Adjunct Research Professor
Design Strategies for Maximizing Total Data Quality
What You'll Learn
- Learn about design tools and techniques for maximizing TDQ.
- Identify aspects of the data generating/gathering process that impact TDQ.
- Understand TDQ maximization strategies that can be applied when gathering designed and found/organic data.
4 Modules
8 Hours
2 hrs per module (approx.)
Rating
About Design Strategies for Maximizing Total Data Quality
By the end of this third course in the Total Data Quality Specialization, learners will be able to:
1. Learn about design tools and techniques for maximizing TDQ across all stages of the TDQ framework during a data collection or a data gathering process.
2. Identify aspects of the data generating or data gathering process that impact TDQ and be able to assess whether and how such aspects can be measured.
3. Understand TDQ maximization strategies that can be applied when gathering designed and found/organic data.
4. Develop solutions to hypothetical design problems arising during the process of data collection or data gathering and processing.
This specialization as a whole aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality.
This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis.
Skills You'll Gain
- Data Classification
- Data Quality
- Data Quality Assessment
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Design Strategies for Maximizing Total Data Quality, a course focused on methods and approaches to maximize the quality of data before and during analysis. You will explore practical strategies for improving data validity, processing, access, and analysis quality, using real case studies and worked examples. By the end of this course, which is part of the Total Data Quality series, you will have the tools to ensure your research is based on the highest-quality data.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Maximizing Validity and Data Origin Quality
- Video: Welcome to Course 3 and the final course in the Specialization!
- Reading: Course Syllabus
- Reading: Course Pre-Survey
- Video: Maximizing Validity for Designed Data
- Video: Case Study: Improving Questions Based on Pre-Testing Results
- Video: Maximizing Validity for Gathered Data
- Reading: Case Study pre-read: Improving Google Flu Trends Estimates for the United States through Transformation
- Video: Case Study: Improving the Validity of Gathered Data using Auxiliary Data and Transformations
- Video: Maximizing Data Origin Quality for Designed Data
- Video: Case Study: Standardized vs. Conversational Interviewing
- Video: Maximizing Data Original Quality for Gathered Data
- Reading: Optional: links from previous lecture on Maximizing Data Original Quality for Gathered Data
- Video: Case Study: Simple Lessons Learned for Improving Data Origin Quality While Web Scraping
Module 2: Maximizing Processing and Data Access Quality
- Video: Maximizing Processing Quality for Designed Data
- Video: Example: Double Data Entry and Imputation to Maximize Data Processing Quality
- Video: Maximizing Processing Quality for Gathered Data
- Reading: Files for the next example
- Video: Example: Maximizing Processing Quality for Gathered Data
- Video: Maximizing Data Access Quality for Designed Data
- Reading: Exploring and Evaluating Enhancements for ABS Sampling Frames
- Video: Maximizing Data Access Quality for Gathered Data
- Video: Example: Maximizing Data Access Quality for Gathered Data
Module 3: Maximizing Data Source Quality and Minimizing Data Missingness
- Video: Maximizing Data Source Quality for Designed Data
- Video: Example: Maximizing Data Source Quality for Designed Data
- Video: Maximizing Data Source Quality for Gathered Data
- Reading: Probability Samples of Twitter
- Video: Minimizing Data Missingness for Designed Data
- Reading: Files for next example
- Video: Example: Imputation and Weighting Adjustment
- Video: Minimizing Data Missingness for Designed Data: Responsive and Adaptive Survey Design
- Reading: Optional: .csv and .py files for the next lecture
- Video: Minimizing Data Missingness for Gathered Data
- Video: Example: Minimizing Data Missingness for Gathered Data
Module 4: Maximizing the Quality of Data Analysis
- Video: Maximizing the Quality of an Analysis of Designed Data
- Video: Case Studies in Analytic Error
- Video: Maximizing the Quality of an Analysis of Gathered Data
- Video: Case Study: Maximizing the Quality of an Analysis of Video Image Data
- Reading: Course and Specialization Conclusion
- Reading: References for Design Strategies for Maximizing Total Data Quality
- Reading: Course and Specialization Post-Survey
Grading Policy
Course materials and assignments are self-paced. You must earn an overall grade of 80% to pass and receive a certificate. The course grade is based on seven quizzes (75% of overall grade) and an assignment worth 25%.
Research Assistant Professor
Research Professor
Research Associate Professor
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prior experience required
