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Frauke Kreuter, Ph.D.
and
Mariel Leonard
This course will provide you with an overview over existing data products and a good understanding of the data collection landscape. With the help of various examples you will learn how to identify which data sources likely matches your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. Furthermore this course will provide you with a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also help you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source. Finally we will introduce different large scale data collection efforts done by private industry and government agencies, and review the learned concepts through these examples. This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products.
Register for this course and see more details by visiting:
OpenCourser.com/course/r6rxo4/framework
What's inside
Syllabus
Research Designs and Data Sources
The first course in the specialization provides an overview of the topics to come. This module walks you through the process of data collection and analysis. Starting with a research question and a review of existing data sources, we cover survey data collection techniques, highlight the importance of data curation, and discuss some basic features that can affect your data analysis when dealing with sample data. Issues of data access and resources for access are introduced in this module.
Read more
Syllabus
Research Designs and Data Sources
The first course in the specialization provides an overview of the topics to come. This module walks you through the process of data collection and analysis. Starting with a research question and a review of existing data sources, we cover survey data collection techniques, highlight the importance of data curation, and discuss some basic features that can affect your data analysis when dealing with sample data. Issues of data access and resources for access are introduced in this module.
Read more
Measurements and Analysis Plan
In this module we will emphasize the importance of having a well-specified research question and analysis plan. We will provide an overview over the various data collection strategies, a variety of available modes for data collection and some thinking on how to choose the right mode.
Quality Framework
In this module you will be introduced to a general framework that allows you to not only understand each step required for a successful data collection and analysis, but also helps you to identify errors associated with different data sources. You will learn some metrics to quantify each potential error, and thus you will have tools at hand to describe the quality of a data source.
Application of TSE Framework to Existing Surveys
In this module we introduce a few surveys across a variety of topics. For each we highlight data collection features. The surveys span a variety of topics. We challenge you to think about alternative data sources that can be used to gather the same information or insights.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces learners to a range of data products and provides a solid understanding of the data collection ecosystem
Suitable for beginners as well as those familiar with specific data sources
Teaches learners how to translate research questions into measurable components
Provides a valuable framework for evaluating data quality and identifying errors
Emphasizes the importance of having a well-defined research question and analysis plan
Suitable for learners interested in understanding different data collection strategies and modes
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Save Framework for Data Collection and Analysis to your list so you can find it easily later:
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Reviews summary
Informative course
According to students, learners say that this course provides a lot of new and interesting information on the Total Survey Error Framework. Reviews indicate that the Total Survey Error framework is a useful way to learn about data collection methods. However, students also say that the course sometimes lacks examples that are simple and practical enough for everyone to understand.
Comes with new and interesting knowledge
"I have learned a lot of new and interesting things from this course."
Useful framework for data collection
"A good overview of surveys and data collection methods. The Total Survey Error framework is good, ..."
Lacks simple and practical examples
"The content of the course however lacks practical and simple examples that are easily understandable by everyone. "
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Framework for Data Collection and Analysis with these
activities:
Gather course materials
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Ensure you have all the necessary resources to fully engage with the course.
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Download and print lecture slides and assignments.
-
Acquire any textbooks or supplemental readings required for the course.
Review statistical concepts
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Familiarize yourself with the foundational principles of statistics before starting the course.
Browse courses on
Sampling Techniques
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Revisit notes or textbooks from previous statistics courses.
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Complete online quizzes or practice problems to test your understanding.
Explore data visualization tools
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Develop a strong foundation in data visualization techniques to enhance your comprehension of data analysis concepts.
Browse courses on
Data Visualization
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-
Enroll in online tutorials or workshops on data visualization.
-
Work through hands-on exercises to create visualizations from real-world datasets.
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Experiment with different data visualization tools to find ones that align with your interests and learning style.
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Reach out to classmates or fellow students to form a study group.
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Set regular meeting times and establish clear goals for each session.
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Work together to review course materials, solve problems, and prepare for assessments.
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Seek guidance and advice from experienced professionals to accelerate your learning.
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Attend industry events or conferences.
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Reach out to professionals on LinkedIn or other professional networking platforms.
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Request informational interviews to learn about career paths and industry trends.
Solve data analysis problems
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Deepen your understanding of data analysis techniques through consistent practice.
Browse courses on
Data Analysis
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-
Find online repositories or textbooks with data analysis exercises.
-
Allocate dedicated time for practicing data analysis problems.
-
Review your solutions and compare them with expert answers to identify areas for improvement.
Participate in data science hackathons
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Apply your knowledge in a competitive environment and learn from experienced professionals.
Browse courses on
Data Science
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-
Identify data science hackathons that align with your interests and skill level.
-
Team up with fellow students or professionals to form a team.
-
Work together to develop innovative solutions to real-world data challenges.
Gather course materials
Show steps
Ensure you have all the necessary resources to fully engage with the course.
Show steps
Show steps
Show steps
- Download and print lecture slides and assignments.
- Acquire any textbooks or supplemental readings required for the course.
Review statistical concepts
Show steps
Familiarize yourself with the foundational principles of statistics before starting the course.
Browse courses on
Sampling Techniques
Show steps
Show steps
Show steps
- Revisit notes or textbooks from previous statistics courses.
- Complete online quizzes or practice problems to test your understanding.
Explore data visualization tools
Show steps
Develop a strong foundation in data visualization techniques to enhance your comprehension of data analysis concepts.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Enroll in online tutorials or workshops on data visualization.
- Work through hands-on exercises to create visualizations from real-world datasets.
- Experiment with different data visualization tools to find ones that align with your interests and learning style.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group
Show steps
Connect with peers and foster a supportive learning environment.
Show steps
Show steps
Show steps
- Reach out to classmates or fellow students to form a study group.
- Set regular meeting times and establish clear goals for each session.
- Work together to review course materials, solve problems, and prepare for assessments.
Connect with experts in the field
Show steps
Seek guidance and advice from experienced professionals to accelerate your learning.
Show steps
Show steps
Show steps
- Attend industry events or conferences.
- Reach out to professionals on LinkedIn or other professional networking platforms.
- Request informational interviews to learn about career paths and industry trends.
Solve data analysis problems
Show steps
Deepen your understanding of data analysis techniques through consistent practice.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find online repositories or textbooks with data analysis exercises.
- Allocate dedicated time for practicing data analysis problems.
- Review your solutions and compare them with expert answers to identify areas for improvement.
Participate in data science hackathons
Show steps
Apply your knowledge in a competitive environment and learn from experienced professionals.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify data science hackathons that align with your interests and skill level.
- Team up with fellow students or professionals to form a team.
- Work together to develop innovative solutions to real-world data challenges.
Career center
Learners who complete Framework for Data Collection and Analysis will develop knowledge and skills
that may be useful to these careers:
Data Architect
Data Architect
A Data Architect is responsible for designing and implementing data systems. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Data Architect.
Data Scientist
Data Scientist
A Data Scientist is responsible for developing and implementing data-driven solutions to business problems. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for developing and implementing machine learning models to solve business problems. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Machine Learning Engineer.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Statistician.
Research Analyst
Research Analyst
A Research Analyst is responsible for conducting research on a variety of topics. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Research Analyst.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Product Manager.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data to make investment recommendations. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Financial Analyst.
Market Research Analyst
Market Research Analyst
A Market Research Analyst conducts research on consumer markets to determine the potential demand for a new product or service. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Market Research Analyst.
Operations Manager
Operations Manager
An Operations Manager is responsible for the day-to-day operations of a business. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as an Operations Manager.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for ensuring that customers are satisfied with their products and services. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Customer Success Manager.
Business Analyst
Business Analyst
A Business Analyst helps businesses to improve their performance by analyzing their data and processes. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Business Analyst.
Actuary
Actuary
An Actuary is responsible for assessing financial risk. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as an Actuary.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Data Analyst.
Sales Manager
Sales Manager
A Sales Manager is responsible for leading and motivating a team of salespeople. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Sales Manager.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns to promote products and services. This course can help prepare you for this role by providing you with a framework for data collection and analysis. You will learn how to identify which data sources are likely to match your research question, how to turn your research question into measurable pieces, and how to think about an analysis plan. This knowledge will be essential for you to be successful as a Marketing Manager.
For more career information including salaries, visit:
OpenCourser.com/course/r6rxo4/framework
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Framework for Data Collection and Analysis.
This classic textbook offers a comprehensive overview of statistical learning methods, covering a wide range of topics from data visualization and feature selection to supervised and unsupervised learning.
Offers a systematic and detailed overview of the principles and challenges of data collection and analysis, providing a solid theoretical foundation andpractical guidelines.
Provides a comprehensive overview of the data collection and analysis process, from research design to data interpretation. It valuable resource for students and researchers in the social sciences.
Is considered a classic in the field of research methods and provides an overview of the entire research process.
Save
Provides a comprehensive introduction to data analysis for students and researchers in the life sciences, covering essential statistical methods and their applications in R.
This introductory textbook on statistics assumes no prior knowledge and provides a clear and intuitive explanation of statistical concepts and methods.
Save
Provides a detailed guide to the data collection and analysis process, including tips and examples.
Provides a comprehensive overview of statistical methods and techniques, including both descriptive and inferential statistics.
Provides a practical guide to research methods and techniques for social workers.
Save
Provides a practical guide to research methods and techniques for social work students and practitioners.
Provides a basic overview of research methods and techniques for practitioners in the field of social work.
Provides a concise overview of research methods and techniques.
Provides a critical overview of research methods and techniques with a focus on social justice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/r6rxo4/framework
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Effort
4 weeks of study, 1-2 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Maryland, College Park
Instructors
Frauke Kreuter, Ph.D.
Mariel Leonard
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Survey Data Collection and Analytics . It's comprised of seven courses:
- Dealing With Missing Data
- Framework for Data Collection and Analysis (viewing)
- Combining and Analyzing Complex Data
- Questionnaire Design for Social Surveys
- Sampling People, Networks and Records
- Survey Data Collection and Analytics Project (Capstone)
- Data Collection: Online, Telephone and Face-to-face
Good to know
Introduces learners to a range of data products and provides a solid understanding of the data collection ecosystem
Suitable for beginners as well as those familiar with specific data sources
Teaches learners how to translate research questions into measurable components
Provides a valuable framework for evaluating data quality and identifying errors
Emphasizes the importance of having a well-defined research question and analysis plan
Suitable for learners interested in understanding different data collection strategies and modes
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