Social Science Experiments: Research Area — Prolific Citations Library

Discover 2 peer-reviewed studies in Social Science Experiments (2024–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 2 peer-reviewed papers in the research area of Social Science Experiments in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Using artificial intelligence to generate visual vignettes in factorial survey experiments

Authors: N Schwitter

Year: 2025

Published in: Social Science Computer Review, 2025 - journals.sagepub.com

Institution: University of Lucerne

Research Area: Artificial Intelligence in Social Science Research Methods, Factorial Survey Experiments, Visual Vignettes Generation

Discipline: Social Science

This paper explores the use of generative AI for creating visual vignettes in factorial survey experiments, highlighting their potential to boost realism and engagement while addressing ethical and technical challenges.

Methods: Techniques for generating and selectively editing AI-generated images were demonstrated, and a pretest with human participants was conducted to evaluate perceptions and interpretations of the images.

Key Findings: Application of AI-generated visual vignettes in social science research and participant interpretation of these images.

Citations: 1
Predicting results of social science experiments using large language models

Authors: L Hewitt, A Ashokkumar, I Ghezae, R Willer

Year: 2024

Published in: Preprint, 2024 - samim.io

Institution: Stanford University, New York University

Research Area: Social Science Experiments, Large Language Model Prediction, LLM

Discipline: Computational Social Science

The study presents a framework using large language models to predict outcomes of social science field experiments, achieving 78% accuracy but facing challenges with experiments on complex social issues.

Methods: Authors used an automated framework powered by large language models to predict outcomes of 276 field experiments drawn from economics literature.

Key Findings: The prediction accuracy of large language models for outcomes of field experiments addressing various human behaviors.

Citations: 68

Sample Size: 276