ArticleMIS quarterly : management information systems2025
AI-Augmented Content Validation in Behavioral Research: Development and Evaluation of the RATER System.
Article in MIS quarterly : management information systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
Funding
Abstract
Content validation is an essential aspect of the scale development process that ensures that measurement instruments capture their intended constructs. However, researchers rarely undertake this core step in behavioral research because it requires costly data collection and specialized expertise. We present RATER (Replicable Approach to Expert Ratings), a free web-based system (www.contval.org) that can help the broader research community (scientists, reviewers, students) gain quick and reliable insights into the content validity of measurement instruments. Guided by psychometric measurement theory, RATER evaluates whether a scale's items correspond to their intended construct, remain distinct from other constructs, and adequately represent all aspects of the construct's content domain. The system employs two unique artificial intelligence models, RATER
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.