Wilhelm Gros

Project

Predicting attitudes and behavior concerning living materials: Assessing the reliability of the CAM method as a measurement tool
Evaluations of behavioral decisions constitute complex cognitive and affective psychological processes with many determining factors. Consequently, we expect a multitude of factors to affect the acceptance of living materials systems. In this project, we aim to advance “Cognitive Affective Mapping (CAM)” as a novel approach that can be used to predict acceptance by assessing cognitive concepts and their affective connotation. In recent years, we’ve explored the application of the CAM method on various research questions, programmed different software systems to implement CAM, and discussed the first analysis approaches. In this research project, we aim to take a closer methodological look on the CAM method. We focus on reliability as one of the classical core quality criteria in psychological test construction. Reliability refers to the consistency of a measurement and the extent of measurement errors. In a first study, we use a classical approach to assess reliability by measuring a stable item twice. From the correlation of the two measurements, we get a first indication of the reliability of the ZAV. Subsequent studies will focus first on how best to implement CAM to achieve high reliability, and second on how CAM and text are convertible into each other. The overall goal is to strengthen CAM research by better understanding its reliability and to provide a powerful tool for evaluating the acceptability of living materials systems.

First supervisor

Prof. Dr. Andrea Kiesel

Publications in livMatS

• Cognitive-Affective Maps (CAMs) as Measurement Tool – Elaboration of Quantitative and Qualitative Test-Retest Reliability*
  Gros, W., Reuter, L., Sprich, J., Schuldzinski, D., Fenn, J., & Kiesel, A. (2024). Cognitive-Affective Maps (CAMs) as Measurement Tool – Elaboration of Quantitative and Qualitative Test-Retest Reliability. Technology in Society, 102651. doi: 10.1016/j.techsoc.2024.102651