Sampling People, Networks and Records

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Sampling People, Networks and Records

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About this course: 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 a…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  University of Michigan
  • Taught by:  James M Lepkowski, Research Professor

    Survey Research Center, Institute for Social Research
Basic Info Course 4 of 7 in the Survey Data Collection and Analytics Specialization Level Beginner Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.4 stars Average User Rating 4.4See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Syllabus


WEEK 1


Module 1: Sampling as a research tool



10 videos, 1 reading expand


  1. Video: 1.0 - Course Introduction
  2. Reading: Help us learn more about you!
  3. Video: 1.1 - Research Design and Sampling - Part 1
  4. Video: 1.1 - Research Design and Sampling - Part 2
  5. Video: 1.2 - Surveys and Sampling
  6. Video: 1.3 - Why Sample At All? - Part 1
  7. Video: 1.3 - Why Sample At All? - Part 2
  8. Video: 1.4 - Why Might We Randomize, and How Might We Do It?
  9. Video: 1.5 - What Happens When We Randomize?
  10. Video: 1.6 - How Do We Evaluate How Good a Sample Is?
  11. Video: 1.7 - What Kinds of Things Can We Sample?


WEEK 2


Mere randomization



7 videos expand


  1. Video: 2.1 - Simple Random Sampling (SRS)
  2. Video: 2.2 - Mere Randomization: A Short History
  3. Video: 2.3 - The SRS Sampling Distribution - Part 1
  4. Video: 2.3 - The SRS Sampling Distribution - Part 2
  5. Video: 2.4 - Sample Size
  6. Video: 2.5 - Margin of Error
  7. Video: 2.6 - Sample Size and Population Size

Graded: Random Sample of Faculty
Graded: Week 2

WEEK 3


Saving money using cluster sampling



10 videos expand


  1. Video: 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 1
  2. Video: 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 2
  3. Video: 3.2 - Design Effects and Intraclass Correlation - Part 1
  4. Video: 3.2 - Design Effects and Intraclass Correlation - Part 2
  5. Video: 3.3 - Two-Stage Sampling
  6. Video: 3.4 - Designing for Two-Stage Sampling - Part 1
  7. Video: 3.4 - Designing for Two-Stage Sampling - Part 2
  8. Video: 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 1
  9. Video: 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 2
  10. Video: 3.6 - Sampling Fraction

Graded: Sampling Schools
Graded: Week 3

WEEK 4


Using auxiliary data to be more efficient



6 videos expand


  1. Video: 4.1 - Forming Groups
  2. Video: 4.2 - Sampling Variance
  3. Video: 4.3 - On Grouping
  4. Video: 4.4 - Allocate Sample
  5. Video: 4.5 - Other Allocations
  6. Video: 4.6 - Weights to Combine Across Strata

Graded: Week 4

WEEK 5


Simplified sampling



5 videos expand


  1. Video: 5.1 - Systematic Selection
  2. Video: 5.2 - Intervals With Fractions - Part 1
  3. Video: 5.2 - Intervals With Fractions - Part 2
  4. Video: 5.3 - List Order
  5. Video: 5.4 - Uncertainty Estimation

Graded: Credit Card Transactions

WEEK 6


Pulling it all together



6 videos, 1 reading expand


  1. Video: 6.1 - Statistical Software for Sample Selection
  2. Video: 6.2 - Stratified Multistage Sampling
  3. Video: 6.3 - Weights for Over/Under Sampling
  4. Video: 6.4 - Nonresponse & Noncoverage Weighting
  5. Video: 6.5 - Sampling Networks: Multiplicity Weighting
  6. Video: 6.6 - Non-Probability Sampling
  7. Reading: Post-course Survey

Graded: Week 6 - Final Quiz
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