Resilienz stärken: Interventionsmöglichkeiten für Hochschulen zur Förderung der akademischen Resilienz ihrer Studierenden; Ein Leitfaden

Gerade in Zeiten von (persönlichen) Krisen benötigen Studierende eine ausreichende psychische Widerstandsfähigkeit, um mit Belastungen im Hochschulkontext umgehen zu können. Dies hat uns spätestens die COVID-19-Pandemie deutlich vor Augen geführt. Daher stellt sich mehr denn je die Frage, was Hochschulen tun können, um ihre Studierenden dabei zu unterstützen, mit Belastungserfahrungen konstruktiv umzugehen und ihr Studium erfolgreich zu Ende zu bringen. Der vorliegende Leitfaden fasst zentrale Erkenntnisse hierzu zusammen und richtet sich an alle Akteure aus der Hochschulpraxis, welche um die Sicherung des Studienerfolgs und der Studierendengesundheit bemüht sind

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

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

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

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