AI Medical Compendium Journal:
Psychometrika

Showing 1 to 5 of 5 articles

A Unified Neural Network Framework for Extended Redundancy Analysis.

Psychometrika
Component-based approaches have been regarded as a tool for dimension reduction to predict outcomes from observed variables in regression applications. Extended redundancy analysis (ERA) is one such component-based approach which reduces predictors t...

Transformer-Based Deep Neural Language Modeling for Construct-Specific Automatic Item Generation.

Psychometrika
Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not id...

Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding.

Psychometrika
Recently, machine learning (ML) methods have been used in causal inference to estimate treatment effects in order to reduce concerns for model mis-specification. However, many ML methods require that all confounders are measured to consistently estim...

A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis.

Psychometrika
Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator's consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-t...

Automated Item Generation with Recurrent Neural Networks.

Psychometrika
Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Hum...