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128 results on '"Stefan, Angelika"'

3. Bayes Factors for Mixed Models: a Discussion

Author

van Doorn, Johnny, Haaf, Julia M., Stefan, Angelika M., Wagenmakers, Eric-Jan, Cox, Gregory Edward, Davis-Stober, Clintin P., Heathcote, Andrew, Heck, Daniel W., Kalish, Michael, Kellen, David, Matzke, Dora, Morey, Richard D., Nicenboim, Bruno, van Ravenzwaaij, Don, Rouder, Jeffrey N., Schad, Daniel J., Shiffrin, Richard M., Singmann, Henrik, Vasishth, Shravan, Veríssimo, João, Bockting, Florence, Chandramouli, Suyog, Dunn, John C., Gronau, Quentin F., Linde, Maximilian, McMullin, Sara D., Navarro, Danielle, Schnuerch, Martin, Yadav, Himanshu, and Aust, Frederik

Published
2023
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7. The JASP guidelines for conducting and reporting a Bayesian analysis

Author

van Doorn, Johnny, van den Bergh, Don, Böhm, Udo, Dablander, Fabian, Derks, Koen, Draws, Tim, Etz, Alexander, Evans, Nathan J., Gronau, Quentin F., Haaf, Julia M., Hinne, Max, Kucharský, Šimon, Ly, Alexander, Marsman, Maarten, Matzke, Dora, Gupta, Akash R. Komarlu Narendra, Sarafoglou, Alexandra, Stefan, Angelika, Voelkel, Jan G., and Wagenmakers, Eric-Jan

Published
2021
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8. Alternative Models of Research Funding

Author

Gigerenzer, Gerd, Allen, Colin, Gaillard, Stefan D.M., Goldstone, Robert L., Haaf, Julia, Holmes, William R., Kashima, Yoshihisa, Motz, Benjamin, Musslick, Sebastian, Stefan, Angelika, Gigerenzer, Gerd, Allen, Colin, Gaillard, Stefan D.M., Goldstone, Robert L., Haaf, Julia, Holmes, William R., Kashima, Yoshihisa, Motz, Benjamin, Musslick, Sebastian, and Stefan, Angelika

Abstract

Funding of basic, curiosity-driven science is the lifeblood of scientific and technological innovation. Various models of funding allocation became institutionalized in the 20th century, shaping the present landscape of research funding. There are numerous reasons for scientists to be dissatisfied with current funding schemes, including the imbalance between funding for basic research and mission-directed funding, regional and country disparities, gender disparities, low inter-reviewer reliability, path-dependency of who gets funded, and the trade-off between the effort and time spent on writing or reviewing proposals and doing research. We discuss possible alternative models for dealing with these issues, such as distributed or concentrated funding, placing more weight on the proposals or on the investigators, representative composition of panel members, and retrospective versus prospective awards. We also consider more radical alternatives to current funding schemes, such as partial lotteries in response to low inter-reviewer reliability and the introduction of international competitive applications to a World Research Council alongside national funding sources. There is likely no single best way to fund basic, curiosity-driven research; we examine arguments for and against the possibility of systematically evaluating alternative models empirically.

Published
2024

16. Bayesian sample size planning for developmental studies.

Author

Visser, Ingmar, Kucharský, Šimon, Levelt, Claartje, Stefan, Angelika M., Wagenmakers, Eric‐Jan, and Oakes, Lisa

Subjects
EXPERIMENTAL design, SAMPLE size (Statistics), ACQUISITION of data, LEARNING strategies, STATISTICAL models
Abstract

Running developmental experiments, particularly with infants, is often time‐consuming and intensive, and the recruitment of participants is hard and expensive. Thus, an important goal for developmental researchers is to optimize sampling plans such that neither too many nor too few participants are tested given the hypothesis of interest. One approach that enables such optimization is the use of Bayesian sequential designs. The use of such sequential designs allows data collection to be terminated as soon as the evidence is deemed sufficiently strong, without compromising the interpretability of the test outcome. In this tutorial, we illustrate how to plan a Bayesian sequential testing design prior to data collection by the method of Bayes factor design analysis—the Bayesian equivalent of power analysis—and discuss the relevance of this for developmental psychologists. The tutorial provides a step‐by‐step guide to perform such analyses, and the methods are illustrated using commonly used statistics in a typical infant‐looking time paradigm such that researchers can easily adapt these methods for their studies. Highlights: Bayesian Sequential Testing can be used to optimize sample sizes and save on data collection.Bayes Factor Design Analysis can be used to analyze a sequential testing study prior to data collection.Step‐by‐step guide for performing Bayes Sequential Testing and Bayes Factor Design Analysis. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Netzwerk der Open-Science-Initiativen (NOSI)

Author

Schönbrodt, Felix, Baumert, Anna, Glöckner, Andreas, Back, Mitja, Arslan, Ruben, Voracek, Martin, Horstmann, Kai, Rohrer, Julia, Mueller, Erik, Stahl, Christoph, Fiebach, Christian, Miller, Robert, Schultheiss, Oliver, Brohmer, Hilmar, Renkewitz, Frank, Janssen, Lieneke, Stefan, Angelika, Heycke, Tobias, Schmidt, Nadya-Daniela, Röseler, Lukas, Rademacher, Lena, Kauff, Mathias, Schoedel, Ramona, Schiestel, Lena, Sust, Larissa, Tomczyk, Samuel, Schneider, Jürgen, Baum, Myriam, Hart, Alexander, Wennerhold, Lasse, Friese, Malte, Schmitt, Tina, Höffler, Jan, Bellhäuser, Henrik, Weber, Marcel, Neuendorf, Claudia, Röer, Jan, Reer, Aaron, Jacobsen, Nadine, Schmidt, Birgit, Stern, Julia, Zapko-Willmes, Alexandra, Meissner, Sibylle, Rausch, Manuel, Germar, Markus, Berkessel, Jana, Schneider, Sabrina, and Riedl, Lydia

Subjects
open science committee
Abstract

This project is the central platform for the Network of (German-speaking) Open Science Initiatives (Netzwerk der Open-Science-Initiativen, NOSI). We're providing protocols, links, and resources that might be of interest to others interested in Open Science.

Published
2022
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27. Bayes Factors for Mixed Models: A Discussion

Author

van Doorn, Johnny, primary, Haaf, Julia M., additional, Stefan, Angelika Marlene, additional, Wagenmakers, Eric-Jan, additional, Cox, Gregory Edward, additional, Davis-Stober, Clintin P., additional, Heathcote, Andrew, additional, Heck, Daniel W., additional, Kalish, Michael, additional, Kellen, David, additional, Matzke, Dora, additional, Morey, Richard Donald, additional, Nicenboim, Bruno, additional, van Ravenzwaaij, Don, additional, Rouder, Jeffrey, additional, Schad, Daniel, additional, Shiffrin, Rich, additional, Singmann, Henrik, additional, Vasishth, Shravan, additional, Veríssimo, João, additional, Bockting, Florence, additional, Chandramouli, Suyog, additional, Dunn, John C, additional, Gronau, Quentin Frederik, additional, Linde, Maximilian, additional, McMullin, Sara D, additional, Navarro, Danielle, additional, Schnuerch, Martin, additional, Yadav, Himanshu, additional, and Aust, Frederik, additional

Published
2022
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37. Procedure

Author

Protzko, John, Lakens, Daniel, Forscher, Patrick, and Stefan, Angelika

Published
2022
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38. Corona couple ESM study

Author

Zygar-Hoffmann, Caroline, Cludius, Barbara, Werner, Gabriela, Stefan, Angelika, Sckopke, Philipp, and Schönbrodt, Felix

Subjects
Other Psychology, FOS: Psychology, Clinical Psychology, Personality and Social Contexts, Medicine and Health Sciences, Experience Sampling, Psychology, Psychiatry and Psychology, Motivational Psychology, Social and Behavioral Sciences
Abstract

Experience sampling study with couples (3 pings a day for 4 weeks), with pre-ESM-questionnaire and 6 monthly followups, during the Corona pandemic 2020. Please see the "files" section -> "Archive of OSF Storage" for the uploaded preregistrations (https://osf.io/9h2c8/files/).

Published
2022
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41. A Prior Distribution for Bayesian Meta Analyses

Author

McKinney, Kaitlan, Gronau, Quentin, and Stefan, Angelika

Subjects
Meta-analysis, Social Psychology, Prior Distribution, Bayesian
Abstract

We create a data-driven prior distribution for meta-analyses in social psychology. We also outline a method that researchers can use to develop priors for their own field.

Published
2022
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42. Data and Scripts

Author

Buchanan, Erin, Coles, Nicholas, Ebersole, Charles, Forscher, Patrick, Moshontz, Hannah, Miller, Jeremy, Szabelska, Anna, Wang, Ke, Vasilev, Martin, Ropovik, Ivan, Kaminski, Gwenaël, Isager, Peder, Holcombe, Alex, Stefan, Angelika, D'Urso, E. Damiano, Nilsonne, Gustav, Pit, Ilse, Beitner, Julia, DeBruine, Lisa, Searston, Rachel, Dorison, Charles, Legate, Nicole, Nguyen, Thuy-vy, Goldenberg, Amit, Lewis, Savannah, Paris, Bastien, and Pavlacic, Jeffrey

Published
2022
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43. Bayesian Hierarchical Modeling: An Introduction and Reassessment

Author

Veenman, Myrthe, Stefan, Angelika, and Haaf, Julia

Abstract

With the recent development of easy-to-use tools for Bayesian analysis, psychologists have started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide an intuitive account of inter- and intraindividual variability and are particularly suited for the evaluation of repeated-measures designs. Here, we provide guidance for model specificaton and interpretation in Bayesian hierarchical modeling and describe common pitfalls that can arise in the process of model fitting and evaluation. Our introduction gives particular emphasis to prior specification and prior sensitivity, as well as to the calculation of Bayes factors for model comparisons. We illustrate the use of state-of-the-art software programs Stan and brms. The result is an overview over best practices in Bayesian hierarchical modeling that, as we hope, will help psychologists in making the best use of Bayesian hierarchical modeling.

Published
2022

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