Causal inference beyond the standard settings

Chair(s): Steffi Pohl, Tim Kaiser

Presentations of the Symposium

Definition and Identification of Causal Ratio Effects

Christoph Kiefer, Axel Mayer

Asymmetric Confidence Intervals for Average Treatment Effects on Binary Outcomes Using Difference- and Ratio-Effect Specifications

Kevin Hoppe, Christoph Kiefer

Using Causal Attribution for Individualized Treatment Selection

Tim Kaiser, Stephen G. West, Steffi Pohl

Explaining Effect Heterogeneity: Using Causal Decomposition across Replication Studies

Steffi Pohl, Soojin Park, Peter Steiner

From Path Diagrams to Causal Graphs: A Structural Approach to Multilevel Models

Moritz Ketzer, Christian Gische, Manuel Voelkle

Measurement and Machine Learning

Chair(s): Melanie Viola Partsch

Presentations of the Symposium

How Much Is Too Much? The Relationship Between Scalar Non-Invariance and Bias in Mean Comparisons

Caterina Luz Sanchez Steinhagen, Philipp Sterner, David Goretzko

A causal framework for cross-sectional and longitudinal investigations of measurement invariance

Philipp Sterner, David Goretzko

Non-Invariance in Machine Learning: Limiting the Comparability of Predictions

David Goretzko, Philipp Sterner

The Potential and Limits of Machine Learning in Rare Event Classifications

Kristin Jankowsky, Katrin Jansen, Florian Scharf

Introduction and illustration of a new method for detecting and classifying SEM misfit using machine learning

Melanie Viola Partsch, David Goretzko

New Approaches to Analysing Parameter Heterogeneity in Models of Response Processes and Temporal Dynamics

Chair(s): Thorsten Meiser, Esther Ulitzsch

Presentations of the Symposium

A Systematic Comparison of Mixture Distribution Models and Score-Based Partitioniong to Capture Response Process Heterogeneity with IRTree models

Emre Alagöz, Thorsten Meiser, Rudolf Debelak

Faking, Fast and Slow: A Response-Time-Based Latent Response Mixture Model to Account for Faking in High-Stakes Personality Assessments

Timo Seitz, Esther Ulitzsch

Variant Approaches to Assessing Measurement Invariance: A Comparison of MNLFA and SEM Trees for Detecting Differential Item Functioning

Leonie Hagitte, Andreas Markus Brandmaier

Exploring Heterogeneity in Temporal Dynamics with Different Extensions of Time-Varying Coefficient Models

Esther Ulitzsch, Oliver Lüdtke, Steffen Nestler, Therese Ruud Snuggerud, Sverre Urnes Johnson

Latent Markov Factor Analysis for Detecting Dynamics in Attentive and Careless Responding in Intensive Longitudinal Data

Leonie V.D.E. Vogelsmeier, Joran Jongerling, Esther Ulitzsch

Score-Based Tests with Fixed Effects Person Parameters in Item Response Theory

Rudolf Debelak, Charles Driver

Machine Learning in Psychology Part I: Evaluation and Guidelines for Using Machine Learning in Psychological Research

Chair(s): Mirka Henninger, Susanne Frick

Presentations of the Symposium

The top five things I wished I had known when starting to work with random forests

Carolin Strobl

Old Data + Machine Learning = New Knowledge? When Can We Expect Strong Predictive Performance in Psychology?

Florian Scharf, Kristin Jankowsky, Kim-Laura Speck, Katrin Jansen, Ulrich Schroeders

Evaluating Methods to Assess the Stability of Decision Trees

Constantin Wiegand, Florian Scharf, Mirka Henninger

How is Machine Learning Used and Interpreted in Psychological Research? A Systematic Review

Jan Radek, Carolin Strobl, Mirka Henninger

Multivariate Anomaly Detection Methods and their Application in Psychology

Loreen Sabel

Machine Learning in Psychology Part II: New Advancements in Statistical Methods Using Machine Learning and AI

Chair(s): Susanne Frick, Mirka Henninger

Presentations of the Symposium

Using Large Language Models for Abstract Screening

Mirka Henninger, Jan Radek, Martin Pauly

Developing and Evaluating Assessment Items: An LLM Framework

Rudolf Debelak, Rasmus Alexander Jensen, Alexandre Clin Deffarges, Laura Stahlhut

Individual Treatment Effect Estimation Through Transfer Learning

Seyda Betul Aydin, Roberto Faleh, Holger Brandt

Improving Causal Estimates with Sparse Autoencoders and Integral Probability Measures

Roberto Faleh, Sofia Morelli, Holger Brandt

Highly Efficient Anomaly Detection with Ensembles of Small Linear Models

Philipp Doebler

Combining machine learning with mixed effects models

Chair(s): Björn S. Siepe

Presentations of the Symposium

Moderator selection in meta-analysis using lasso and elastic net regularization

Katrin Jansen, Steffen Nestler

Tree-based methods for multilevel data: The influence of predictor levels on variable selection and prediction

Linus Hany, Mirka Henninger

Are Bayesian regularization methods a must for dynamic latent variable models?

Vivato V. Andriamiarana, Pascal Kilian, Holger Brandt, Augustin Kelava

Mixed effects LSTMs: Long short-term memory neural networks for hierarchical data

Anton Ernst, Daniel W. Heck, Björn S. Siepe

Versatility in the Application of Bayesian Statistics

Chair(s): Tina Braun

Presentations of the Symposium

Designing for Power: Managing Sample Size Amid Effect Size Uncertainty

Robert Miller, Timo von Oertzen

Using Flat Priors as a Simple Mean to Conduct Bayesian Multilevel Analysis

Tina Braun, Timo von Oertzen

Reading Emotions from the Real World: Embeddings over Categories

Hannes Diemerling, Patricia Kulla, Joachim Kruse

Why We Should Abandon Tests with Reduced Dimensionality, Even if They are Bayesian

Timo von Oertzen