Psychometric Methods: Theory and Practice

Dear Colleagues,

Measurement and quantification are ubiquitous in modern society. The historical foundation of psychometrics arose from the need to measure human abilities through suitable tests. This discipline then underwent rapid conceptual growth due to the incorporation of advanced mathematical and statistical methods. Today, psychometrics not only covers virtually all statistical methods but also incorporates advanced techniques from machine learning and data mining that are useful for the behavioral and social sciences, including but not limited to the handling of missing data, the combination of multiple-source information with measured data, measurement obtained from special experiments, visualization of statistical outcomes, measurement that discloses underlying problem-solving strategies, and so on. Psychometric methods now have a wide range of applicability in various disciplines, such as education, psychology, social sciences, behavioral genetics, neuropsychology, clinical psychology, medicine, and even visual arts and music, to name a few.

The dramatic development of psychometric methods and rigorous incorporation of psychometrics, data science, and even artificial intelligence techniques in interdisciplinary fields have aroused significant attention and led to pressing discussions about the future of measurement.

The aim of this Special Topic is to gather studies on the latest development of psychometric methods covering a broad range of methods, from traditional statistical methods to advanced data-driven approaches, and to highlight discussions about different approaches (e.g., theory-driven vs. data-driven) to address challenges in psychometric theory and practice.

This Special Topic consists of two subtopics: (1) theory-driven psychometric methods that exhibit the advancement of psychometric and statistical modeling in measurement to contribute to the development of psychological and hypothetical theories; and (2) data-driven computational methods that leverage new data sources and machine learning/data mining/artificial intelligence techniques to address new psychometric challenges.

In this issue, we seek original empirical or methodological studies, thematic/conceptual review articles, and discussion and comment papers highlighting pressing topics related to psychometrics.

Interested authors should submit a letter of intent including (1) a working title for the manuscript, (2) names, affiliations, and contact information for all authors, and (3) an abstract of no more than 500 words detailing the content of the proposed manuscript to the topic editors.

There is a two-stage submission process. Initially, interested authors are requested to submit only abstracts of their proposed papers. Authors of the selected abstracts will then be invited to submit full papers. Please note that the invitation to submit does not guarantee acceptance/publication in the Special Topic. Invited manuscripts will be subject to the usual review standards of the participating journals, including a rigorous peer review process.

Dr. Qiwei He
Dr. Yunxiao Chen
Prof. Dr. Carolyn Jane Anderson
Topic Editors