Course Summary
This course provides an overview of sampling methods in statistics, including simple random sampling, stratified sampling, cluster sampling, and more. Students will learn how to choose the appropriate sampling method for a given population and how to analyze data using the selected method.Key Learning Points
- Understand the different types of sampling methods and when to use them
- Learn how to choose the appropriate sampling method for a given population
- Learn how to analyze data using the selected sampling method
Related Topics for further study
Learning Outcomes
- Choose the appropriate sampling method for a given population
- Analyze data using the selected sampling method
- Design surveys and collect data using various sampling methods
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Familiarity with statistical software (such as R or SPSS)
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Quizzes and assignments
Similar Courses
- Applied Data Science with Python
- Probability and Statistics for Business and Data Science
- Data Science Methodology
Related Education Paths
Related Books
Description
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.
Outline
- Module 1: Sampling as a research tool
- 1.0 - Course Introduction
- 1.1 - Research Design and Sampling - Part 1
- 1.1 - Research Design and Sampling - Part 2
- 1.2 - Surveys and Sampling
- 1.3 - Why Sample At All? - Part 1
- 1.3 - Why Sample At All? - Part 2
- 1.4 - Why Might We Randomize, and How Might We Do It?
- 1.5 - What Happens When We Randomize?
- 1.6 - How Do We Evaluate How Good a Sample Is?
- 1.7 - What Kinds of Things Can We Sample?
- Help us learn more about you!
- How to get your questions answered by the instructor in the discussion forums!
- Mere randomization
- 2.1 - Simple Random Sampling (SRS)
- 2.2 - Mere Randomization: A Short History
- 2.3 - The SRS Sampling Distribution - Part 1
- 2.3 - The SRS Sampling Distribution - Part 2
- 2.4 - Sample Size
- 2.5 - Margin of Error
- 2.6 - Sample Size and Population Size
- Notice for Auditing Learners: Assignment Submission
- Week 2
- Saving money using cluster sampling
- 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 1
- 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 2
- 3.2 - Design Effects and Intraclass Correlation - Part 1
- 3.2 - Design Effects and Intraclass Correlation - Part 2
- 3.3 - Two-Stage Sampling
- 3.4 - Designing for Two-Stage Sampling - Part 1
- 3.4 - Designing for Two-Stage Sampling - Part 2
- 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 1
- 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 2
- 3.6 - Sampling Fraction
- Week 3
- Using auxiliary data to be more efficient
- 4.1 - Forming Groups
- 4.2 - Sampling Variance
- 4.3 - More On Grouping
- 4.4 - Allocate Sample
- 4.5 - Other Allocations
- 4.6 - Weights to Combine Across Strata
- Week 4
- Simplified sampling
- 5.1 - Systematic Selection
- 5.2 - Intervals With Fractions - Part 1
- 5.2 - Intervals With Fractions - Part 2
- 5.3 - List Order
- 5.4 - Uncertainty Estimation
- Pulling it all together
- 6.1 - Statistical Software for Sample Selection
- 6.2 - Stratified Multistage Sampling
- 6.3 - Weights for Over/Under Sampling
- 6.4 - Nonresponse & Noncoverage Weighting
- 6.5 - Sampling Networks: Multiplicity Weighting
- 6.6 - Non-Probability Sampling
- Post-course Survey
- Keep Learning with Michigan Online
- Week 6 - Final Quiz
Summary of User Reviews
The Sampling Methods course on Coursera has received positive reviews from many users. The course is comprehensive and well-structured, with engaging content and accessible explanations. One key aspect that users appreciated was the practical applications of the sampling methods covered in the course.Pros from User Reviews
- Comprehensive and well-structured course
- Engaging content
- Accessible explanations
- Practical applications of sampling methods covered
- Helpful quizzes and assignments
Cons from User Reviews
- Course pace may be too slow for some
- Limited interaction with course instructors
- Some users found the course difficult to follow
- Additional resources may be needed for deeper understanding
- Course may not be suitable for advanced learners
James M Lepkowski
4.4 Raiting
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