The frequent practice of overall fit evaluation for latent variable models in educational and behavioral research is reconsidered. It is argued that since overall plausibility does not imply local plausibility and is only necessary for the latter, local misfit should be considered a sufficient condition for model rejection, even in the case of omnibus model tenability. The argument is exemplified with a comparison of the widely used one-parameter and two-parameter logistic models. A theoretically and practically relevant setting illustrates how discounting local fit and concentrating instead on overall model fit may lead to incorrect model selection, even if a popular information criterion is also employed. The article concludes with the recommendation for routine examination of particular parameter constraints within latent variable models as part of their fit evaluation.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377339 | PMC |
http://dx.doi.org/10.1177/0013164420944566 | DOI Listing |
J Surg Educ
January 2025
Washington University of St. Louis, Department of Orthopaedic Surgery, St. Louis, Missouri.
Objective: Orthopedic residents are tasked with rapidly acquiring clinical and surgical skills, especially during their PGY-1 year. However, resource constraints and other factors frequently cause skills training to fall short of established guidelines. We aimed to design and evaluate a cross-institutional, month-long curriculum aimed at pooling resources to optimize training.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China.
The abrupt drop of resistance to zero at a critical temperature is a key signature of the current paradigm of the metal-superconductor transition. However, the emergence of an intermediate bosonic insulating state characterized by a resistance peak preceding the onset of the superconducting transition has challenged this traditional understanding. Notably, this phenomenon has been predominantly observed in disordered or chemically doped low-dimensional systems, raising intriguing questions about the generality of the effect and its underlying fundamental physics.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFACS Nano
January 2025
NOVA Medical School|Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisbon 1169-056, Portugal.
The "" under this Perspective underline the importance of interdisciplinary collaboration and partnerships across several disciplines, such as medical science and technology, medicine, bioengineering, and computational approaches, in bridging the gap between research, manufacturing, and clinical applications. Effective communication is key to bridging team gaps, enhancing trust, and resolving conflicts, thereby fostering teamwork and individual growth toward shared goals. Drawing from the success of the COVID-19 vaccine development, we advocate the application of similar collaborative models in other complex health areas such as nanomedicine and biomedical engineering.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!