Research Professor
Sampling People, Networks and Records
What You'll Learn
- Understand the value and risks sampling and randomization methods bring to scientific investigation
- Differentiate different types of sampling methods and their applications
- Explain the principles and techniques of probability sampling methods
6 Modules
24 Hours
4 hrs per module (approx.)
Rating
About Sampling People, Networks and Records
Good data collection is built on good samples. But the samples can be chosen in many ways. Samples can be haphazard or convenient selections of persons, or records, or networks, or other units, but one questions the quality of such samples, especially what these selection methods mean for drawing good conclusions about a population after data collection and analysis is done. Samples can be more carefully selected based on a researcher’s judgment, but one then questions whether that judgment can be biased by personal factors. Samples can also be draw in statistically rigorous and careful ways, using random selection and control methods to provide sound representation and cost control. It is these last kinds of samples that will be discussed in this course. We will examine simple random sampling that can be used for sampling persons or records, cluster sampling that can be used to sample groups of persons or records or networks, stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. The course concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.
Skills You'll Gain
- Sample Surveys
- Stratified 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 Sampling People, Networks, and Records, a course focused on sampling strategies used in social science research. Learners examine real-world data collection challenges using people, institutional records, and network-based samples.
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: Sampling as a research tool
- Video: 1.0 - Course Introduction
- Reading: Help us learn more about you!
- Reading: How to get your questions answered by the instructor in the discussion forums!
- Video: 1.1 - Research Design and Sampling - Part 1
- Video: 1.1 - Research Design and Sampling - Part 2
- Video: 1.2 - Surveys and Sampling
- Video: 1.3 - Why Sample At All? - Part 1
- Video: 1.3 - Why Sample At All? - Part 2
- Video: 1.4 - Why Might We Randomize, and How Might We Do It?
- Video: 1.5 - What Happens When We Randomize?
- Video: 1.6 - How Do We Evaluate How Good a Sample Is?
- Video: 1.7 - What Kinds of Things Can We Sample?
Module 2: Mere randomization
- Video: 2.1 - Simple Random Sampling (SRS)
- Video: 2.2 - Mere Randomization: A Short History
- Video: 2.3 - The SRS Sampling Distribution - Part 1
- Video: 2.3 - The SRS Sampling Distribution - Part 2
- Video: 2.4 - Sample Size
- Video: 2.5 - Margin of Error
- Video: 2.6 - Sample Size and Population Size
- Reading: Notice for Auditing Learners: Assignment Submission
- Graded: Random Sample of Faculty
- Graded: Week 2
Module 3: Saving money using cluster sampling
- Video: 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 1
- Video: 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 2
- Video: 3.2 - Design Effects and Intraclass Correlation - Part 1
- Video: 3.2 - Design Effects and Intraclass Correlation - Part 2
- Video: 3.3 - Two-Stage Sampling
- Video: 3.4 - Designing for Two-Stage Sampling - Part 1
- Video: 3.4 - Designing for Two-Stage Sampling - Part 2
- Video: 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 1
- Video: 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 2
- Video: 3.6 - Sampling Fraction
- Graded: Sampling Schools
- Graded: Week 3
Module 4: Using auxiliary data to be more efficient
- Video: 4.1 - Forming Groups
- Video: 4.2 - Sampling Variance
- Video: 4.3 - More On Grouping
- Video: 4.4 - Allocate Sample
- Video: 4.5 - Other Allocations
- Video: 4.6 - Weights to Combine Across Strata
- Graded: Week 4
Module 5: Simplified sampling
- Video: 5.1 - Systematic Selection
- Video: 5.2 - Intervals With Fractions - Part 1
- Video: 5.2 - Intervals With Fractions - Part 2
- Video: 5.3 - List Order
- Video: 5.4 - Uncertainty Estimation
- Graded: Credit Card Transactions
Module 6: Pulling it all together
- Video: 6.1 - Statistical Software for Sample Selection
- Video: 6.2 - Stratified Multistage Sampling
- Video: 6.3 - Weights for Over/Under Sampling
- Video: 6.4 - Nonresponse & Noncoverage Weighting
- Video: 6.5 - Sampling Networks: Multiplicity Weighting
- Video: 6.6 - Non-Probability Sampling
- Graded: Week 6 - Final Quiz
Grading Policy
There are a number of quizzes and peer review assignments throughout the course, with each accounting for between 10%-20% of your final grade.
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
