Editorial Board Members' Collection Series: Latent Trait Models and Machine Learning
A special issue of Psych (ISSN 2624-8611). This special issue belongs to the section "Psychometrics and Educational Measurement".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1646
Special Issue Editors
2. Institute of Psychology, University of Leipzig, Neumarkt 9, 04109 Leipzig, Germany
Interests: item response theory; psychological assessments; machine learning; factor analysis
Interests: longitudinal data modeling; test development; meta-analysis; item response theory; psychometrics
Special Issue Information
Dear Colleagues,
Supervised and unsupervised techniques of machine learning have a considerable impact on psychological research. Alongside applications of current machine learning methods to genuinely psychological research questions, current progress in quantitative methodology often includes machine learning tailored to psychological applications. Recent methodological developments also include combinations of established latent trait models and machine learning, including the estimation of model parameters, the problem of variable and model selection, uncertainty quantification, and the detection of model violations. The Special Issue on "Latent Trait Models and Machine Learning" presents studies discussing applications of machine learning to latent trait models in psychology and related fields such as educational measurement. Comparisons of new and existing methods are also highly encouraged. Potential latent trait models include, but are not limited to, item response models, structural equation models, and latent class models.
Dr. Rudolf Debelak
Prof. Dr. Philipp Doebler
Dr. Shenghai Dai
Guest Editors
Manuscript Submission Information
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