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Sampling People, Networks and Records
  • Level Foundation
  • Duration 26 hours
  • Course by University of Michigan
  • Offered by

About

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.

Modules

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    • 1 Videos
    • 3 Readings
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1 Videos

  • 1.0 - Course Introduction

3 Readings

  • Help us learn more about you!
  • How to get your questions answered by the instructor in the discussion forums!
  • Lecture notes

1.1 Ever heard of the three R's of research design?

    • 2 Videos
    • 1 Readings
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2 Videos

  • 1.1 - Research Design and Sampling - Part 1
  • 1.1 - Research Design and Sampling - Part 2

1 Readings

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1.2 How do surveys and sampling fit together?

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1 Videos

  • 1.2 - Surveys and Sampling

1 Readings

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1.3 What does sampling have to do with research?

    • 2 Videos
    • 1 Readings
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2 Videos

  • 1.3 - Why Sample At All? - Part 1
  • 1.3 - Why Sample At All? - Part 2

1 Readings

  • Lecture notes for both videos

1.4 Why randomize?

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1 Videos

  • 1.4 - Why Might We Randomize, and How Might We Do It?

1 Readings

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1.5 What happens when we randomize?

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  • 1.5 - What Happens When We Randomize?

1 Readings

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1.6 How do we evaluate how good the sample is?

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1 Videos

  • 1.6 - How Do We Evaluate How Good a Sample Is?

1 Readings

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1.7 What kinds of things can we sample?

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1 Videos

  • 1.7 - What Kinds of Things Can We Sample?

1 Readings

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2.1 Simple Random Sampling

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1 Videos

  • 2.1 - Simple Random Sampling (SRS)

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2.2 A short history

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1 Videos

  • 2.2 - Mere Randomization: A Short History

1 Readings

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2.3 SRS sampling distributions

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    • 1 Readings
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2 Videos

  • 2.3 - The SRS Sampling Distribution - Part 1
  • 2.3 - The SRS Sampling Distribution - Part 2

1 Readings

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2.4 Sample size

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1 Videos

  • 2.4 - Sample Size

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2.5 Margin of error

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1 Videos

  • 2.5 - Margin of Error

1 Readings

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2.6 Sample and population size

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1 Videos

  • 2.6 - Sample Size and Population Size

1 Readings

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Week 2: Assessments

    • 1 Readings
    • 1 PeerReview
    • 1 Assignment
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1 Assignment

  • Week 2

1 Peer Review

  • Random Sample of Faculty

1 Readings

  • Notice for Auditing Learners: Assignment Submission

3.1 Simple complex sampling -- choosing entire clusters

    • 2 Videos
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2 Videos

  • 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 1
  • 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 2

1 Readings

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3.2 Design effects

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2 Videos

  • 3.2 - Design Effects and Intraclass Correlation - Part 1
  • 3.2 - Design Effects and Intraclass Correlation - Part 2

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3.3 Two-stage cluster sampling

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1 Videos

  • 3.3 - Two-Stage Sampling

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3.4 Designing 2-stage samples

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2 Videos

  • 3.4 - Designing for Two-Stage Sampling - Part 1
  • 3.4 - Designing for Two-Stage Sampling - Part 2

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3.5 Unequal sized clusters

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2 Videos

  • 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 1
  • 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 2

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3.6 Subsample size

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1 Videos

  • 3.6 - Sampling Fraction

1 Readings

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Week 3: Assessments

    • 1 PeerReview
    • 1 Assignment
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1 Assignment

  • Week 3

1 Peer Review

  • Sampling Schools

4.1 Using auxiliary data to be more efficient

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1 Videos

  • 4.1 - Forming Groups

1 Readings

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4.2 Sampling variances for stratified samples

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1 Videos

  • 4.2 - Sampling Variance

1 Readings

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4.3 More on grouping

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1 Videos

  • 4.3 - More On Grouping

1 Readings

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4.4 Allocation sample to strata: proportionate allocation

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  • 4.4 - Allocate Sample

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4.5 Other allocations

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  • 4.5 - Other Allocations

1 Readings

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4.6 Stratum or element weights?

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1 Videos

  • 4.6 - Weights to Combine Across Strata

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Week 4: Assessment

    • 1 Assignment
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1 Assignment

  • Week 4

5.1 Systematic selection -- how it's done

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1 Videos

  • 5.1 - Systematic Selection

1 Readings

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5.2 What happens if the “interval” is not an “interval?”

    • 2 Videos
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2 Videos

  • 5.2 - Intervals With Fractions - Part 1
  • 5.2 - Intervals With Fractions - Part 2

1 Readings

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5.3 Systematic selection and implicit stratification

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1 Videos

  • 5.3 - List Order

1 Readings

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5.4 How to estimate standard errors for systematic samples

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1 Videos

  • 5.4 - Uncertainty Estimation

1 Readings

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Week 5 Assessments

    • 1 PeerReview
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1 Peer Review

  • Credit Card Transactions

6.1 What about using statistical software to select samples?

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1 Videos

  • 6.1 - Statistical Software for Sample Selection

1 Readings

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6.2 Combining stratification and clusters

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1 Videos

  • 6.2 - Stratified Multistage Sampling

1 Readings

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6.3 Oversampling and weighting

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1 Videos

  • 6.3 - Weights for Over/Under Sampling

1 Readings

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6.4 Nonresponse and weighting

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1 Videos

  • 6.4 - Nonresponse & Noncoverage Weighting

1 Readings

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6.5 Network sampling

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1 Videos

  • 6.5 - Sampling Networks: Multiplicity Weighting

1 Readings

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6.6 Non-probability sampling

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1 Videos

  • 6.6 - Non-Probability Sampling

2 Readings

  • Lecture notes
  • Annotated Bibliography of a Few Sampling Books

Week 6 Assessment

    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Week 6 - Final Quiz

2 Readings

  • Post-course Survey
  • Keep Learning with Michigan Online

Auto Summary

"Sampling People, Networks and Records" is a foundational course in Personal Development offered by Coursera. This course, led by an expert instructor, delves into the art of selecting high-quality samples for data collection. It covers various sampling methods including simple random sampling, cluster sampling, stratification, and systematic selection. With a duration of 1560 minutes, it provides comprehensive insights into ensuring sound representation and cost control in research. Ideal for beginners, it also offers a brief overview of estimating and summarizing the uncertainty in randomized sampling. Available with a Starter subscription, this course is perfect for those looking to enhance their research skills.

James M Lepkowski
Instructor

James M Lepkowski