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OSF-Materials for the Manuscript 'Explaining effect-heterogeneity: Adjustment for unintended differences between studies in conceptual replications'
No full textWhile previous research has described that intervention effects vary across replication studies, less effort has been devoted to identifying causes of this effect heterogeneity with regard to differences in study implementations. However, knowing in which way study characteristics (such as population, measurement instrument, setting, or treatment implementation) impact the study results may not only help to better infer the impact of research practices but also provide evidence for theory building.
Causal effects can be easily identified if all study characteristics but the one under investigation are kept constant across two studies. This is, however, not always possible in practice and unintended differences between the studies to be compared may confound the relationship of the study characteristic of interest and the treatment effect.
In this paper, we present a statistical approach for identifying effects of study characteristics on study-specific treatment effects from randomized experiments in cases in which unintended differences in study implementation across studies cannot be prevented.
We present formal definitions of the causal effects of interest, identification assumptions, and derive respective causal estimands. The assumptions can more likely be fulfilled in prospective replication studies or many-lab studies, where researchers have more control over design and measurement of covariates in both studies. We also provide ways to test the assumptions and illustrate consequences of not meeting the assumptions. The approach is illustrated using an empirical example on the imagined intergroup contact effect in social psychology
Neurobiological and Clinical Foundations of Yoga-Hypnotherapy Integration: A Comprehensive Review of Mechanisms, Applications, and Future Directions
No full textMeinungsmonitor Künstliche Intelligenz. Die KI-Nutzung unter Erwerbstätigen – Unterschiede zwischen Erwerbsklassen, Nutzungsprofilen und Folgenwahrnehmungen von KI am Arbeitsplatz
No full textDer Kurzbericht präsentiert zentrale Ergebnisse einer Segmentierungsstudie des Projekts Meinungsmonitor Künstliche Intelligenz 3.0 (MeMo:KI 3.0), die im Juni 2025 durchgeführt wurde. Analysiert wird die Nutzungshäufigkeit von Künstlicher Intelligenz (KI) unter 1.987 Erwerbstätigen in Deutschland sowie deren Zusammenhang mit Erwerbsklassen, soziodemografischen Merkmalen und arbeitsbezogenen Einstellungen. Die Ergebnisse zeigen deutliche Unterschiede zwischen Erwerbsklassen auf Grundlage der Oesch-Klassifikation. Höher qualifizierte Gruppen mit technischer oder soziokultureller Arbeitslogik – etwa technische Experten oder selbstständige Fachkräfte – berichten deutlich häufiger von regelmäßiger KI-Nutzung. Niedrigere Nutzungsraten finden sich dagegen in Erwerbsklassen mit geringerer formaler Qualifikation oder stärker standardisierten Tätigkeitsprofilen, etwa bei qualifizierten Arbeitern, Fachkräften im Dienstleistungsbereich oder kaufmännischen Angestellten. Insgesamt zeigt sich, dass ein erheblicher Teil der Erwerbstätigen bislang nur selten oder gar nicht mit KI arbeitet. Häufigere KI-Nutzung geht zugleich mit höherer subjektiver KI-Kompetenz und positiveren affektiven Einstellungen zu KI am Arbeitsplatz einher. Vielnutzende bewerten zudem die erwarteten Auswirkungen von KI auf Arbeitsbedingungen deutlich positiver und berichten geringere negative affektive und verhaltensbezogene Reaktionen auf die KI-Einführung am Arbeitsplatz. Insgesamt weisen die Befunde auf eine digitale Spaltung der KI-Nutzung entlang von Alter, Bildung und beruflicher Position hin
COVID-19 Vaccination Engagement and Protective Health Behaviors: The Roles of Decision Regret and Conspiracy Mentality
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A comprehensive overview of publication bias in dentistry: A meta-search
No full textThis meta-research aims to establish an overview of the prevalence, characteristics, and factors associated with publication bias in scientific publications in dentistry. A systematic search will be conducted in the MEDLINE/PubMed, Embase, Scopus, Web of Science, and gray literature databases, without language or date restrictions, to identify studies that investigated publication bias in dental research. Two independent reviewers will perform data selection and extraction, including study characteristics, publication bias detection methodologies, overall prevalence, associated factors, and distribution by dental specialty. The results will be summarized narratively with descriptive statistics of the characteristics of the included studies, prevalence of publication bias by dental area, methods used to detect publication bias, temporal analysis of the evolution of knowledge, and associated factors identified. We expect to provide a comprehensive overview of current knowledge on publication bias in dentistry, identify prevalences in the area, determine association factors, and recommend future research in areas of dentistry that have not yet evaluated this bias, in addition to suggesting potential actions to mitigate the problem
Topology Controls Synchronization Persistence in Noisy Kuramoto Networks: Evidence from Matched-Pair Simulations
No full text50-seed matched-pair Kuramoto simulations (three graph families: Erdős–Rényi, Watts–Strogatz, triangle-enriched) showing that motif-rich topologies produce significantly longer coherence lifetimes under noise at all three noise levels. Includes full statistical results and three professional plots. The recurrence also maps onto stability models in power-grid synchronization and swarm robotics
Sperm function testing to predict poor fertilisation and fertilisation failure
No full textSemen analysis, which has been used to measure male fertility for over a century since its debut in the 1900s, is still a commonly used tool for examining semen features such as sperm count, motility, and morphology.(Cary and Hotchkiss, 1934, Hotchkiss, 1936, Andrade-Rocha, 2017, World Health Organization, 2021, Wang et al., 2022) Despite its extensive history, this technique falls short of directly assessing the biological capability to result in a successful pregnancy and healthy livebirth. With the exception of azoospermia situations when sperm is completely absent, it is generally established that sperm analysis is not an absolute predictor of fertility.(Barratt et al., 2010, Patel et al., 2019, Björndahl, 2022) The limits of standard semen analysis in providing a thorough understanding of sperm function highlights the need for research and advancement in the field of male fertility assessment. We proposed a narrative review to explore available sperm function tests and their capability to predict poor fertilisation and fertilisation failure. We have excluded sperm DNA damage and oxidative stress as these have been extensively explored and reviewed across the literature
