Behaviormetrika - Call for Papers on "Bridging the Gap between Machine Learning and Psychological Measurement"

Behaviormetrika is excited to announce a special issue dedicated to the fusion of machine learning techniques with classification methods in the context of psychological and behavioral measurement. Although classification and scaling have seemingly opposite goals, many measurement contexts can be explained, explored, and/or implemented using classification methodology. With the rapid pace of emerging classification methods used by and implemented in machine learning algorithms, we envision the process of psychological and behavioral measurement changing. This special issue seeks to provide some guidance on how we can best harness classification methodology for measurement purposes. We invite researchers from diverse fields, including data science, psychology, education, and the social sciences, to contribute their original research and insights to this innovative issue.

Aims and Scope

Machine learning methods have revolutionized the field of data science and offer unique opportunities for enhancing our understanding of psychological and behavioral phenomena. This special issue seeks to showcase the application of machine learning algorithms and classification techniques to address fundamental questions in psychological and behavioral measurement while emphasizing approaches that mirror confirmatory approaches to measurement. To that end, papers that feature purely exploratory or unsupervised methods fall outside the scope of this special issue. Topics of interest include but are not limited to:

• Integration of machine learning with diagnostic classification models
• Bayesian classification methods enhanced by machine learning
• Deep learning approaches for psychological assessment within a confirmatory framework
• Item response theory combined with machine learning for classification tasks
• Computerized adaptive testing empowered by machine learning algorithms 
• Novel interdisciplinary applications of machine learning in educational and behavioral measurement 
• Innovative psychometric approaches utilizing machine learning 
• Ethical considerations in the use of machine learning for psychological and behavioral classification

Submission Guidelines

Please prepare your paper following Behaviormetrika’s submission guidelines (this opens in a new tab). All papers must be submitted to the journal's submission system (this opens in a new tab).

While submitting, please select "Yes" for the question “Does this manuscript belong to a special feature?” and then select the special feature “S.I. : Bridging the Gap between Machine Learning and Psychological Measurement”.

In addition, please indicate in your cover letter that your submission is intended for the "Bridging the Gap between Machine Learning and Psychological Measurement" special issue. The initial drafts should be submitted by the deadline stipulated below.

All submissions will undergo a rigorous peer-review process to ensure the highest quality and relevance to the special issue's theme. Please note that fully unsupervised or exploratory methods are outside the scope of this special issue.

Important Dates

• Manuscript submission deadline: February 1, 2024
• First round of reviews: May 1, 2024
• Revisions due: July 1, 2024
• Second round of reviews: September 1, 2024
• Revisions due: November 1, 2024
• Final editorial decisions: December 1, 2024
• Publication of the special issue: January, 2025

Guest Editors

Jonathan Templin, University of Iowa

Inquiries

Please direct inquiries to the special issue's Guest Editor at jonathan-templin@uiowa.edu.

Join us in advancing the field of psychological and behavioral measurement by integrating machine learning into the classification methods. We look forward to receiving your innovative research contributions and promoting meaningful discussions on how machine learning can enhance our understanding of human behavior and cognition.