MANOVA AS A TOOL FOR ENSURING THE INTEGRITY OF BIO-MEDICAL AND ENVIRONMENTAL RESEARCH
DOI:
https://doi.org/10.58407/bht.1.26.19Keywords:
academic integrity, multivariate analysis of variance, ecology, medical biology, scientific researchAbstract
Reproducibility and integrity of research are important principles of scientific, in particular biomedical and environmental, research, from the perspective of implementing sustainable development goals and the principles of open science.
Purpose of the work. The aim of the work is to prove that the use of multivariate analysis of variance in biomedical and environmental research is a tool for ensuring the integrity of scientific activity.
Methodology. The paper uses the authors’ research results to illustrate the advantages of the MANOVA approach for cyclical data in biomedical and environmental research from an integrity perspective. An analysis of modern published works on the problem of using statistical processing of scientific research results was applied, and general scientific methods were used: abstract-logical, induction-deduction and comparison.
Scientific novelty. The appropriateness of using one or another analysis to address situations in biomedical and environmental research involving statistical comparison of more than two groups, and often using data obtained through the application of complex experimental designs, is considered. Specific examples show how MANOVA improves and reduces the number of false positives, increases statistical efficiency, and allows for the identification of interactions between independent and dependent variables, providing a complete picture of the system under study, a full interpretation of the results, and the formulation of well-founded conclusions. Academic integrity issues related to the use of MANOVA are outlined: the validity of the post hoc procedure – descriptive discriminant analysis (DDA) and the lack of reporting on the statistical software used to analyze quantitative data.
Conclusions. Multivariate analysis of variance improves the integrity of life-science and environmental research by testing multiple dependent variables simultaneously, allowing for control of the rate of false positive errors in an experiment. It is statistically more efficient than performing multiple separate analyses. MANOVA also allows for the detection of interactions between factors, dependent and independent variables, better capturing the complexity of the organization of biological and ecological systems. This leads to more complete and reliable results, especially when working with multiple related biomedical or ecological measurements.
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