Program

You can find the program here.

The conference starts Tuesday, August 6, 2024 at 18:00 (6:00 p.m.)

The conference dinner will be Thursday, August 8, 2024 at 20:00 (8:00 p.m.).

The Early Career Evening will take place on Wednesday, August 7, 2024 at 20:30 (8:30p.m.).

The workshops will be held on August 5, 2024.

VENUE ADDRESS

The conference will be held at the following address:

Humboldt-Universität zu Berlin
Universitätsgebäude am Hegelplatz 
Dorotheenstraße 24
10117 Berlin

Attention: Workshops will be held on August 5, here:

Psychological Institute
Rudower Chaussee 18
12489 Berlin


You can find more information about the preliminary timeline in the following tables.
Time information corresponds to the Eastern European time zone.

Tuesday, August 6, 2024
Start End Content
18:00 18:15 Opening Ceremony
18:15 19:15 Keynote
19:15   Get together

 

Wednesday, August 7, 2024
Start End Content
08:30 09:30 Session 1
09:30 09:45 Pause
09:45 10:45 Keynote
10:45 11:00 Pause
11:00 12:30 Session 2
12:30 13:30 Lunch
13:30 15:00 Session 3
15:00 15:15 Pause
15:15 16:45 Session 4
16:45 17:00 Pause
17:00 18:30 Session 5
18:30 18:40 Pause
18:40 20:00 Poster Session
20:30   Early Career Evening

 

Thursday, August 8, 2024
Start End Content
09:00 10:00 Session 1 
10:00 10:15 Pause
10:15 11:45 Session 2
11:45 12:45 Lunch
12:45 14:15 Keynote
14:15 14:30 Pause
14:30 16:00 Session 3
16:00 16:15 Pause 
16:15 17:45  Session 4
17:45 18:45 Members Meeting
20:00   Conference Dinner

 

Friday, August 9, 2024
Start End Content
09:00 10:00 Session 1
10:00 10:15 Pause
10:15 11:00 Meet the editor
11:00 12:00 Keynote
12:00 13:00 Lunch
13:00 14:30 Session 2
14:30 14:45 Pause
14:45 16:15 Session 3
16:15   Closing / Farewell

 

Keynotes

Keynote speakers:

ANNA BROWN, University of Kent


© University of Kent

 

CORNELIA WRZUS, Universität Heidelberg


© Universität Heidelberg, KuM

 

AIDAN WRIGHT, University of Michigan, LSA


© Aidan G. C. Wright

 

Workshops (August 5th, 2024)

Best Practices and Innovative Approaches to Assessing Character Traits

Juliette Ratchford1 & Eranda Jayawickreme1
1Wake Forest University

In recent years there has been increased interest within personality psychology in studying moral (e.g., fairness, honesty) and other character traits (e.g., intellectual humility). However, there are distinctive challenges with studying these traits. For example, there is disagreement on the core content of such traits (e.g., recent work on honesty [Fleeson et al., 2022] and intellectual humility [Porter et al., 2022]). These traits are furthermore socially desirable, meaning that there are unique challenges with their empirical assessment. In this workshop, we will present innovative approaches (based on our own work) to conceptualizing and assessing such traits. The workshop would give 1) recommendations for how the core content of such traits should be identified and how items should be developed, 2) an overview of appropriate assessment approaches, 3) the type of research questions that can be addressed with different approaches, 3) resources pertinent to the approach (e.g., seminal method papers), 4) code/packages in both R and (when applicable) Mplus, and 5) considerations for cultural research on character traits.

 

Processing Digital Footprints of Behavior

Larissa Sust1 & Ramona Schoedel2 
1LMU Munich, Department of Psychology, Munich, Germany
2Charlotte Fresenius Hochschule, University of Psychology, Department of Psychology, Munich, Germany

With a strong focus on questionnaire assessments throughout the past decades, personality researchers
have largely neglected the study of actual behavior “in situ.” While investigating behavior in the field was
practically infeasible in the past, people now automatically produce behavioral data every time they use
online platforms like social media sites and streaming services or digital devices like smartphones and
fitness watches throughout the day. These large quantities of digital footprints bring researchers closer
to the goal of studying people’s everyday lives but also provide new methodological challenges.
In our workshop, we will address one of those challenges: How to get from unstructured, high temporal
resolution digital data to meaningful behavioral variables needed for modeling psychological constructs.
In the first part of the workshop, we will give a non-technical, conceptual introduction to how to process
digital data. We draw on our own experience on how to draft variables from different types of digital
data and discuss how to handle the researcher's degrees of freedom in doing so (e.g., specifying
aggregation measures or time frames). Thereby, we also explain how external data sources can help
make sense of raw digital data points (e.g., enriching GPS data with location tags). In the second part of
the workshop, we will use a smartphone-sensing dataset to practice the variable extraction from digital
data. Workshop participants will have the opportunity to extract their own variables using the statistical
software R, under the supervision of the trainers. We will pay special attention to how to perform
variable extraction at larger scales over large samples.
After the workshop, participants will have the basic tools to set up their own projects working with
digital behavioral data for personality research. Participants should have at least basic knowledge of R
and bring their own laptop.

 

Moderated factor analysis in R using OpenMx

Dylan Molenaar1
1University of Amsterdam

Moderated factor analysis is a powerful tool to establish if the parameters in a factor model depend on one or more observed covariates (Bauer & Hussong, 2009; Bauer, 2017; Neale, 1998). For instance, in personality research, “personality differentiation” refers to the hypothesis that personality is more differentiated at the higher ends of general intelligence (Austin et al., 1997). This hypothesis can be studied for -for instance- neuroticism by testing if the neuroticism factor loadings depend on IQ. If the factor loadings decrease across IQ, there is support for the differentiation hypothesis. Other applications of moderated factor analysis include tests on measurement invariance and test fairness (i.e., testing if latent variables can meaningfully be compared across one or more covariates), testing for interactions between observed variables and latent variables, testing for age and ability differentiation (i.e., similar as above, but with the differentiation effect occurring across age and general intelligence respectively), and multi-group factor analysis with multiple grouping variables (e.g., age-group and sex) and possible interactions (e.g., age-group by sex effects on the factor variances).
In this workshop participants learn what moderated factor analysis is, how it works, and how it can be applied to real data using R-package OpenMx (Boker et al., 2011). Focus will be mainly on moderated factor analysis for continuous indicators (moderated linear factor analysis), but some directions will be given for applications to discrete indicators (moderated non-linear factor analysis). In addition, as moderated factor analysis assumes all moderation effects to be (generalized-)linear, some attention will be devoted to a non-parametric version of this method (i.e., Local Structural Equation Modeling; Hildebrandt et al., 2016).