Keynotes
The promises and pitfalls of complex statistics and simple feedback in psychology
Feedback based on the experience sampling method (ESM) is becoming increasingly popular. This ranges from simple descriptives, such as pie plots, to more complex statistics, such as psychological network models. Recently there has been a lot of criticism on network models, leading researchers to develop other kinds of feedback. One new development is collecting qualitative data with open text boxes and self-learning items, and giving back qualitative feedback based on this data. Another development is extending ESM measurements so that also the social context and activities can be captured in a more personalized way. In this talk, I will discuss various pitfalls in applying complex statistical models to ESM data, how to move forward with ESM measurement and feedback to align them better with clinical research.
Professor
Laura Bringmann
University of Groningen, The Netherlands
Professor
Minjeong Jeon
University of California at Los Angeles, USA
Measuring progress with an interaction map approach for longitudinal assessment data
In this talk, I will introduce a novel approach for longitudinal assessment data that involve item responses from individuals at two or more time points. A key limitation of existing longitudinal item response models is their inability to capture item-by-person interactions that can change over time. To address this, I propose an interaction map approach that can capture and visualize time-varying person-by-item interactions, offering valuable insights into individuals’ progress over time. Furthermore, I will present a more structured version of the interaction map approach, which focuses on tracking individuals’ progress toward a measurement target directly within the map. Real-world examples will be shared to illustrate the practical applications of the proposed methodologies.
Artificial Intelligence and Measurement
This presentation scrutinizes the transformative potential of Large Language Models (LLMs) in survey research, focusing on three critical areas: questionnaire design, synthetic data creation, and the role of LLMs as qualitative interviewers. In the domain of questionnaire design, the lecture delves into if and how LLMs can construct contextually accurate and highly effective survey items. However, there are valid concerns about the model’s understanding and potential biases, which we will critically evaluate. She also discusses LLMs’ ability to fabricate synthetic data, preserving core statistical properties whilst ensuring privacy. Here too, the ethical implications and the potential for misuse of this capability pose challenges that need to be addressed. Lastly, the lecture explores how LLMs, with their human-like conversational ability, can act as qualitative interviewers, allowing in-depth information gathering at scale. Yet, questions about their ability to fully capture the complexity and subtleties of human interaction and response also remain. The underlying theme of this talk is the question on how research in this space should be structured.
Professor
Frauke Kreuter
LMU München, Germany
