OSF
open access
Exploratory Item Response Matrix Factorization: Bridging Psychometrics and Machine Learning
Recent evolution in psychological and educational assessments has challenged traditional exploratory psychometric methods regarding analyzing high-dimensional sparse data and making accurate predictions, while methods from machine learning present potential for addressing relevant issues. This study
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Exploratory Item Response Matrix Factorization: Bridging Psychometrics and Machine Learning#1135415
- Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis#7357
- An assessment of the predictive validity of impact factor scores: Implications for academic employment decisions in social work#941859
- Data-driven approach to understand associations between dietary patterns, sleep problems, and mental health in adolescents.#298741
- How Many Factors Underlie the WAIS-5? Evidence From the Australian and New Zealand Standardization Sample.#989403
Record · ID 1135245
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive