
Behaviormetrika is issued twice a year to provide an international forum for new theoretical and empirical quantitative approaches in data science. When Behaviormetrika was launched in 1974, the journal advocated data science, as an interdisciplinary field that included the use of statistical methods to extract meaningful knowledge from data in its various forms: structured or unstructured. Behaviormetrika is the oldest journal addressing the topic of data science. The first editor-in-chief of Behaviormetrika, Dr. Chikio Hayashi, described data science in this way:
“Data science is not only a synthetic concept to unify statistics, data analysis, and their related methods; it also comprises its results. Data science is intended to analyze and understand actual phenomena with ‘data.’ In other words, the aim of data science is to reveal the features or the hidden structure of complicated natural, human, and social phenomena using data from a different perspective from the established or traditional theory and method.”
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
Journal information
- Editor-in-Chief
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- Maomi Ueno
- Publishing model
- Hybrid (Transformative Journal). Learn about publishing Open Access with us
Journal metrics
- 58 days
- Submission to first decision
- 191 days
- Submission to acceptance
- 20,479 (2019)
- Downloads
Latest issue
Latest articles
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WAIC and WBIC for mixture models
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Standard error estimates for rotated estimates of canonical correlation analysis: an implementation of the infinitesimal jackknife method
Authors (first, second and last of 4)
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Journal updates
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COVID-19 and impact on peer review
As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times. Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.
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Call for Papers on "Work at the Intersection of Educational Data Mining and Statistics"
Guest Editors:
Ryan Baker (rybaker@upenn.edu) and Bryan Keller (keller4@tc.columbia.edu)Timeline:
First draft submission: November 1, 2020
Publication of the special issue: September 2021
Societies, partners and affiliations
About this journal
- Electronic ISSN
- 1349-6964
- Print ISSN
- 0385-7417
- Abstracted and indexed in
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- ANVUR
- CNKI
- Dimensions
- EBSCO Discovery Service
- Google Scholar
- Institute of Scientific and Technical Information of China
- Japanese Science and Technology Agency (JST)
- Naver
- OCLC WorldCat Discovery Service
- ProQuest-ExLibris Primo
- ProQuest-ExLibris Summon
- SCImago
- SCOPUS
- TD Net Discovery Service
- UGC-CARE List (India)
- Copyright information