No. 307.

Fokkema, M. & Strobl, C. (2020). Fitting prediction rule ensembles to psychological research data: An introduction and tutorial. Psychological Methods, 25(5), 636-652.

Huelmann, T., Debelak, R. & Strobl, C. (2020). A comparison of aggregation rules for selecting anchor items in multigroup DIF analysis. Journal of Educational Measurement, 57(2), 185-215.

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.

Komboz, B., Strobl, C. & Zeileis, A. (2018). Tree-based global model tests for polytomous rasch models. Educational and Psychological Measurement, 78(1), 128-166.

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(4), 325-365.

Meiser, T., Eid, M., Carstensen, C., Erdfelder, E., Gollwitzer, M., Pohl, S., Steyer, R. & Strobl, C. (2018). Stellungnahme zum Diskussionsforum. Psychologische Rundschau, 69(4), 362-365.

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.

Philipp, M., Strobl, C., de la Torre, J. & Zeileis, A. (2018). On the estimation of standard errors in cognitive diagnosis models. Journal of Educational and Behavioral Statistics, 43(1), 88-115.

Wang, T., Strobl, C., Zeileis, A. & Merkle, E. C. (2018). Score-based tests of differential item functioning via pairwise maximum likelihood estimation. Psychometrika, 83(1), 132-155.

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.

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.

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.

Strobl, C. (2015). Das Rasch-Modell. Eine verständliche Einführung für Studium und Praxis (3., erw. Aufl.). Mering: Rainer Hampp Verlag.

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, 986-1000.

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.

Strobl, C. (2014). Discussion to Wei-Yin Lohs "Fifty years of classification and regression trees". International Statistical Review, 82(3), 349-352.

Janitza, S., Strobl, C. & Boulesteix, A.-L. (2013). An AUC-based permutation variable importance measure for random forests. BMC Bioinformatics (Online Journal), No. 119.

Kopf, J., Augustin, T. & Strobl, C. (2013). The potential of model-based recursive partitioning in the social sciences. Revisiting Ockham's razor. In J. McArdle & G. Ritschard (Eds.), Contemporary issues in exploratory data mining in the behavioral sciences (pp. 75-95). New York: Routledge.

Sauer, S., Strobl, C., Walach, H. & Kohls, N. (2013). Rasch-Analyse des Freiburger Fragebogens zur Achtsamkeit. Diagnostica, 59(2), 86-99.

Strobl, C. (2013). Data mining. In T. Little (Ed.), The Oxford Handbook on Quantitative Methods, Vol. II (pp. 678-700). New York: Oxford University Press.

Strobl, C., Kopf, J. & Zeileis, A. (2013). Rasch trees: A new method for detecting differential item functioning in the Rasch model. Psychometrika, 1-28.

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.

Frick, H., Strobl, C., Leisch, F. & Zeileis, A. (2012). Flexible Rasch mixture models with package psychomix. Journal of Statistical Software, 48(7), 1-25.

Wickelmaier, F., Strobl, C. & Zeileis, A. (2012). Psychoco: Psychometric computing in R. Journal of Statistical Software, 48(1), 1-5.

Frick, H., Strobl, C., Leisch, F. & Zeileis, A. (2011). psychomix: Psychometric Mixture Models [R package].

Strobl, C. (2011). Contributions to Psychometric Computing and Machine Learning. Habilitation thesis, Department of Statistics, Ludwig-Maximilians-Universität München.

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.

Zeileis, A., Strobl, C. & Wickelmaier, F. (2011). psychotools: Infrastructure for Psychometric Modeling [R package].

Zeileis, A., Strobl, C., Wickelmaier, F. & Kopf, J. (2011). psychotree: Recursive Partitioning Based on Psychometric Models [R package].

Nicodemus, K., Malley, J., Strobl, C. & Ziegler, A. (2010). The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics, 11(110), 1471-2105.

Strobl, C. (2010). Advances in social science research using R [book review]. Journal of Statistical Software, 34(2), 1-2.

Strobl, C. (2010). Das Rasch-Modell. Eine verständliche Einführung für Studium und Praxis. München: Hampp.

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 (Hrsg.), Allgemeinbildung in Deutschland. Erkenntnisse aus dem SPIEGEL-Studentenpisa-Test (S. 255-272). Wiesbaden: VS Verlag für Sozialwissenschaften.

Tutz, G. & Strobl, C. (2010). Generalisierte lineare Modelle. In H. Holling & B. Schmitz (Hrsg.), Handbuch Statistik, Methoden und Evaluation (S. 461-471). Göttingen: Hogrefe.

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.

Hothorn, T., Hornik, K., Strobl, C. & Zeileis, A. (2009). party: A Laboratory for Recursive Part(y)itioning [R package].

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.

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 (pp. 217-230). Heidelberg: Springer.

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.

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.

Boulesteix, A.-L., Strobl, C., Augustin, T. & Daumer, M. (2008). Evaluating microarray-based classifiers: An overview. Cancer Informatics, 6, 77-97.

Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. (2008). Conditional variable importance for random forests. BMC Bioinformatics, 9(307), 1471-2105.

Strobl, C., Weidinger, S., Baurecht, H., Wagenpfeil, S., Henderson, J., Novak, N., Sandilands, A., Chen, H., Rodriguez, E., O'Regan, G., Watson, R., Liao, H., Zhao, Y., Barker, J., Allen, M., Reynolds, N., Meggit, S., Northstone, K. & Smith, G. (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.

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 (pp. 59-66). Heidelberg: Springer.

Strobl, C. (2008). Statistical issues in machine learning - Towards reliable split selection and variable importance measures. Dissertation, Universität, Fakultät für Mathematik, Informatik und Statistik, München.

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.

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.

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.

Strobl, C., Boulesteix, A.-L., Zeileis, A. & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(25), 1471-2105.

Augustin, T. & Strobl, C. (2006). Interactive statistics for the behavioral sciences [book review]. Biometrics, 62, 625-626.

Strobl, C. (2005). Variable selection in classification trees based on imprecise probabilities. In T. S. F. Cozman, R. Nau (Ed.), Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications, Pittsburgh.

Strobl, C. & Reimer, R. (2004). Akzeptanz von Studiengebühren für das Erststudium. Bericht der Studierendenvertretung, LMU München.

weitere Schriften:

Strobl, C., Kopf, J. & Zeileis, A. (2011). Using the raschtree function for detecting differential item functioning in the Rasch model. R package vignette.

Strobl, C. (2005). Statistical sources of variable selection bias in classification trees based on the Gini index (SFB386-Discussion Paper 420). LMU München: Department of Statistics.

Strobl, C. (2004). Variable Selection Bias in Classification Trees. Unpublished Master's thesis. Department of Statistics, Ludwig-Maximilians-Universität München.

Strobl, C. (2002). Experimental Study on the Relationship between Perceived Binocular Direction and Distance. Unpublished diploma thesis. Department of Psychology, Universität Regensburg.

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