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Direct linkVeronika Batzdorfer; Wolfgang Zenk-Möltgen; Laura Young; Alexia Katsanidou; Johannes Breuer; Libby Bishop – Research Ethics, 2024
Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science…
Descriptors: Literature Reviews, Social Science Research, Data Analysis, COVID-19
Tina Law; Elizabeth Roberto – Sociological Methods & Research, 2025
Although there is growing social science research examining how generative AI models can be effectively and systematically applied to text-based tasks, whether and how these models can be used to analyze images remain open questions. In this article, we introduce a framework for analyzing images with generative multimodal models, which consists of…
Descriptors: Artificial Intelligence, Visual Aids, Open Source Technology, Social Science Research
Seliem El-Sayed; Filip Paspalj – Research Ethics, 2024
Recital 33 GDPR has often been interpreted as referring to 'broad consent'. This version of informed consent was intended to allow data subjects to provide their consent for certain areas of research, or parts of research projects, conditional to the research being in line with 'recognised ethical standards'. In this article, we argue that broad…
Descriptors: Ethics, Social Science Research, Standards, Data Analysis
Download full textKane Meissel; Esther S. Yao – Practical Assessment, Research & Evaluation, 2024
Effect sizes are important because they are an accessible way to indicate the practical importance of observed associations or differences. Standardized mean difference (SMD) effect sizes, such as Cohen's d, are widely used in education and the social sciences -- in part because they are relatively easy to calculate. However, SMD effect sizes…
Descriptors: Computer Software, Programming Languages, Effect Size, Correlation
Ke-Hai Yuan; Ling Ling; Zhiyong Zhang – Grantee Submission, 2024
Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…
Descriptors: Structural Equation Models, Computation, Social Science Research, Error of Measurement
Ke-Hai Yuan; Ling Ling; Zhiyong Zhang – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Data in social and behavioral sciences typically contain measurement errors and do not have predefined metrics. Structural equation modeling (SEM) is widely used for the analysis of such data, where the scales of the manifest and latent variables are often subjective. This article studies how the model, parameter estimates, their standard errors…
Descriptors: Structural Equation Models, Computation, Social Science Research, Error of Measurement
