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Nick Barter,
Christopher Stevenson,
Angela Victor,
Chris Rawson,
Jessica Cheung,
Georgie Lowe,
Matthew Clarke,
and
Michael Rose
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at anyone interested in research, but may be of particular interest to people who are undertaking a research project related to their profession or area of study.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at anyone interested in research, but may be of particular interest to people who are undertaking a research project related to their profession or area of study.
Topics Covered
Read more
Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at anyone interested in research, but may be of particular interest to people who are undertaking a research project related to their profession or area of study.
Topics Covered
- Why numbers matter in quantitative research
- How to use statistics to analyse data and solve real world problems
- How to formulate research questions, informed by accurate and reliable measurement
- How to select and justify an appropriate research method to answer your question
- The close relationship between quantitative and qualitative research
Register for this course and see more details by visiting:
OpenCourser.com/course/gktjga/why
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Reviews summary
Quantitative research: foundational yet theoretical
According to students, "Why Numbers Matter: Quantitative Research" provides a solid introduction and clear explanations for those new to the field, making complex topics accessible and easy-to-follow. Many appreciate its focus on formulating research questions and the importance of reliable measurement. However, some learners express that the course is too theoretical and lacks sufficient practical application, particularly regarding statistical software and hands-on data analysis. While it offers a strong conceptual foundation, more experienced individuals may find the content too basic, suggesting a better fit for beginners seeking an overview rather than in-depth skills.
Highly beneficial for beginners, potentially too simple for those with prior experience.
"It's a great starting point, though advanced learners might find it too basic."
"The course is extremely high-level and doesn't provide any actionable skills. I already knew most of what was taught from basic undergrad courses. It's too simplistic for anyone with a background in social sciences or data. Wasted my time."
"This course laid out the fundamentals clearly for someone new to research."
"I'd recommend this for absolute beginners, but not if you have any existing knowledge in the field."
Good coverage of research questions and measurement reliability.
"I liked the emphasis on formulating good research questions."
"It truly helps one understand the importance of reliable measurement."
"The connection between quantitative and qualitative research was particularly insightful for me."
Well-organized modules with clear explanations.
"The explanations were clear and the content was presented in a logical, easy-to-follow manner."
"The modules were well-structured, and the quizzes reinforced learning effectively."
"I found the course laid out the fundamentals very clearly."
Excellent for newcomers, clarifies core quantitative concepts.
"This course provided an excellent foundation in quantitative research. The explanations were clear and the content was presented in a logical, easy-to-follow manner."
"Fantastic course! It demystified quantitative research for me. The modules were well-structured, and the quizzes reinforced learning effectively."
"Excellent course for beginners! As someone new to research, this course laid out the fundamentals clearly."
"I feel much more confident now after taking this course, it really helped me understand the basics."
Emphasizes concepts, but less hands-on application of statistics.
"Some parts felt a bit rushed, especially the statistical software section, which could have used more hands-on practice or detailed walkthroughs."
"The course promised to teach 'how to use statistics to analyse data' but only scratched the surface. I felt it was too theoretical and lacked the practical application I was looking for."
"The conceptual parts were fine, but the practical application was weak. I was hoping for more hands-on work with data..."
"I found it to be a good theoretical foundation, but it was less practical than I had hoped."
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Reading list
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Provides a comprehensive overview of research design and quantitative methodology. It covers a wide range of topics, including research ethics, research methods, data collection, data analysis, and interpretation.
Is designed for students and professionals in business and management. It covers the basics of quantitative research and provides examples of how quantitative research can be used to solve business problems.
Focuses on the practical aspects of quantitative research design. It provides step-by-step instructions on how to design and conduct a quantitative research study.
This handbook provides a comprehensive overview of quantitative research methods. It covers a wide range of topics, including research design, data collection, data analysis, and interpretation.
Provides a comprehensive overview of quantitative research methods in communication. It covers topics such as research design, data collection, data analysis, and interpretation.
Classic text on quantitative research methods. It provides a clear and concise overview of the principles and methods of quantitative research.
Provides a comprehensive overview of quantitative research methods for the social sciences. It covers topics such as research design, data collection, data analysis, and interpretation.
Comprehensive introduction to statistical learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It good choice for students who want to learn the basics of statistical learning.
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Comprehensive introduction to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It good choice for students who want to learn the basics of machine learning.
Comprehensive introduction to Bayesian data analysis, covering topics such as Bayesian probability, Bayesian inference, and Bayesian modeling. It good choice for students who want to learn the basics of Bayesian statistics.
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Concise introduction to statistical inference, covering topics such as point estimation, hypothesis testing, and confidence intervals. It good choice for students who want to learn the basics of statistical inference.
True to its title, this book offers a straightforward and accessible introduction to statistical concepts and techniques. It's particularly useful for undergraduate students and those in fields outside of statistics who need to understand and interpret statistical results. Each chapter clearly explains a statistical technique, when to use it, how it works, and provides examples of how to write about the results.
Comprehensive introduction to statistics, covering topics such as data collection, analysis, and interpretation. It is well-written and provides plenty of examples and exercises.
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Comprehensive handbook of statistical techniques, covering topics such as data collection, analysis, and interpretation. It good choice for students who want to learn how to apply statistical techniques to real-world problems.
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Comprehensive introduction to biostatistics, covering topics such as data collection, analysis, and interpretation. It good choice for students who want to learn the basics of biostatistics for health science research.
Comprehensive introduction to causal inference, covering topics such as causal models, causal effects, and causal inference methods. It good choice for students who want to learn the basics of causal inference.
Popular introduction to statistics, covering topics such as data collection, analysis, and interpretation. It is written in a clear and concise style, making it accessible to readers of all levels.
Is an excellent starting point for anyone looking to gain a broad understanding of statistics without getting bogged down in complex formulas. It uses real-world examples and engaging prose to explain fundamental statistical concepts, making it ideal for high school students and undergraduates. It serves as valuable background reading to demystify statistics and build intuition before tackling more technical material.
Comprehensive introduction to statistics for psychologists, covering topics such as data collection, analysis, and interpretation. It is written in German and good choice for students who want to learn the basics of statistics in German.
Provides a comprehensive overview of basic statistical concepts, including data collection, analysis, and interpretation. It is written in a clear and concise style, making it accessible to readers of all levels.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gktjga/why
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Time
6 hours
Via
FutureLearn
Institutions
Deakin University
Griffith University
Instructors
Nick Barter
Christopher Stevenson
Angela Victor
Chris Rawson
Jessica Cheung
Georgie Lowe
Matthew Clarke
Michael Rose
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