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Alex Lyman; Bryce Hepner; Lisa P. Argyle; Ethan C. Busby; Joshua R. Gubler; David Wingate – Sociological Methods & Research, 2025
Generative artificial intelligence (AI) has the potential to revolutionize social science research. However, researchers face the difficult challenge of choosing a specific AI model, often without social science-specific guidance. To demonstrate the importance of this choice, we present an evaluation of the effect of alignment, or human-driven…
Descriptors: Artificial Intelligence, Computer Simulation, Open Source Technology, Social Science Research
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Jose M. Pavía; Rafael Romero – Sociological Methods & Research, 2024
The estimation of RxC ecological inference contingency tables from aggregate data is one of the most salient and challenging problems in the field of quantitative social sciences, with major solutions proposed from both the ecological regression and the mathematical programming frameworks. In recent decades, there has been a drive to find…
Descriptors: Elections, Voting, Social Science Research, Programming
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Amir Abdul Reda; Semuhi Sinanoglu; Mohamed Abdalla – Sociological Methods & Research, 2024
How can we measure the resource mobilization (RM) efforts of social movements on Twitter? In this article, we create the first ever measure of social movements' RM efforts on a social media platform. To this aim, we create a four-conditional lexicon that can parse through tweets and identify those concerned with RM. We also create a simple RM…
Descriptors: Social Media, Social Action, Natural Language Processing, Politics
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Xin Guo; Qiang Fu – Sociological Methods & Research, 2024
Grouped and right-censored (GRC) counts have been used in a wide range of attitudinal and behavioural surveys yet they cannot be readily analyzed or assessed by conventional statistical models. This study develops a unified regression framework for the design and optimality of GRC counts in surveys. To process infinitely many grouping schemes for…
Descriptors: Attitude Measures, Surveys, Research Design, Research Methodology
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Sandra Wankmüller – Sociological Methods & Research, 2024
Transformer-based models for transfer learning have the potential to achieve high prediction accuracies on text-based supervised learning tasks with relatively few training data instances. These models are thus likely to benefit social scientists that seek to have as accurate as possible text-based measures, but only have limited resources for…
Descriptors: Social Science Research, Transfer of Training, Natural Language Processing, Artificial Intelligence
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Richard A. Berk; Arun Kumar Kuchibhotla; Eric Tchetgen Tchetgen – Sociological Methods & Research, 2024
In the United States and elsewhere, risk assessment algorithms are being used to help inform criminal justice decision-makers. A common intent is to forecast an offender's "future dangerousness." Such algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In…
Descriptors: Criminals, Correctional Rehabilitation, Recidivism, Risk Assessment