Advanced Research Methodology: Ethics, Statistical Models and Artificial Intelligence is a comprehensive reference designed to introduce learners to the principles, practices, and emerging approaches of contemporary research. The book combines foundational research methodology with statistical theory, experimental design, participatory approaches, advanced analytical models, and artificial intelligence, providing a broad framework for conducting systematic, ethical, and evidence-based research.

The opening chapters explain the fundamentals of research and research methodology, followed by a detailed discussion of research ethics and the responsible conduct of research. These chapters emphasize the importance of integrity, transparency, accountability, originality, and ethical decision-making throughout the research process. The book also introduces scientific sampling theory and experimental design, including approaches for developing reliable studies and examining causal relationships.

Participatory Learning and Action Research is covered to highlight collaborative, community-oriented, and problem-solving approaches to research. The subsequent chapters focus on advanced analytical techniques, including cluster analysis and segmentation models, supervised learning and classification models, Bayesian inference, and Bayesian research thinking. These methods help researchers understand complex data, identify patterns, classify observations, and make informed decisions under uncertainty.

The book further explores time-series and dynamic research models, along with survival analysis and event-history research, offering insights into the analysis of temporal processes, changing conditions, event occurrence, and duration-related data. The concluding chapter introduces artificial intelligence models in advanced research methodology, reflecting the growing significance of machine learning, computational analysis, and intelligent research tools in modern academic and applied research.

By integrating conceptual foundations with advanced quantitative and computational approaches, this book is useful for postgraduate students, research scholars, faculty members, social scientists, agricultural scientists, management researchers, and professionals seeking to strengthen their research capabilities. It can serve as a valuable resource for coursework, research training, doctoral preparation, and the application of modern analytical methods across diverse disciplines.

Prof. (Dr.) Sankar Kr. Acharya, former Dean of Post Graduate Studies, former Director of Extension Education, and former Head of the Department of Agricultural Extension at Bidhan Chandra Krishi Viswavidyalaya (BCKV), Mohanpur, West Bengal, has served in teaching, research, and extension for over 37 years since joining BCKV as an Assistant Professor in 1988. Born on 6 October 1960, he is internationally recognized for his pioneering contributions to Social Entropy and Energy Metabolism, Social Ecology and Environmental Sociology, Enterprise Ecology Framework, and Conservation Stewardship. With more than 261 research papers, 132 authored books, 1,130 citations, an h-index of 13, i10-index of 14, and a ResearchGate Research Interest score of 2609, he has delivered 51 keynote addresses, chaired 57 national and international scientific sessions, supervised 23 Ph.D. and 97 M.Sc. scholars, and served as Co-PI in several ICAR, NAHEP, and World Bank-funded projects. He has been an expert member of the West Bengal State Agriculture Commission, WWF’s Buxa Tiger Reserve Project, DFID’s Primary Education Programme, ASRB (ICAR), and the Research Council of Vidyasagar University, while also serving as editor and reviewer for numerous national and international journals. A Fellow of several prestigious professional societies, including the West Bengal Academy of Science and Technology (2024), he has received numerous national and international honors such as the Dr. Daulat Singh Memorial Award, Distinguished Professor and Academician Award, Eminent Scientist Award, Teaching Excellence Award, and Best Academician Award (2026). He has convened major panels at the World Congress of the International Union of Anthropological and Ethnological Sciences (IUAES) in Manchester (2013) and New Delhi (2023), holds a German utility model patent and an Indian design patent, has undertaken academic missions to Italy, France, Germany, China, Sri Lanka, Vietnam, Bangladesh, and Malaysia, and most recently delivered a keynote address at the International Conference on Conservation Agriculture and Renewable Energy in Zurich, Switzerland (June 2025).

Dr. Sushovon Jana is an Assistant Professor in the Department of Applied Statistics at Maulana Abul Kalam Azad University of Technology (MAKAUT), West Bengal, where he has been serving since December 2021, after previously working as a Guest Faculty at the University of Kalyani. He earned his Ph.D. in Statistics from the University of Kalyani in 2020, following an M.Phil. (First Class First) from Utkal University and M.Sc. in Statistics from the University of Kalyani. His research focuses on Astrostatistics, Applied Multivariate Analysis, Statistical Inference, Machine Learning, and Uncertainty Analysis, with applications to astronomical and astrophysical data, including galaxies, exoplanets, globular clusters, stellar evolution, solar activity, and active galactic nuclei using advanced statistical methods such as clustering, classification, factor analysis, structural equation modeling, and change-point analysis. He has published extensively in reputed national and international peer-reviewed journals, including Springer publications, and has contributed to interdisciplinary research in health sciences and demographic studies. An active researcher and invited speaker, he regularly presents at national and international conferences and has delivered lectures on astrostatistics and uncertainty analysis. As a dedicated teacher and mentor, he has taught undergraduate and postgraduate courses in Applied Multivariate Analysis, Probability Theory, Astrostatistics, and Biostatistics while supervising student research projects. His academic achievements include the DST-PURSE Senior Research Fellowship, the Outstanding Paper Award at the 2nd Regional Science and Technology Congress (2017), and the Second Prize in Poster Presentation at the Ninth International Triennial Calcutta Symposium on Probability and Statistics. A Life Member of the International Astrostatistics Association, he is proficient in R, Python, C++, SPSS, MINITAB, EViews, and LaTeX, and continues to contribute to statistical science through research, teaching, academic service, and professional engagement.

Chapter 1. Introduction to Research and Research Methodology

Chapter 2. Research Ethics and Responsible Conduct of Research

Chapter 3. Scientific Sampling Theory

Chapter 4. Experimental Design and Causal Research Methodology

Chapter 5. Participatory Learning and Action Research

Chapter 6. Cluster Analysis and Segmentation Models

Chapter 7. Supervised Learning and Classification Models

Chapter 8. Bayesian Inference and Bayesian Research Thinking

Chapter 9. Time Series and Dynamic Research Models

Chapter 10. Survival Analysis and Event-History Research

Chapter 11. AI Models in Advanced Research Methodology