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- Autorius: Katarzyna Szczepanska-Woszczyna, Lukasz Sulkowski, Maria Rybaczewska
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 194
- ISBN-10: 1041339437
- ISBN-13: 9781041339434
- Formatas: 17.4 x 24.6 x cm, kieti viršeliai
- Kalba: Anglų
Quantitative Methods of Research in Digital Landscape (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
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.
- Autorius: Katarzyna Szczepanska-Woszczyna, Lukasz Sulkowski, Maria Rybaczewska
- Leidėjas: Taylor & Francis Ltd
- Metai: 2026
- Puslapiai: 194
- ISBN-10: 1041339437
- ISBN-13: 9781041339434
- Formatas: 17.4 x 24.6 x cm, kieti viršeliai
- Kalba: Anglų
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.
Atsiliepimai