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Empirisch-quantitative Abschlussarbeiten – Ein Blick nach vorne

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Quantitative Forschung in Masterarbeiten

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Zusammenfassung

Empirische Abschlussarbeiten haben sich im Laufe der Zeit verändert. So haben sich Forschungsfragen gewandelt, aber auch die Möglichkeiten der Datennutzung und Datenanalyse werden in den letzten Jahren immer vielfältiger. Die Replikationskrise und die anhaltenden Fehlinterpretationen von statistischen Ergebnissen sind Herausforderungen, die auch Erstellerinnen und Ersteller von Abschlussarbeiten betreffen. Aktuell steht z. B. der p-Wert in der Kritik, die auch in Abschlussarbeiten Beachtung finden sollte. Neue Möglichkeiten hingegen ergeben sich beispielsweise unter den Schlagwörtern Big Data, Künstliche Intelligenz und Open Science. In diesem kurzen Kapitel wird ein kleiner Ausblick versucht, wie die Kritik und die Möglichkeiten im Zusammenhang mit Abschlussarbeiten aufgegriffen werden können. Insbesondere werden Hinweise auf vertiefende Literatur gegeben.

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Literatur

  • Allen, C., & Mehler, D. M. (2019). Open science challenges, benefits and tips in early career and beyond. PLoS biology, 17(5), e3000246.

    Article  Google Scholar 

  • Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567, 305–307.

    Article  Google Scholar 

  • Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2017). Modern data science with R. CRC Press.

    Google Scholar 

  • Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., & Horton, N. J. (2014). R Markdown: Integrating a reproducible analysis tool into introductory statistics. arXiv preprint arXiv:1402.1894.

    Google Scholar 

  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, 57(1), 289–300.

    Google Scholar 

  • Boßow-Thies, S. & Gansser, O. (2021): Grundlagen empirischer Forschung in quantitativen Masterarbeiten, in: Boßow-Thies, S., Krol, B. (Hrsg.), Quantitative Forschung in Masterarbeiten – Best-Practice-Beispiele wirtschaftswissenschaftlicher Studienrichtungen, Springer Gabler, Wiesbaden.

    Google Scholar 

  • Bojinov, I., Chen, A., & Liu, M. (2020). The Importance of Being Causal. Harvard Data Science Review, 2(3).

    Google Scholar 

  • Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In Handbook of causal analysis for social research, Dordrecht: Springer, 301–328.

    Chapter  Google Scholar 

  • Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199–231.

    Article  Google Scholar 

  • Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785–794. New York.

    Google Scholar 

  • Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766.

    Article  Google Scholar 

  • Donoho, D. L. (2000). High-dimensional data analysis: The curses and blessings of dimensionality. AMS math challenges lecture.

    Google Scholar 

  • Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655.

    Article  Google Scholar 

  • Efron, B., & Hastie, T. (2016). Computer age statistical inference (Vol. 5). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Gelman, A. (2018). Ethics in statistical practice and communication: Five recommendations. Significance, 15(5), 40–43.

    Article  Google Scholar 

  • Gelman, A., & Loken, E. (2014). The statistical crisis in science: data-dependent analysis – a „garden of forking paths“ – explains why many statistically significant comparisons don’t hold up. American scientist, 102(6), 460–466.

    Article  Google Scholar 

  • Gelman, A., & Vehtari, A. (2020). What are the most important statistical ideas of the past 50 years?. arXiv preprint arXiv:2012.00174.

    Google Scholar 

  • Greenland, S. (2020). The causal foundations of applied probability and statistics. arXiv preprint arXiv:2011.02677.

    Google Scholar 

  • Grosz, M. P., Rohrer, J. M., & Thoemmes, F. (2020). The taboo against explicit causal inference in nonexperimental psychology. Perspectives on Psychological Science, 15(5), 1243–1255.

    Article  Google Scholar 

  • Herbert, A., Griffith, G., Hemani, G., & Zuccolo, L. (2020). The spectre of Berkson’s paradox: Collider bias in Covid-19 research. Significance, 17(4), 6–7.

    Article  Google Scholar 

  • Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945–960.

    Article  Google Scholar 

  • Kaplan, R. M., Chamber, D. A., & Glasgow, R. E. (2014). Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias. Clinical and Translation Science, 7(4), 342–346.

    Article  Google Scholar 

  • Kohavi, R., & Longbotham, R. (2017). Online Controlled Experiments and A/B Testing. Encyclopedia of machine learning and data mining, 7(8), 922–929.

    Article  Google Scholar 

  • Lakens, D. (2019). The value of preregistration for psychological science: A conceptual analysis. Japanese Psychological Review, 62(3), 221–230.

    Google Scholar 

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

    Article  Google Scholar 

  • Lübke, K., Gehrke, M., Horst, J., & Szepannek, G. (2020). Why We Should Teach Causal Inference: Examples in Linear Regression with Simulated Data. Journal of Statistics Education, 28(2), 133–139.

    Article  Google Scholar 

  • Mayo, D. G. (2018). Statistical inference as severe testing. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press.

    Google Scholar 

  • Meng, X. L. (2018). Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. The Annals of Applied Statistics, 12(2), 685–726.

    Article  Google Scholar 

  • Munafò, M. R., Nosek, B. A., Bishop, D. V., Button, K. S., Chambers, C. D., Du Sert, N. P., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. (2017). A manifesto for reproducible science. Nature human behaviour, 1(1), 1–9.

    Article  Google Scholar 

  • Munzert, S., Rubba, C., Meißner, P., & Nyhuis, D. (2014). Automated data collection with R: A practical guide to web scraping and text mining. Chichester: John Wiley & Sons.

    Book  Google Scholar 

  • Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606.

    Article  Google Scholar 

  • Pearl, J. (2018). Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016.

    Google Scholar 

  • Pfannkuch, M., Ben-Zvi, D., & Budgett, S. (2018). Innovations in statistical modeling to connect data, chance and context. ZDM, 50(7), 1113–1123.

    Article  Google Scholar 

  • Ridgway, J. (2016). Implications of the data revolution for statistics education. International Statistical Review, 84(3), 528–549.

    Article  Google Scholar 

  • Riede, T., Tümmler, T., & Wondrak, S. (2018). Die Digitale Agenda des Statistischen Bundesamtes. Wirtsch Stat, 1, 102–111.

    Google Scholar 

  • Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215.

    Article  Google Scholar 

  • Samek, W., & Müller, K. R. (2019). Towards explainable artificial intelligence. In Explainable AI: interpreting, explaining and visualizing deep learning. Cham: Springer, 5–22.

    Chapter  Google Scholar 

  • Schüller, K., Busch, P., & Hindinger, C. (2019). Future Skills: Ein Framework für Data Literacy. Kompetenzrahmen und Forschungsbericht. Hochschulforum für Digitalisierung.

    Google Scholar 

  • Shmueli, G. (2010). To explain or to predict?. Statistical science, 25(3), 289–310.

    Article  Google Scholar 

  • Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. Sebastopol: O’Reilly Media, Inc.

    Google Scholar 

  • Stark, P. B., & Saltelli, A. (2018). Cargo-cult statistics and scientific crisis. Significance, 15(4), 40–43.

    Article  Google Scholar 

  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.

    Article  Google Scholar 

  • Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129–133.

    Article  Google Scholar 

  • Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a world beyond „p< 0.05“. The American Statistician, 73:sup1, 1–19.

    Google Scholar 

  • Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.

    Article  Google Scholar 

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Authors and Affiliations

  1. FOM Hochschule für Oekonomie & Management, Dortmund, Deutschland

    Karsten Lübke

  2. FOM Hochschule für Oekonomie & Management, Essen, Deutschland

    Bianca Krol

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  1. Karsten Lübke
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  2. Bianca Krol
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Correspondence to Karsten Lübke .

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Editors and Affiliations

  1. FOM Hochschule für Oekonomie & Management, Hamburg, Deutschland

    Silvia Boßow-Thies

  2. FOM Hochschule für Oekonomie & Management, Essen, Deutschland

    Bianca Krol

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Lübke, K., Krol, B. (2022). Empirisch-quantitative Abschlussarbeiten – Ein Blick nach vorne. In: Boßow-Thies, S., Krol, B. (eds) Quantitative Forschung in Masterarbeiten. FOM-Edition. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-35831-0_17

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  • DOI: https://doi.org/10.1007/978-3-658-35831-0_17

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