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2022 | OriginalPaper | Chapter

17. Empirisch-quantitative Abschlussarbeiten – Ein Blick nach vorne

Authors : Karsten Lübke, Bianca Krol

Published in: Quantitative Forschung in Masterarbeiten

Publisher: Springer Fachmedien Wiesbaden

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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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Literature
go back to reference Allen, C., & Mehler, D. M. (2019). Open science challenges, benefits and tips in early career and beyond. PLoS biology, 17(5), e3000246.CrossRef Allen, C., & Mehler, D. M. (2019). Open science challenges, benefits and tips in early career and beyond. PLoS biology, 17(5), e3000246.CrossRef
go back to reference Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567, 305–307.CrossRef Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567, 305–307.CrossRef
go back to reference Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2017). Modern data science with R. CRC Press. Baumer, B. S., Kaplan, D. T., & Horton, N. J. (2017). Modern data science with R. CRC Press.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Bojinov, I., Chen, A., & Liu, M. (2020). The Importance of Being Causal. Harvard Data Science Review, 2(3). Bojinov, I., Chen, A., & Liu, M. (2020). The Importance of Being Causal. Harvard Data Science Review, 2(3).
go back to reference 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.CrossRef 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.CrossRef
go back to reference Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199–231.CrossRef Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical science, 16(3), 199–231.CrossRef
go back to reference 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. 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.
go back to reference Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766.CrossRef Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766.CrossRef
go back to reference Donoho, D. L. (2000). High-dimensional data analysis: The curses and blessings of dimensionality. AMS math challenges lecture. Donoho, D. L. (2000). High-dimensional data analysis: The curses and blessings of dimensionality. AMS math challenges lecture.
go back to reference Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655.CrossRef Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655.CrossRef
go back to reference Efron, B., & Hastie, T. (2016). Computer age statistical inference (Vol. 5). Cambridge: Cambridge University Press.CrossRef Efron, B., & Hastie, T. (2016). Computer age statistical inference (Vol. 5). Cambridge: Cambridge University Press.CrossRef
go back to reference Gelman, A. (2018). Ethics in statistical practice and communication: Five recommendations. Significance, 15(5), 40–43.CrossRef Gelman, A. (2018). Ethics in statistical practice and communication: Five recommendations. Significance, 15(5), 40–43.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Gelman, A., & Vehtari, A. (2020). What are the most important statistical ideas of the past 50 years?. arXiv preprint arXiv:2012.00174. Gelman, A., & Vehtari, A. (2020). What are the most important statistical ideas of the past 50 years?. arXiv preprint arXiv:2012.00174.
go back to reference Greenland, S. (2020). The causal foundations of applied probability and statistics. arXiv preprint arXiv:2011.02677. Greenland, S. (2020). The causal foundations of applied probability and statistics. arXiv preprint arXiv:2011.02677.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945–960.CrossRef Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945–960.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Kohavi, R., & Longbotham, R. (2017). Online Controlled Experiments and A/B Testing. Encyclopedia of machine learning and data mining, 7(8), 922–929.CrossRef Kohavi, R., & Longbotham, R. (2017). Online Controlled Experiments and A/B Testing. Encyclopedia of machine learning and data mining, 7(8), 922–929.CrossRef
go back to reference Lakens, D. (2019). The value of preregistration for psychological science: A conceptual analysis. Japanese Psychological Review, 62(3), 221–230. Lakens, D. (2019). The value of preregistration for psychological science: A conceptual analysis. Japanese Psychological Review, 62(3), 221–230.
go back to reference LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRef LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Mayo, D. G. (2018). Statistical inference as severe testing. Cambridge: Cambridge University Press.CrossRef Mayo, D. G. (2018). Statistical inference as severe testing. Cambridge: Cambridge University Press.CrossRef
go back to reference McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press. McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC Press.
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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.CrossRef 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.CrossRef
go back to reference Pearl, J. (2018). Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016. Pearl, J. (2018). Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016.
go back to reference Pfannkuch, M., Ben-Zvi, D., & Budgett, S. (2018). Innovations in statistical modeling to connect data, chance and context. ZDM, 50(7), 1113–1123.CrossRef Pfannkuch, M., Ben-Zvi, D., & Budgett, S. (2018). Innovations in statistical modeling to connect data, chance and context. ZDM, 50(7), 1113–1123.CrossRef
go back to reference Ridgway, J. (2016). Implications of the data revolution for statistics education. International Statistical Review, 84(3), 528–549.CrossRef Ridgway, J. (2016). Implications of the data revolution for statistics education. International Statistical Review, 84(3), 528–549.CrossRef
go back to reference Riede, T., Tümmler, T., & Wondrak, S. (2018). Die Digitale Agenda des Statistischen Bundesamtes. Wirtsch Stat, 1, 102–111. Riede, T., Tümmler, T., & Wondrak, S. (2018). Die Digitale Agenda des Statistischen Bundesamtes. Wirtsch Stat, 1, 102–111.
go back to reference 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.CrossRef 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.CrossRef
go back to reference Samek, W., & Müller, K. R. (2019). Towards explainable artificial intelligence. In Explainable AI: interpreting, explaining and visualizing deep learning. Cham: Springer, 5–22.CrossRef Samek, W., & Müller, K. R. (2019). Towards explainable artificial intelligence. In Explainable AI: interpreting, explaining and visualizing deep learning. Cham: Springer, 5–22.CrossRef
go back to reference Schüller, K., Busch, P., & Hindinger, C. (2019). Future Skills: Ein Framework für Data Literacy. Kompetenzrahmen und Forschungsbericht. Hochschulforum für Digitalisierung. Schüller, K., Busch, P., & Hindinger, C. (2019). Future Skills: Ein Framework für Data Literacy. Kompetenzrahmen und Forschungsbericht. Hochschulforum für Digitalisierung.
go back to reference Shmueli, G. (2010). To explain or to predict?. Statistical science, 25(3), 289–310.CrossRef Shmueli, G. (2010). To explain or to predict?. Statistical science, 25(3), 289–310.CrossRef
go back to reference Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. Sebastopol: O’Reilly Media, Inc. Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. Sebastopol: O’Reilly Media, Inc.
go back to reference Stark, P. B., & Saltelli, A. (2018). Cargo-cult statistics and scientific crisis. Significance, 15(4), 40–43.CrossRef Stark, P. B., & Saltelli, A. (2018). Cargo-cult statistics and scientific crisis. Significance, 15(4), 40–43.CrossRef
go back to reference Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.CrossRef Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.CrossRef
go back to reference Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129–133.CrossRef Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129–133.CrossRef
go back to reference 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. 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.
go back to reference Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.CrossRef Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–248.CrossRef
Metadata
Title
Empirisch-quantitative Abschlussarbeiten – Ein Blick nach vorne
Authors
Karsten Lübke
Bianca Krol
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-658-35831-0_17

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