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Jennifer Bachner, PhD

This is the final course in the Data Literacy Specialization. In this capstone course, you'll apply the skills and knowledge you have acquired in the specialization to the critical evaluation of an original quantitative analysis. The project will first require you to identify and read a piece of high-quality, original, quantitative research on a topic of your choosing. You’ll then interpret and evaluate the findings as well as the methodological approach. As part of the project, you’ll also review other students’ submissions. By the end of the project, you should be empowered to be a critical consumer and user of quantitative research.

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What's inside

Syllabus

Capstone Project Overview
Welcome to the Capstone Course for the Data Literacy Specialization! The capstone project will allow you to apply the knowledge and skills you've acquired throughout the specialization to the evaluation of a published piece of scholarly work. In this first module, I'll present an overview of the project's purpose and components.
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Locating Quality Scholarship
In this module, I'll present information that will help you identify an article or report that you'll evaluate for the capstone project. We'll explore sources that frequently publish high-quality, original, quantitative research. We'll also discuss the difference between primary and secondary research.
Final Paper Submission
Welcome to the final module in the Data Literacy Specialization. In this module, you'll complete and submit your capstone project via the peer review system. You'll also review three submissions from other students in the specialization. I hope this specialization has given you a set of tools and skills that empower you to be a critical consumer and user of quantitative research.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Students will be able to evaluate the findings of a piece of high-quality quantitative research on a topic of their choice
Students will be able to apply the skills and knowledge acquired throughout the specialization to the evaluation of published scholarly work
Students will be able to identify an article or report that they'll evaluate for the capstone project with the help of resources that frequently publish original, quantitative research
Students will be able to differentiate between primary and secondary research
Students will be able to complete and submit capstone projects and review submissions from other students as part of peer review system

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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 Data Literacy Capstone – Evaluating Research with these activities:
Review Introduction to Quantitative Research
Refresh your knowledge of the key principles and methods of quantitative research to build a solid foundation for the course.
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  • Read introductory articles or textbook chapters on quantitative research methods
  • Review online tutorials or videos on basic quantitative research concepts
  • Complete practice questions or exercises on quantitative research techniques
Explore Peer-Reviewed Quantitative Research
Gain hands-on experience in finding and evaluating high-quality quantitative research articles to apply in your capstone project.
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  • Identify reputable journals and databases for quantitative research
  • Use search terms and filters to locate relevant articles
  • Review abstracts and introductions to assess article quality
  • Read selected articles in detail and evaluate their methodology, findings, and conclusions
Develop a Research Question and Hypothesis
Solidify your understanding of how to formulate a clear and testable research question and hypothesis for your capstone project.
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  • Review course materials on research question and hypothesis development
  • Identify a research topic of interest and explore related literature
  • Brainstorm potential research questions and refine them for clarity and specificity
  • Develop a hypothesis that is testable and supported by evidence
Four other activities
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Collaborate on Capstone Project Evaluation
Enhance your critical thinking and evaluation skills by reviewing and providing feedback on other students' capstone projects.
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  • Read and evaluate assigned capstone project submissions
  • Provide constructive feedback on the methodology, findings, and conclusions
  • Engage in discussions with peers to exchange perspectives and deepen understanding
Support Peer Learning
Contribute to a positive learning environment by assisting fellow students on discussion boards or in study groups.
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  • Participate in online forums or discussion boards
  • Provide helpful answers and explanations to student questions
  • Help clarify concepts or provide additional examples to aid understanding
Implement a Data Literacy Project
Apply your acquired knowledge and skills to a practical project that demonstrates your proficiency in data literacy.
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  • Choose a data-driven problem or issue to address
  • Collect and analyze relevant data using appropriate techniques
  • Develop and implement a data-driven solution or intervention
  • Document and share your project findings and outcomes
Connect with Experts
Seek guidance and support from experienced practitioners or researchers to enhance your learning and professional development.
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  • Identify potential mentors in your field of interest
  • Reach out to potential mentors and request mentorship opportunities
  • Establish regular communication and seek advice on your research, career, or other professional goals

Career center

Learners who complete Data Literacy Capstone – Evaluating Research will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use scientific methods to extract knowledge from data. They use their findings to develop new products and services. The skills learned in this course, such as evaluating research and using data to solve problems, would be very helpful in this role, which typically requires a master's or PhD degree.
Data Analyst
Data Analysts examine large amounts of data to uncover patterns and trends. They use their findings to help businesses make better decisions. The evaluation and interpretation skills developed in this course would be very helpful in this role, which often requires a master's degree.
Quantitative Researcher
Quantitative Researchers use statistical methods to analyze data. They use their findings to test hypotheses and draw conclusions. The skills learned in this course, such as evaluating research and understanding statistical methods, would be very helpful in this role, which typically requires a master's degree or PhD.
Research Analyst
Research Analysts gather and analyze data on a specific topic. They use their findings to write reports and make recommendations. The skills learned in this course, such as searching for rigorous research and critically evaluating research, would be very helpful in this role, which often requires a master's degree.
Statistician
Statisticians use data to solve problems. They use their findings to develop new methods and improve existing ones. The skills learned in this course, such as evaluating research and understanding statistical methods, would be very helpful in this role, which typically requires a master's or PhD degree.
Epidemiologist
Epidemiologists use data to study the causes of disease. They use their findings to develop new prevention and treatment strategies. The skills learned in this course, such as evaluating research and understanding data, would be very helpful in this role, which typically requires a master's or PhD degree.
Marketing Analyst
Marketing Analysts use data to help businesses understand their customers. They use their findings to develop new marketing campaigns and improve customer service. The skills learned in this course, such as evaluating research and understanding data, would be very helpful in this role.
Business Analyst
Business Analysts use data to help businesses make better decisions. They use their findings to develop new strategies and improve operations. The skills learned in this course, such as evaluating research and using data to solve problems, would be very helpful in this role, which typically requires a master's degree.
Health Economist
Health Economists use data to study the cost and effectiveness of health care. They use their findings to develop new policies and improve the efficiency of the health care system. The skills learned in this course, such as evaluating research and understanding data, would be very helpful in this role, which typically requires a master's or PhD degree.
Data Librarian
Data Librarians are responsible for managing and organizing data. They use their skills to help researchers and other users find and access the data they need. The skills learned in this course, such as evaluating research and managing data, would be helpful in this role.
Financial Analyst
Financial Analysts use data to help businesses make investment decisions. They use their findings to develop financial models and make recommendations. The skills learned in this course, such as evaluating research and understanding data, would be helpful in this role.
Product Manager
Product Managers are responsible for the development and marketing of new products. They use data to understand customer needs and develop products that meet those needs. The skills learned in this course, such as evaluating research and using data, would be very helpful in this role, which typically requires a bachelor's degree.
Data Journalist
Data Journalists use data to tell stories. They use their findings to inform the public about important issues. The skills learned in this course, such as evaluating research and understanding data, would be very helpful in this role.
Management Consultant
Management Consultants use data to help businesses improve their operations. They use their findings to develop new strategies and improve processes. The skills learned in this course, such as evaluating research and using data to solve problems, would be very helpful in this role, which typically requires a bachelor's degree.
Market Researcher
Market Researchers use data to understand customer behavior. They use their findings to develop new products and services. The skills learned in this course, such as evaluating research and understanding data, would be helpful in this role, which may require a bachelor's degree or higher.

Reading list

We've selected ten 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 Data Literacy Capstone – Evaluating Research.
This comprehensive handbook covers a broad range of quantitative research methods used in psychology. It serves as a reference tool for advanced researchers.
This widely used textbook for social science research methods. It offers a comprehensive introduction to statistical concepts and methods used in social science research.
Provides a detailed and comprehensive guide to using SPSS for data analysis, step by step. It assumes no prior knowledge of SPSS.
Comprehensive and up-to-date guide to regression analysis. It covers both the theoretical and practical aspects of regression analysis.
Comprehensive introduction to the Python programming language for data analysis. It covers the basics of Python and provides practical examples of using Python for data analysis and visualization.
Comprehensive introduction to the R programming language for data science. It covers the basics of R and provides practical examples of using R for data analysis and visualization.
This advanced textbook covers research methods used in psychology. It discusses research design, data collection, and data analysis in quantitative psychology research.
Classic guide to social research methods, covering both qualitative and quantitative methods. It provides a good overview of the research process, from problem formulation to data analysis and interpretation.
Gentle introduction to machine learning for beginners. It covers the basics of machine learning and provides practical examples of using machine learning for data analysis and prediction.

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