Special Issue "Learning from Psychometric Data"
Deadline for manuscript submissions: 15 October 2020.
Interests: measurement error models; multilevel modeling; item response models; Rasch modeling; equating; social network analysis
In recent years, technology has enhanced the possibility of collecting and analyzing high-dimensional datasets, meaning both a large number of observations and a large number of variables. The complexity of the data often requires elaborate psychometric models that involve many parameters, possibly leading to numerical instability of the estimates. This is especially true in small samples, due to the limited information available. In all cases, it is important to extract the relevant patterns and relations, disentangling them from the noise. In this respect, statistical learning approaches could be exploited to reduce the variability of the estimates or attain sparse solutions, hence improving the interpretability of the results and the predictive capacity of the model. This Special Issue of Psych aims to review the current state-of-the-art on statistical learning methods for psychometric data, propose methodological advancements, compare methods available in the literature by simulation and/or application to real data, and describe interesting case studies. This Special Issue aims to cover, without being limited to, the following areas:
- Shrinkage methods;
- Variable selection;
- Ensemble methods;
- Dimension reduction.
Dr. Michela Battauz
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 papers will be 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 1000 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.
- Statistical learning
- Machine learning
- Data mining
- Data analysis