Behaviormetrika

Aims & Scope

Behaviormetrika is a fully refereed international journal, which publishes original research papers, notes, and review articles.

Subject areas suitable for publication include but are not limited to the following methodologies and fields. - Methodologies: Data science; Mathematical statistics; Survey methodologies; Artificial intelligence; Information theory; Machine learning; Knowledge discovery in databases (KDD); Graphical models; Computer science; Algorithms. - Fields: Medicine; Psychology; Education; Economics; Marketing; Social science; Sociology; Political science; Policy science; Cognitive science; Brain science.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Mathematics and Psychology, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Beyond SEM: General Latent Variable Modeling

  Citation: 780

  Authors: Bengt O.

• The Bootstrap Method for Assessing Statistical Accuracy

  Citation: 159

  Authors: Bradley, Robert

• A concept analysis of methodological research on composite-based structural equation modeling: bridging PLSPM and GSCA

  Citation: 128

  Authors: Heungsun, Marko, Jun Hwa, Christian M.

• Some Mathematical Concepts of the Analytic Hierarchy Process

  Citation: 108

  Authors: Thomas L.

• On Averaging Variables in a Confirmatory Factor Analysis Model

  Citation: 104

  Authors: Ke-Hai, Peter M., Yutaka

• Lingam: Non-Gaussian Methods for Estimating Causal Structures

  Citation: 85

  Authors: Shohei

• Is Omega Squared Less Biased? a Comparison of Three Major Effect Size Indices in One-Way Anova

  Citation: 84

  Authors: Kensuke

• Nonmetric Multidimensional Scaling of Asymmetric Proximities

  Citation: 83

  Authors: Akinori, Tadashi

• Feature Selection With Neural Networks

  Citation: 69

  Authors: Philippe, Patrick

• How to conceptualize, represent, and analyze log data from technology-based assessments? A generic framework and an application to questionnaire items

  Citation: 68

  Authors: Ulf, Frank