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 1593

Special Issue Editors


E-Mail Website
Guest Editor
1. Department of Psychology, University of Zurich, Binzmuehlestrasse 14/27, 8050 Zurich, Switzerland
2. Institute of Psychology, University of Leipzig, Neumarkt 9, 04109 Leipzig, Germany
Interests: item response theory; psychological assessments; machine learning; factor analysis

E-Mail Website
Guest Editor
Statistical Methods in the Social Sciences, Department of Statistics, TU Dortmund, 44227 Dortmund, Germany
Interests: longitudinal data modeling; test development; meta-analysis; item response theory; psychometrics

E-Mail Website
Guest Editor
Department of Kinesiology and Educational Psychology, College of Education, Washington State University, Pullman, WA 99164, USA
Interests: educational measurement; psychometrics; quantitative methods; large-scale assessment; missing data

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Psych is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 560 KiB  
Article
L0 and Lp Loss Functions in Model-Robust Estimation of Structural Equation Models
by Alexander Robitzsch
Psych 2023, 5(4), 1122-1139; https://doi.org/10.3390/psych5040075 - 20 Oct 2023
Viewed by 1012
Abstract
The Lp loss function has been used for model-robust estimation of structural equation models based on robustly fitting moments. This article addresses the choice of the tuning parameter ε that appears in the differentiable approximations of the nondifferentiable Lp loss functions. [...] Read more.
The Lp loss function has been used for model-robust estimation of structural equation models based on robustly fitting moments. This article addresses the choice of the tuning parameter ε that appears in the differentiable approximations of the nondifferentiable Lp loss functions. Moreover, model-robust estimation based on the Lp loss function is compared with a recently proposed differentiable approximation of the L0 loss function and a direct minimization of a smoothed version of the Bayesian information criterion in regularized estimation. It turned out in a simulation study that the L0 loss function slightly outperformed the Lp loss function in terms of bias and root mean square error. Furthermore, standard errors of the model-robust SEM estimators were analytically derived and exhibited satisfactory coverage rates. Full article
Show Figures

Figure 1

Back to TopTop