Quantitative Methods of Research in Digital Landscape equips social science and management researchers to design, run, and interpret statistical analyses for today’s data-rich research environment, from core descriptive statistics and hypothesis tests through to regression modeling and factor analysis, with a clear focus on transparency and responsible inference.
Written for PhD and Master’s students, advanced undergraduates, early-career researchers, and practitioners who work with social data in fields such as sociology, management, communication, education, public policy, and media studies, it supports methods training and applied research projects in the digital age.
Table of Contents:
Introduction, 1. Descriptive Statistics and Exploratory Data Analysis, 2. Fundamentals of Statistical Inference and Hypothesis Testing, 3. Comparing Group Means – t-Tests and ANOVA, 4. Correlation and Simple Linear Regression, 5. Multiple Linear Regression and Model Building, 6. Logistic Regression and Generalized Linear Models, 7. Non-Parametric and Robust Statistical Methods, 8. Exploratory Factor Analysis and Principal Component Analysis, 9. Confirmatory Factor Analysis and Structural Equation Modeling, 10. Conclusion, 11. References
About the Author :
Łukasz Sułkowski is a professor of economic sciences and humanities specializing in higher education management, social science methodology, HRM, and organizational culture, and serves as President of WSB University.
Marta Pikiewicz is an early-career researcher affiliated with WSB University, whose work focuses on sustainable development, organisational value creation, and management in contemporary organisations, with contributions to research on ESG practices in the IT sector.
Katarzyna Szczepańska-Woszczyna holds a postdoctoral degree in economic sciences and is a Vice-Rector for Research and Education at WSB University, specializing in management and quality sciences, with a focus on HRM, innovation, knowledge transfer, and sustainability
