Lehrstuhl für Psychologische Methodenlehre und Diagnostik

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Prof. Dr. Felix Schönbrodt

Prof. Dr. Felix Schönbrodt

Principal Investigator (Akademischer Rat, apl. Prof)


Ludwig-Maximilians-Universität München
Department Psychologie
Psychologische Methodenlehre und Diagnostik
Leopoldstraße 13
80802 München

Raum: 3327
Telefon: +49 (0) 89 / 2180 - 5050

Website: Personal Homepage / Blog
Website: Interactive Statistical Tool (ShinyApps.org)
Website: Commitment to Research Transparency
Website: Managing director of the LMU Open Science Center

Während der Coronapandemie findet *in der Vorlesungszeit* eine offene Sprechstunde per Zoom statt. Sie brauchen sich nicht anmelden, Sie können einfach "anklingeln" (und landen dann erstmal im Warteraum); falls schon jemand in der Sprechstunde ist, müssten Sie im Warteraum warten bis ich Sie reinlasse.

Wann: Dienstags, 8:00 bis 9:00
Wo: https://lmu-munich.zoom.us/j/94916680552?pwd=NDFXTFpoRTJySWMvckJ2eHl0OStodz09

Meeting-ID: 949 1668 0552
Kenncode: 096808

Sie können auch einen gesonderten Termin per Email anfragen.


In der *vorlesungsfreien Zeit* bitte generell per Email einen Termin ausmachen. Am 27.7. findet eine Sprechstunde von 9:00 bis 10:00 statt.

Bei Fragen zur Anerkennung von Studienleistungen gehen Sie bitte auf diese Webseite.

Open Science and my commitment to research transparency

I embrace the values of openness and transparency in science. I believe that such research practices increase the informational value and impact of our research, as the data can be reanalyzed and synthesized in future studies. Furthermore, they increase the credibility of the results, as an independent verification and replication is possible.

For this reason, I developed and signed the Commitment to Research Transparency:

Service to the field

Software packages

  • fSRM (R package) (CRAN, Github): Social relations models with roles
  • RSA (R package) (CRAN, Github): Response surface analyses
  • BFDA (R package) (Github): Bayes factor design analysis

Grants (PI)

Psychometrie impliziter Motive. Grant from German Research Foundation (DFG SCHO1334/1-1; 35,587 €; together with Birk Hagemeyer, Uni Jena).

Die Messung impliziter (nicht-bewusster) Motive mithilfe des Thematischen Apperzeptionstests und verwandter Verfahren ("Picture Story Exercises"; PSE) hat eine lange Tradition in der klinischen Praxis und in der psychologischen Grundlagenforschung. Gleichzeitig hat das Interesse an impliziten Motiven in den letzten 15 Jahren, unter anderem auch inspiriert durch die strikte Unterscheidung zwischen impliziten und expliziten Dispositionen, stark zugenommen. Die psychometrische Qualität der PSE-Methodik wurde häufig kritisiert, obwohl ihre Validität vielfach und eindrucksvoll belegt ist. In Reaktion auf diese Methodenkritik wurde wiederholt argumentiert, dass die Klassische Testtheorie keine geeignete Rahmentheorie für PSE- Messungen ist. Allerdings existiert bis heute kein Konsens darüber, wie eine alternative Messtheorie aussehen sollte. Das beantragte wissenschaftliche Netzwerk soll Expert/innen der Motivationspsychologie mit Expert/innen für statistische Methoden und Psychometrie vernetzen, um gemeinsam eine Messtheorie für PSEs zu entwickeln. Dazu sollen bestehende theoretische Ansätze gesammelt, integriert und erweitert sowie mögliche Modifikationen der PSE-Methode diskutiert werden. Neben der Vernetzung der einzelnen Forschungsprogramme bestehen die konkreten Ziele des beantragten Netzwerks zum einen in der Erstellung und Einreichung eines gemeinsamen Überblicksartikels und zum anderen in der Konzeption und Beantragung kooperativer DFG-Projekte zur Untersuchung noch offener und neu aufgeworfener Fragen.

Improving the course „Scientific working“ at the B.Sc. Psychology at LMU Munich. Grant from BMBF (28,600 €; together with Michael Zehetleitner, Catholic University Eichstätt).
Automatic classification of the emotional impact of video sequences. Grant from German Research Foundation (DFG SCHO1334/4-1; 230,000€). This is an interdisciplinary project together with Prof. Klaus Diepold (TU Munich; Computer Science), with an overall volume of 520,000€

The concept Quality of Experience (QoE) tries to assess the subjective quality which an observer experiences when consuming multimedia content. In the current proposal, QoE should be extended by a new central component, namely the emotional state an observer experiences when watching a video. The core of our project is to extract emotionally relevant key features if videos using several machine learning approaches, which in turn are used to predict the experienced emotion of the observer. In order to realize this interdisciplinary research question technically, we draw upon and extend psychological models of emotion, which are then computationally implemented by engineers. Supervised machine learning approaches are trained by comparing the predicted with the actual emotional experience of an observer. This learning stage will use a Crowd Sourcing approach which allows to gather a large amount of data for relatively low costs. The large sample allows a broad generalizability of the results, and allows to investigate how personality characteristics of the observers modulate the emotional impact of the videos. The results from the online study will be cross-validated and extended in a controlled laboratory experiment. In this study, objective and indirect indicators of the experienced emotions are assessed (electromyography of facial expressions; changes in skin conductance). The interdisciplinary project is composed of researchers from psychology and computer engineers and works on the following research questions: a) Is it possible to determine a mapping of technical features onto emotionally relevant stimuli? b) Are psychological models able to correctly predict the experienced emotions based on relevant stimuli of the video and personality features of a person? Goals of the project are, on the one hand, to implement existing psychological theories of emotion, validate them using large data sets, and to develop them further based on these results. On the other hand, the technical framework should be employed to classify a large set of videos concerning the emotional impact on observers. Furthermore, the project will generate a standardized data base of videos, for which the emotional impact is very well known. We expect that such a data base will have a huge impact and benefit for future studies in the area of human emotions.

The dynamics of implicit motives in close relationships. Grant from German Research Foundation (DFG SCHO1334/5-1; 237,800€). This project is in close cooperation with an affiliated DFG project by Birk Hagemeyer (Uni Jena), with an overall volume of 463,400€.

The research project aims at an extensive examination of the relevance of implicit motives for (a) long-term developmental trajectories and (b) short-term regulatory processes in intimate couple relationships. Implicit motives are defined as dispositional needs which, despite their non-conscious representation, energize and direct subjective experience and behavior towards specific classes of affectively valued goal states. In the domain of couple relationships, such goal states can be classified as communal (i.e., related to intimacy and closeness with one's partner) or agentic (i.e., related to independence, mastery, and dominance). Communal and agentic motives are distinct need dimensions which have been shown to be valid predictors of relationship quality in cross-sectional and prospective studies. However, longitudinal studies with repeated motive assessments that allow for analyses of the dynamic transactions between motives and couple relationships are missing. Moreover, the motivational processes and mechanisms that underly the observed associations between motives and relationships are largely unknown. The proposed research project aims to fill these gaps in our knowledge about the role of motives in couple relationships. First, we intend to assess implicit motives in a subsample of the pairfam panel study three times in time intervals of one year. This design allows for (a) the prediction of long-term developmental trajectories in couple relationships and (b) analyses of the dynamic transactions between relationships and implicit motives. Second, we intend to implement an extensive experience sampling study to investigate (a) individual short-term processes of motivation and (b) dyadic patterns of coregulation that mediate the influences of implicit motives on couple relationships.


Grants (Co-PI)

Bayesian evidence synthesis: New meta-analytic procedures for measuring, monitoring, combining, and projecting statistical evidence. SSMART grant (29,767$; Wagenmakers, Gasman, Gronau, & Schönbrodt).
Examining the Reproducibility of Meta-Analyses in Psychology. SSMART grant (30,000$; Lakens, …, & Schönbrodt).
Bayesian Hypothesis Testing without Tears: An Interactive Introduction for Psychology Teachers and Students. APS Fund for Teaching and Public Understanding of Psychological Science (5000$; Wagenmakers, Schönbrodt, & Morey).