Course Summary
This course on Coursera teaches students the basics of quantitative research methods, including statistical analysis, measurement, and experimental design.Key Learning Points
- Learn how to analyze data using statistical methods.
- Understand the basics of measurement and experimental design.
- Gain hands-on experience with real-world data sets.
- Develop critical thinking skills in research methodology.
- Explore different research methods for answering complex questions.
Related Topics for further study
Learning Outcomes
- Understand the basics of quantitative research methods.
- Be able to analyze data using statistical methods.
- Develop critical thinking skills in research methodology.
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics.
- Familiarity with Excel or other spreadsheet software.
Course Difficulty Level
IntermediateCourse Format
- Online and Self-paced
- Video Lectures
- Hands-on Exercises
- Quizzes and Assignments
Similar Courses
- Statistics with R
- Research Methods for Social Sciences
- Data Analysis and Visualization
Related Education Paths
Notable People in This Field
- Statistician and Founder of FiveThirtyEight
- Professor of Statistics and Political Science at Columbia University
Related Books
Description
Discover the principles of solid scientific methods in the behavioral and social sciences. Join us and learn to separate sloppy science from solid research!
Outline
- Before we get started...
- Undecided? See why you should join!
- Welcome to quantitative methods!
- Hi there!
- How to navigate this course
- How to contribute
- General info - What will I learn in this course?
- Course format - How is this course structured?
- Requirements - What resources do I need?
- Grading - How do I pass this course?
- Contact - How do I stay informed?
- Team - Who created this course?
- Origins of the scientific method
- 1.01 Non-scientific Methods
- 1.02 Scientific Method
- 1.03 Scientific Claims
- 1.04 Classical Period
- 1.05 Enlightenment
- 1.06 Modern Science
- 1.07 Epistemology
- 1.08 Ontology
- 1.09 Approaches
- 1.10 Goals
- Origins - Interview - Gerben Moerman (Part 1 of 4)
- Origins - Interview - Gerben Moerman (Part 2 of 4)
- Origins - Interview - Gerben Moerman (Part 3 of 4)
- Origins - Interview - Gerben Moerman (Part 4 of 4)
- What makes knowledge scientific?
- What are the essential qualities of a systematic method?
- What's the difference between a hypothesis and a theory?
- Who developed the scientific method and when?
- What is your philosophy of science?
- Do you prefer your science hard or soft?
- Honor Code - Integrity in this course
- Transcripts: Origins
- About the interview
- Origins
- The Scientific Method
- 2.01 Empirical Cycle
- 2.02 (Dis)confirmation
- 2.03 Criteria
- 2.04 Causality
- 2.05 Internal Validity Threats: Participants
- 2.06 Internal Validity Threats: Instruments
- 2.07 Internal Validity Threats: Artificiality
- 2.08 Internal Validity Threats: Research setup
- 2.09 Variables of Interest
- 2.10 Variables of Disinterest
- Scientific Method - Interview - Marjan Bakker (Part 1 of 3)
- Scientific Method - Interview - Marjan Bakker (Part 2 of 3)
- Scientific Method - Interview - Marjan Bakker (Part 3 of 3)
- What would be your 'recipe' for the scientific method?
- What will it take for you to accept a hypothesis?
- What do you look for in a good research study?
- How do you identify what caused an effect?
- What makes a causal explanation less likely?
- What different relations and roles can variables have?
- Transcripts: Scientific Method
- About the interview
- Informed Consent Form
- Scientific Method
- Research Designs
- 3.01 True Experiments
- 3.02 Factorial Designs
- 3.03 Repeated Measures
- 3.04 Manipulation
- 3.05 Lab vs. Field
- 3.06 Randomization
- 3.07 Experimental Designs
- 3.08 Matching
- 3.09 Quasi-Experimental Designs
- 3.10 Correlational Designs
- 3.11 Other Designs
- Research Designs - Interview - Maarten Bos (Part 1 of 4)
- Research Designs - Interview - Maarten Bos (Part 2 of 4)
- Research Designs - Interview - Maarten Bos (Part 3 of 4)
- Research Designs - Interview - Maarten Bos (Part 4 of 4)
- What are the essential features of a true experiment?
- What are other ways of comparing?
- How do manipulation and control work (in the lab vs the field)?
- What experimental designs can you think of?
- What if you cannot assign randomly?
- What if you can't manipulate either?
- Transcripts: Research Designs
- About the interview
- Research Designs
- Measurement
- 4.01 Operationalization
- 4.02 Measurement Structure
- 4.03 Measurement Levels
- 4.04 Variable Types
- 4.05 Measurement Validity
- 4.06 Measurement Reliability
- 4.07 Survey, Questionnaire, Test
- 4.08 Scales and Response Options
- 4.09 Response and Rater Bias
- 4.10 Other Measurement Types
- Measurement - Interview - Andries van der Ark (Part 1 of 4)
- Measurement - Interview - Andries van der Ark (Part 2 of 4)
- Measurement - Interview - Andries van der Ark (Part 3 of 4)
- Measurement - Interview - Andries van der Ark (Part 4 of 4)
- How do you measure something?
- What is measurement exactly?
- How do you know whether you have used the right instrument?
- How are measures constructed and what are their features?
- Transcripts: Measurement
- About the interview
- Measurement
- Sampling
- 5.01 External Validity Threats
- 5.02 Sampling Concepts
- 5.03 Probability Sampling
- 5.04 Probability Sampling - Simple
- 5.05 Probability Sampling - Complex
- 5.06 Non-Probability Sampling
- 5.07 Sampling Error
- 5.08 Non-Sampling Error
- 5.09 Sample Size
- Sampling - Interview - Armén Hakhverdian (Part 1 of 4)
- Sampling - Interview - Armén Hakhverdian (Part 2 of 4)
- Sampling - Interview - Armén Hakhverdian (Part 3 of 4)
- Sampling - Interview - Armén Hakhverdian (Part 4 of 4)
- How are samples used for generalization?
- Why would you use probability sampling?
- Why would you use non-probability sampling?
- To what extent does a sample reflect the population?
- How large should your sample be?
- Transcripts: Sampling
- About the interview
- Sampling
- Practice, Ethics & Integrity
- 6.01 Documentation
- 6.02 Data Management
- 6.03 Unethical Studies
- 6.04 Ethics Towards Participants
- 6.05 Research Integrity
- 6.06 Questionable Research Practices
- 6.07 Peer Review Process
- 6.08 Dissemination Problems
- 6.extra Milgram's Obedience Study (see OPTIONAL assignment)
- Interview - Practice, Ethics & Integrity - Jelte Wicherts (Part 1 of 4)
- Interview - Practice, Ethics & Integrity - Jelte Wicherts (Part 2 of 4)
- Interview - Practice, Ethics & Integrity - Jelte Wicherts (Part 3 of 4)
- Interview - Practice, Ethics & Integrity - Jelte Wicherts (Part 4 of 4)
- How would you manage and store your data?
- How do we make sure participants are treated ethically?
- How do we make sure researchers behave ethically and with integrity?
- What about ethics in the publication process?
- Transcripts: Practice, Ethics & Integrity
- About the interview
- Practice, Ethics & Integrity
- Catch Up
- Screencast Practice Exam 2 - Questions 1-10
- Screencast Practice Exam 2 - Questions 11-20
- Screencast Practice Exam 2 - Questions 21-30
- Transcripts: All modules
- Practice Exam 1 - immediate feedback
- Practice Exam 2 - feedback in screencasts
- Exam Time!
- Bonus material - presentations on research integrity
- Final Exam
Summary of User Reviews
Discover how to apply quantitative methods in your research process by taking this course on Coursera. Students have rated this course highly for its comprehensive curriculum and engaging content.Key Aspect Users Liked About This Course
Many users found the course to be comprehensive and engaging.Pros from User Reviews
- The course covers a wide range of quantitative methods, providing a comprehensive understanding of the subject.
- The content is well-organized and easy to follow, making it accessible to learners of all levels.
- The instructor is knowledgeable and engaging, keeping students interested throughout the course.
- The course provides practical examples and exercises, allowing students to apply what they've learned in real-world scenarios.
Cons from User Reviews
- Some users found the course to be too basic, lacking in-depth analysis of certain topics.
- The course can be time-consuming, requiring a significant commitment from students.
- Some users found the course to be too theoretical, with limited practical applications.
- The course may not be suitable for those who are already familiar with quantitative methods.
- The course materials can be dense and difficult to understand for some learners.
Annemarie Zand Scholten
4.7 Raiting
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