Applied Statistics and Machine Learning for Social Science Research

Abstract

The growing availability of large-scale datasets and advances in computational methods have transformed quantitative research across the social sciences. Statistical modelling and machine learning offer powerful approaches for examining complex socioeconomic relationships, identifying patterns, predicting outcomes, and supporting evidence-based decision-making. However, researchers and students often encounter challenges in selecting appropriate analytical methods, understanding their underlying assumptions, implementing statistical techniques, and interpreting results meaningfully. This book, Applied Statistics and Machine Learning for Social Science Research, provides a comprehensive yet accessible introduction to statistical analysis, advanced modelling techniques, and machine learning applications in social science research. Designed primarily for undergraduate and postgraduate students, early-career researchers, and development practitioners, the book integrates theoretical foundations with practical applications using R programming. The book begins with fundamental concepts of quantitative research, data collection, sampling, descriptive statistics, probability, and statistical inference. It subsequently introduces regression analysis, generalized linear models, multivariate statistical techniques, complex survey analysis, panel data methods, and causal inference. Building on these foundations, the book explores supervised and unsupervised machine learning algorithms, including decision trees, random forests, support vector machines, gradient boosting, principal component analysis, and clustering techniques. Particular emphasis is placed on model selection, diagnostic assessment, predictive performance, interpretability, and responsible applications of machine learning. Drawing on real-world datasets and research problems related to poverty, education, public health, employment, inequality, and socioeconomic development, with particular attention to Bangladesh, the book demonstrates how quantitative methods can be applied to address contemporary social science questions. Step-by-step computational examples, mathematical explanations, visualisations, and practical exercises are incorporated to strengthen analytical understanding and research skills. By bridging conventional statistical inference with modern machine learning approaches, this book aims to equip readers with the methodological knowledge and computational competencies necessary to conduct rigorous, reproducible, and policy-relevant quantitative research.