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The journal Quantitative and Computational Methods in Behavioral Sciences (QCMB) strives to foster the development of methods in psychology and related fields. To achieve this aim, QCMB publishes scientific articles that are suited to extend the understanding of foundational mathematics used in psychological methods, development of new methods and software or hardware for those, comparison of existing or new methods, and dissemination of this knowledge to a broader audience of scientists in psychology or related fields. QCMB is dedicated to Open Science: All published articles are openly available for free. There is no publication fee for the review process or the publication. QCMB makes all articles available as pre-prints as soon as they go into the review process and keeps them available regardless of the formal decision.
QCMB publishes articles in two sections:
The fundamental research section targets an audience of quantitative psychologists, mathematicians, and statisticians with an interest in psychological applications of computational, statistical, and mathematical models. This section publishes articles that advance fundamental research in the field of quantitative psychology. Topics include but are not limited to mathematical foundations of statistical methods, introduction and investigation of heuristics that guide data-analytic decisions, introduction of algorithms, performance analyses of existing software, simulation studies that provide insights into mathematical properties of models, the adoption of statistical learning and machine learning paradigms to psychological inquiries, and mathematical theories related to statistical analyses in quantitative psychology.
The method dissemination section concentrates on methodological articles that target an audience of social scientists that want to apply top-notch analysis methods. Topics include but are not limited to introducing new statistical methods or software, tutorials explaining the application of statistical methods or software, simulation studies to compare the benefit of methods across different domains, introduction of new data tools used for methodology, and experimental design methods or tools, as for example for optimal study design planning.