PsychArchives (ZPID Leibniz-Zentrum für Psychologische Information und Dokumentation)
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Attitudes, knowledge, and justifications concerning industrially farmed animal welfare between residents of high and low animal agriculture states
No full textDo residents of states with high levels of animal agriculture have different views about animal welfare on industrialized farms compared to residents of states with low levels of animal agriculture? In a survey of residents of high and low farmed animal agriculture states in the USA (N = 1985), we found that views about farmed animal welfare were largely similar between residents of those two sets of states. Using an extreme groups analysis of the 5 highest animal agriculture (e.g., Oklahoma) and 5 lowest animal agriculture states (e.g., Massachusetts), there were no measurable differences on some key outcome variables (e.g., mental state attributions to farmed animals, knowledge of factory farming, killing practices on industrialized farms, state and farmers’ responsibility for farmed animal welfare). Among the variables where we found measurable differences (e.g., those in high animal agriculture states, compared to those in low agriculture states, knew less about animals used as food and had lower estimates of the percent of farmed animals on industrial farms), the size of those differences was small (mean Cohen’s d of variables with significant differences = |0.18|) and none involved a qualitative shift (e.g., from agree to disagree). Moreover, predictors of those views were significant and stable across residents of the two sets of states and consistent with previous research (e.g., knowledge significantly predicted magnitude of factory farming independent of state of residency). These results may help inform where, for what, and by how much differences among residences high and low animal agriculture states matter.peerReviewedpublishedVersio
Signs of change: Estimating the impact of animal cruelty billboards on plant-based and dairy milk consumption in the UK
No full textWe present a field experiment to evaluate a social marketing campaign encouraging people to try plant-based milk. We ran six anti-dairy billboards for one month in a city in the UK. The billboards featured a photo of a suffering dairy cow, a link to a website with information about dairy cow suffering, and an appeal to try plant-based milk. To estimate the impact of the billboards on plant-based and dairy milk consumption, we triangulated three novel data collection methods. First, we compared changes in regional vs. nationwide sales data from two plant-based milk companies. Second, we evaluated the proportion of dairy-free orders from six cafes in the city where we ran the billboards (‘Billboard City’) before, during, and after the campaign. Third, we compared changes in the proportion of household waste representing plant-based or dairy milk in the Billboard City vs. another UK city with no intervention (‘Control City’). Although descriptively, our results appear to be in line with some positive impact of the billboards, ultimately the study design and data were too limited to support a general claim about the impact of the billboard campaign. There were logistical challenges with each data source, as well as too many extraneous factors for the design to account for adequately. We discuss the challenges of field research, the strengths and weaknesses of each novel data collection method, and present considerations for future research.peerReviewedpublishedVersio
Book review of “Numerical cognition and the epistemology of arithmetic” by Markus Pantsar
No full textNo abstract available.notReviewedpublishedVersio
New editorial team at the Journal of Numerical Cognition
No full textNo abstract available.notReviewedpublishedVersio
Supplementary materials for: Non-binary Counsellors' Experience of Self-Disclosure
No full textSupplementary materials for: Non-binary Counsellors' Experience of Self-Disclosureunknownunknow
Code for: Agent-Based Model presented at DPK
No full textThe satisfaction of interpersonal motives is modelled with Agent-Based Modeling and three different variants simulating different environments are compared. Results are presented on a conference poster session (DPK 2025).unknownunknow
Dataset on resources availability vs. usage, scientific work (deliberate practice, Domain Impact Level of Activities, DILA), its outcomes (e.g. number of publications), and achievement emotions (career satisfaction, job satisfaction, worries about the future) of Ph.D. students (study 2 of 2)
No full textResources are necessary for learning and achievement. The dataset allows for the distiction of merely having and actually using resources and was used to test how well these two differentiated measures predict work behavior, its outcomes and achievement emotions in Ph.D. students (article submitted).Dataset for: Harder, B. (2025). Differential effects of resource availability and usage on learning, achievement, and subjective well-being. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2501440unknow
Breaking the silence with authenticity: Evaluating a leadership and employee training in a scenario-based experiment
No full textEmployee silence, particularly acquiescent and quiescent silence, is linked to emotional exhaustion and impaired psychological well-being. Prior research highlights the role of authenticity in mitigating silence-related outcomes.
This study evaluates a new online training designed to foster authenticity in work teams. Tailored versions exist for leaders and employees. The training is expected to reduce silence and emotional strain.
Twelve pseudo-teams per condition (intervention group/active control group), each consisting of five “employees” and one “leader”, participate in the study. The intervention group will work in the laboratory on three occasions to complete the training "Working Authentically," while the active control group will participate in a training of equal duration focusing on breaks and recovery in the workplace context. As part of both the pre- and post-assessment, all teams engage in a simulated meeting, designed to assess behavioral indicators such as emotional expression and silence under emotionally challenging conditions. Using a pre-post longitudinal design with clustered data, the study combines self-report and observed data (expressed emotions). It aims to test training effects within a multilevel randomized controlled trial framework in a simulated work context.notReviewedothe
The Hybrid Modern Network Model: A Multi-Technique Framework for Comprehensive Network Analysis
No full textThis research addresses the limitations of traditional network models in capturing the complexity and dynamics of real-world social networks. Motivated by the need for a more comprehensive and flexible framework, the study introduces the Hybrid Modern Network Model (HMNM). The HMNM integrates foundational models like the Stochastic Block Model (SBM) and Preferential Attachment with advanced machine learning techniques, including Graph Neural Networks (GNNs), Reinforcement Learning (RL), Hierarchical Random Graphs (HRGs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs).
The methods employed involve constructing initial network structures using SBM, simulating network growth through preferential Attachment, learning node embeddings with GNNs, dynamically optimizing network properties using RL, capturing hierarchical community structures with HRGs, controlling degree distributions using GANs, and uncovering latent patterns with VAEs. The empirical illustration of HMNM highlights its effectiveness in providing a more realistic, scalable, and comprehensive analysis of social networks compared to traditional models. Integrating diverse methodologies allows for accurately modeling of network structures, dynamic processes, and latent patterns. In conclusion, the HMNM offers significant advancements in network modeling, providing a robust and flexible framework for analyzing social networks. This model overcomes the limitations of traditional models and delivers deeper insights into the complexities and dynamics of social structures. Future research will optimize the HMNM and explore its applications across various domains. The R programming code used for the network simulations and visualizations is conceptual and demonstrates the HMNM framework. The results and metrics are illustrative placeholders, emphasizing the methodology rather than empirical validation.reviewedacceptedVersio
Verzeichnis Testverfahren: Teil (2) Testverzeichnis geordnet nach Kurznamen (31., aktualisierte Auflage 2025)
No full textThe PSYNDEX database contains descriptions of tests, rating scales, questionnaires, interview methods, observation methods, test apparatus, computer-assisted diagnosis measures, and other diagnostic instruments used in German-speaking countries, covering all areas of psychology and education, as well as related disciplines such as medicine, psychiatry, and business. It also contains descriptions of German-language adaptations of American and English tests, as well as tests in other languages. In addition to published tests, unpublished research instruments are also documented. The German-language Verzeichnis Testverfahren (Test Directory) provides test abbreviations, test names, test authors, year of publication, and record number of test description in the PSYNDEX database for 8,826 psychological and educational tests in use in the German-speaking countries (Germany, Austria, Switzerland, Luxembourg). It is structured in five sections. Part 1 organizes tests according to the test categories used in the PSYNDEX database, part 2 lists test abbreviations alphabetically, part 3 lists test names alphabetically, part 4 lists tests by test author(s), part 5 lists test reviews by test abbreviations alphabetically. (M.E. - ZPID)unknownunknow