Robert Seliga – författare

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Data Management and Digital Infrastructure in Social Sciences 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.Written 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.

Data Management and Digital Infrastructure in Social Sciences 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.Written 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.

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Quantitative, Qualitative, and Mixed Methodologies in Digital Social Sciences brings together quantitative analysis, qualitative inquiry, and mixed methods for the digital era. This volume shows how to design robust social science research using online surveys, platform data, digital traces, and computational tools, while keeping theory, context, and interpretation in view.Designed for doctoral students, early-career researchers, and applied researchers across sociology, psychology, education, public policy, communication, and management, it supports graduate methods teaching and real research projects in data-rich digital settings.

Data Management and Digital Infrastructure in Social Sciences 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.Written 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.