Research Professor, Survey Methodology
Data Collection: Online, Telephone and Face-to-face
What You'll Learn
- Appricate and compare the pros and cons of self-administered modes and interviews
- Explore the emerging modes and data sources such as mobile web surveys and social media data
- Understand the key concepts about survey data collection methods
4 Modules
24 Hours
6 hrs per module (approx.)
Rating
About Data Collection: Online, Telephone and Face-to-face
This course presents research conducted to increase our understanding of how data collection decisions affect survey errors. This is not a “how–to-do-it” course on data collection, but instead reviews the literature on survey design decisions and data quality in order to sensitize learners to how alternative survey designs might impact the data obtained from those surveys.
The course reviews a range of survey data collection methods that are both interview-based (face-to-face and telephone) and self-administered (paper questionnaires that are mailed and those that are implemented online, i.e. as web surveys). Mixed mode designs are also covered as well as several hybrid modes for collecting sensitive information e.g., self-administering the sensitive questions in what is otherwise a face-to-face interview. The course also covers newer methods such as mobile web and SMS (text message) interviews, and examines alternative data sources such as social media. It concentrates on the impact these techniques have on the quality of survey data, including error from measurement, nonresponse, and coverage, and assesses the tradeoffs between these error sources when researchers choose a mode or survey design.
Skills You'll Gain
- Data Collection
- Online Surveys (Evaluation Methods)
- Sample Surveys
- Survey Sampling
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 Data Collection: Online, Telephone and Face-to-Face, an engaging course that explores how data collection choices influence survey errors and data quality. Learn about traditional methods like face-to-face and telephone surveys, self-administered approaches such as online and mailed questionnaires, mixed-mode designs, and innovative approaches including mobile and SMS surveys. Gain insight into trade-offs in survey design, error sources, and emerging data sources such as social media. This course is part of the Survey Data Collection and Analytics series.
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: Classic Modes of Data Collection
- Reading: Module 1 Overview
- Reading: Help us learn more about you!
- Reading: Module 1 Required Readings
- Video: 1.1 What this course is … and is not
- Reading: Module 1 Lecture Slides
- Video: 1.2.1 Introduction to Survey Errors
- Video: 1.2.2 Variable Error and Bias
- Video: 1.2.3 Total Survey Error
- Discussion Prompt: Survey Error
- Video: 1.3.1 What do we mean by “mode?”
- Video: 1.3.2 Mode Choice (by respondent)
- Video: 1.4.1 Mixed Mode Design
- Video: 1.4.2 Concurrent Mixed Mode
- Video: 1.4.3 Sequential (Follow-up) Mixed Mode
- Video: 1.4.4 Interview with David Weir (U. Michigan) on Mixed Mode Designs
- Video: 1.5.1 Response Rates
- Video: 1.5.2 Nonresponse Error
- Reading: Notice for Auditing Learners: Assignment Submission
Module 2: Self-Administration, Online Data Collection
- Reading: Module 2 Overview
- Reading: Module 2 Required Readings
- Video: 2.1.1 Modes (interviewer- and self-administered), CASI, ACASI
- Video: 2.1.2 ACASI continued
- Reading: Module 2 Lecture Slides
- Video: 2.2.1 Coverage
- Video: 2.2.2 Nonresponse
- Video: 2.2.3 Measurement
- Video: 2.3.1 Progress Indicators, Running Tallies
- Video: 2.3.2 Online Definitions
- Video: 2.3.3 Speeding Interventions
- Video: 2.4 Reg Baker (MRII) about web surveys in market research
- Discussion Prompt: Modes
Module 3: Interviewers and Interviewing
- Reading: Module 3 Overview
- Reading: Module 3 Required Readings
- Video: 3.1.1 Interviewer Roles, Obtaining Interviews
- Video: 3.1.2 Respondent selection, Within Household Sampling
- Video: 3.1.3 Proxy Responding
- Reading: Module 3 Lecture Slides
- Video: 3.2.1 Standardization Debate: Wording vs. Meaning
- Video: 3.2.2 Different approaches to standardized interviewing
- Discussion Prompt: Standardization Debate
- Video: 3.2.3 Personal vs. Formal Style, I-R Rapport
- Video: 3.3.1 Variance: Interviewer Behavior
- Video: 3.3.2 Bias: Interviewers’ Fixed Attributes
- Video: 3.4 Interview with Nora Cate Schaeffer (UW) about recruitment and interviewing
Module 4: Emerging Modes, New Data Sources
- Reading: Module 4 Overview
- Reading: Module 4 Required Readings
- Video: 4.1.1 New Modes, New Data
- Video: 4.1.2 Mobile Web Surveys
- Video: 4.1.3 Text Message Surveys
- Video: 4.1.4 Text vs. Voice Interviews
- Reading: Module 4 Lecture Slides
- Video: 4.2.1 Record linkage: statistical issues
- Video: 4.2.2 Record linkage: Techniques
- Video: 4.2.3 Record linkage: informed consent and ethical issues
- Video: 4.3.1 Uses of Big Data, Sensing Technology, Social Media Content as Data
- Video: 4.3.2 Social media applications: Measuring Mood and Depression
- Video: 4.3.3 Social Media and Population Estimates: Successes
- Video: 4.3.4 Why does social media content align with surveys data sometimes and not other times?
- Discussion Prompt: Social Media and Survey Methods
- Video: 4.4 Interview with Aigul Mavletova (National Research University Higher School of Economics, Moscow) on mobile web surveys
- Reading: Post-course Survey
Grading Policy
Learners are assessed on quizzes, programming assignments, and a final exam. The course grade is based on two quizzes worth 30% (15% each), two graded assignments worth 30% (15% each), and a final exam worth 40%.
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
