Maria Rybaczewska – författare

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 modelling and factor analysis, with a clear focus on transparency and responsible inference.Key features include:· A structured pathway from statistical foundations to advanced quantitative techniques used across digital social research.· Practical guidance on modelling and inference, including linear and logistic regression, multivariate methods, and factor analysis.· Coverage of common pitfalls in interpretation and reporting, including p-values, uncertainty, and claims that overreach the evidence.· An open science and reproducibility lens, with emphasis on clear documentation of data preparation and analytic choices.· A platform-neutral approach that supports readers working across software packages and coding environments.Written for PhD and Masters 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.

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 modelling and factor analysis, with a clear focus on transparency and responsible inference.Key features include:· A structured pathway from statistical foundations to advanced quantitative techniques used across digital social research.· Practical guidance on modelling and inference, including linear and logistic regression, multivariate methods, and factor analysis.· Coverage of common pitfalls in interpretation and reporting, including p-values, uncertainty, and claims that overreach the evidence.· An open science and reproducibility lens, with emphasis on clear documentation of data preparation and analytic choices.· A platform-neutral approach that supports readers working across software packages and coding environments.Written for PhD and Masters 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.

Data Sources, Management, and Digital Infrastructure in Social Sciences and Management shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.Key features include:· Strategies for working with diverse digital data sources, from social media and administrative records to geospatial and sensor data· Data governance and research ethics, including the FAIR principles, privacy, consent, and responsible reuse· Metadata and documentation practices that keep datasets interpretable over time, including codebooks and common standards· Clear introductions to data platforms and infrastructure, including repositories, data warehouses and lakes, APIs, and cloud or high-performance computing· Step-by-step approaches to data pipelines (ETL), quality assurance, provenance, version control, and open science data sharingWritten for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.