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8 results on '"Schiestel, Lena"'

1. 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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4. Measuring Implicit Motives with the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms

Author

Schönbrodt, Felix D; https://orcid.org/0000-0002-8282-3910, Hagemeyer, Birk; https://orcid.org/0000-0003-4833-1370, Brandstätter, Veronika; https://orcid.org/0000-0002-6824-1754, Czikmantori, Thomas; https://orcid.org/0000-0003-2753-1701, Gröpel, Peter; https://orcid.org/0000-0001-8765-176X, Hennecke, Marie; https://orcid.org/0000-0002-0263-4598, Israel, Laura S F, Janson, Kevin T; https://orcid.org/0000-0001-5109-9360, Kemper, Nina, Köllner, Martin G; https://orcid.org/0000-0002-5102-805X, Kopp, Philipp M; https://orcid.org/0000-0002-3589-5217, Mojzisch, Andreas, Müller-Hotop, Raphael, Prüfer, Johanna, Quirin, Markus, Scheidemann, Bettina, Schiestel, Lena, Schulz-Hardt, Stefan, Sust, Larissa N N, Zygar-Hoffmann, Caroline; https://orcid.org/0000-0002-8677-2276, Schultheiss, Oliver C; https://orcid.org/0000-0002-7892-5228, Schönbrodt, Felix D; https://orcid.org/0000-0002-8282-3910, Hagemeyer, Birk; https://orcid.org/0000-0003-4833-1370, Brandstätter, Veronika; https://orcid.org/0000-0002-6824-1754, Czikmantori, Thomas; https://orcid.org/0000-0003-2753-1701, Gröpel, Peter; https://orcid.org/0000-0001-8765-176X, Hennecke, Marie; https://orcid.org/0000-0002-0263-4598, Israel, Laura S F, Janson, Kevin T; https://orcid.org/0000-0001-5109-9360, Kemper, Nina, Köllner, Martin G; https://orcid.org/0000-0002-5102-805X, Kopp, Philipp M; https://orcid.org/0000-0002-3589-5217, Mojzisch, Andreas, Müller-Hotop, Raphael, Prüfer, Johanna, Quirin, Markus, Scheidemann, Bettina, Schiestel, Lena, Schulz-Hardt, Stefan, Sust, Larissa N N, Zygar-Hoffmann, Caroline; https://orcid.org/0000-0002-8677-2276, and Schultheiss, Oliver C; https://orcid.org/0000-0002-7892-5228

Abstract

We present two openly accessible databases related to the assessment of implicit motives using Picture Story Exercises (PSEs): (a) A database of 183,415 German sentences, nested in 26,389 stories provided by 4,570 participants, which have been coded by experts using Winter's coding system for the implicit affiliation/intimacy, achievement, and power motives, and (b) a database of 54 classic and new pictures which have been used as PSE stimuli. Updated picture norms are provided which can be used to select appropriate pictures for PSE applications. Based on an analysis of the relations between raw motive scores, word count, and sentence count, we give recommendations on how to control motive scores for story length, and validate the recommendation with a meta-analysis on gender differences in the implicit affiliation motive that replicates existing findings. We discuss to what extent the guiding principles of the story length correction can be generalized to other content coding systems for narrative material. Several potential applications of the databases are discussed, including (un)supervised machine learning of text content, psychometrics, and better reproducibility of PSE research.

Published
2021

5. Measuring Implicit Motives With the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms

Author

Schönbrodt, Felix D., primary, Hagemeyer, Birk, additional, Brandstaetter-Morawietz, Veronika, additional, Czikmantori, Thomas, additional, Gröpel, Peter, additional, Hennecke, Marie, additional, Israel, Laura, additional, Janson, Kevin Thomas, additional, Kemper, Nina, additional, Köllner, Martin G., additional, Kopp, Philipp, additional, Mojzisch, Andreas, additional, Müller-Hotop, Raphael, additional, Prüfer, Johanna, additional, Quirin, Markus, additional, Scheidemann, Bettina, additional, Schiestel, Lena, additional, Schulz-Hardt, Stefan, additional, Sust, Larissa, additional, Zygar-Hoffmann, Caroline, additional, and Schultheiss, Oliver C., additional

Published
2021
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6. Measuring Implicit Motives with the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms

Author

Schönbrodt, Felix D., primary, Hagemeyer, Birk, additional, Brandstätter, Veronika, additional, Czikmantori, Thomas, additional, Gröpel, Peter, additional, Hennecke, Marie, additional, Israel, Laura S. F., additional, Janson, Kevin T., additional, Kemper, Nina, additional, Köllner, Martin G., additional, Kopp, Philipp M., additional, Mojzisch, Andreas, additional, Müller-Hotop, Raphael, additional, Prüfer, Johanna, additional, Quirin, Markus, additional, Scheidemann, Bettina, additional, Schiestel, Lena, additional, Schulz-Hardt, Stefan, additional, Sust, Larissa N. N., additional, Zygar-Hoffmann, Caroline, additional, and Schultheiss, Oliver C., additional

Published
2020
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7. Measuring Implicit Motives with the Picture Story Exercise (PSE): Databases of Expert-Coded German Stories, Pictures, and Updated Picture Norms.

Author

Schönbrodt, Felix D., Hagemeyer, Birk, Brandstätter, Veronika, Czikmantori, Thomas, Gröpel, Peter, Hennecke, Marie, Israel, Laura S. F., Janson, Kevin T., Kemper, Nina, Köllner, Martin G., Kopp, Philipp M., Mojzisch, Andreas, Müller-Hotop, Raphael, Prüfer, Johanna, Quirin, Markus, Scheidemann, Bettina, Schiestel, Lena, Schulz-Hardt, Stefan, Sust, Larissa N. N., and Zygar-Hoffmann, Caroline

Subjects
SUPERVISED learning, WORD frequency, DATABASES, TALLIES, PICTURES
Abstract

We present two openly accessible databases related to the assessment of implicit motives using Picture Story Exercises (PSEs): (a) A database of 183,415 German sentences, nested in 26,389 stories provided by 4,570 participants, which have been coded by experts using Winter's coding system for the implicit affiliation/intimacy, achievement, and power motives, and (b) a database of 54 classic and new pictures which have been used as PSE stimuli. Updated picture norms are provided which can be used to select appropriate pictures for PSE applications. Based on an analysis of the relations between raw motive scores, word count, and sentence count, we give recommendations on how to control motive scores for story length, and validate the recommendation with a meta-analysis on gender differences in the implicit affiliation motive that replicates existing findings. We discuss to what extent the guiding principles of the story length correction can be generalized to other content coding systems for narrative material. Several potential applications of the databases are discussed, including (un)supervised machine learning of text content, psychometrics, and better reproducibility of PSE research. [ABSTRACT FROM AUTHOR]

Published
2021
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