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Nick Taylor,
Mel Woods,
Andy Cobley,
Laura Underwood,
Gerid Hager,
Dr Saskia Coulson,
Inian Moorthy,
Linda See,
Dilek Fraisl,
Margaret Gold,
Joan Masó,
Valantis Tsiakos,
Uta Wehn,
and
Ester Prat
Topics Covered
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Topics Covered
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Topics Covered
- Understanding the issue or problem: exploring environmental issues and deciding on a research focus.
- Creating a community: finding the people who are brought together by a shared concern and positively nurturing the sharing of ideas and experiences.
- Deciding what data to collect: using the research question(s) to select what information will be gathered.
- Capturing or generating the data: collecting the information, keeping motivated and engaged.
- Analysing the data: interpreting the data, being able to spot trends and anomalies.
- Disseminating results: using the findings from the data to communicate with others about the environmental concern.
- Change-making / planning action: using the findings to lobby for change, or plan an intervention or action to inform others about the environmental concern.
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Explores the innovative aspects of Citizen Science, focusing on its impact on open science, society, and policy.
Provides a practical guide to using Citizen Science for environmental management, covering topics such as data collection, analysis, and interpretation.
This report by the National Research Council provides a comprehensive overview of public participation in scientific research, including Citizen Science.
This handbook provides practical guidance for researchers and practitioners on how to design, implement, and evaluate Citizen Science projects.
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This comprehensive textbook provides a detailed overview of the principles and practices of data collection in the social sciences, covering topics such as research design, sampling, data quality, and ethical considerations. It offers a strong foundation for understanding the entire data collection process.
This practical guide to data collection methods helps researchers design and implement effective studies, encompassing a wide range of techniques, including surveys, interviews, observations, and archival research. It provides clear and detailed instructions for each method and emphasizes the importance of reliability and validity.
Presents a comprehensive overview of survey research methods, including sampling design, questionnaire construction, data collection modes, and analysis techniques. It provides insights into the challenges and complexities of survey research and offers practical guidance for conducting successful surveys.
This textbook offers a comprehensive introduction to survey research, covering the entire process from research design to data analysis. It provides theoretical foundations, practical guidance, and real-world examples, helping students understand the strengths and limitations of survey methods.
This introductory text provides a clear and accessible overview of data collection and analysis principles. It covers fundamental concepts such as data types, sampling techniques, data cleaning, and statistical analysis, making it suitable for beginners in data science and related fields.
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Focuses on qualitative interviewing techniques, providing a step-by-step guide to conducting effective interviews. It covers ethical considerations, interview design, data analysis, and common challenges researchers may encounter. It offers a valuable resource for those interested in qualitative data collection methods.
This comprehensive encyclopedia provides an extensive overview of social science research methods, including data collection techniques such as surveys, interviews, observations, and experiments. It covers both quantitative and qualitative approaches and serves as a valuable reference for researchers seeking a deeper understanding of various data collection methodologies.
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Explores big data analytics techniques and their applications across various fields. It covers data collection methods for handling large and complex datasets, as well as advanced analytics methodologies such as machine learning and predictive modeling. Researchers interested in leveraging big data for insights and decision-making will find this book valuable.
Provides an introduction to multivariate analysis methods, such as principal component analysis, cluster analysis, and discriminant analysis, using the R programming language. It explains the underlying concepts and provides practical examples and exercises, making it suitable for researchers interested in applying multivariate techniques to their data.
Offers a beginner-friendly introduction to data science, including data collection techniques. It provides a broad overview of data analysis concepts and machine learning algorithms, catering to individuals with little or no prior knowledge in these areas.
Focuses on longitudinal data analysis, which involves collecting and analyzing data over time. It covers techniques such as generalized linear mixed models and structural equation modeling, providing a valuable resource for researchers working with longitudinal datasets.
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An excellent overview of Bayesian statistics, this book provides a comprehensive introduction to the theory and practice of Bayesian data analysis. The focus on practical applications and real-life examples makes it a great choice for students and practitioners alike.
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A classic text in the field of data mining, this book provides a comprehensive overview of techniques and algorithms used for extracting knowledge from large datasets. Written by leading experts in the field, it valuable resource for students and researchers.
A hands-on guide to data analysis using Python, this book covers a wide range of topics, including data cleaning, transformation, visualization, and modeling. Written by the creator of Pandas, it practical resource for students and professionals in various fields.
An authoritative text on statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
This online book provides a comprehensive overview of machine learning concepts and techniques. Written by a leading expert in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
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Time
12 hours
Via
FutureLearn
Institutions
University of Dundee
WeObserve
Instructors
Nick Taylor
Mel Woods
Andy Cobley
Laura Underwood
Gerid Hager
Dr Saskia Coulson
Inian Moorthy
Linda See
Dilek Fraisl
Margaret Gold
Joan Masó
Valantis Tsiakos
Uta Wehn
Ester Prat
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