Sampling People, Networks and Records
- Offered byCoursera
Sampling People, Networks and Records at Coursera Overview
Duration | 20 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Sampling People, Networks and Records at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 4 of 7 in the Survey Data Collection and Analytics Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level
- Approx. 20 hours to complete
- English Subtitles: English
Sampling People, Networks and Records at Coursera Course details
- 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.
Sampling People, Networks and Records at Coursera Curriculum
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?
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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
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Week 6 - Final Quiz
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