À propos de ce cours : 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.
Créé par : Université du Michigan
Enseigné par : James M Lepkowski, Research Professor
Survey Research Center, Institute for Social Research
| Informations de base | Cours 4 de 7 dans Survey Data Collection and Analytics Specialization |
| Niveau | Beginner |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Module 1: Sampling as a research tool
10 vidéos, 2 lectures
- Vidéo: 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!
- Vidéo: 1.1 - Research Design and Sampling - Part 1
- Vidéo: 1.1 - Research Design and Sampling - Part 2
- Vidéo: 1.2 - Surveys and Sampling
- Vidéo: 1.3 - Why Sample At All? - Part 1
- Vidéo: 1.3 - Why Sample At All? - Part 2
- Vidéo: 1.4 - Why Might We Randomize, and How Might We Do It?
- Vidéo: 1.5 - What Happens When We Randomize?
- Vidéo: 1.6 - How Do We Evaluate How Good a Sample Is?
- Vidéo: 1.7 - What Kinds of Things Can We Sample?
SEMAINE 2
Mere randomization
7 vidéos, 1 lecture
- Vidéo: 2.1 - Simple Random Sampling (SRS)
- Vidéo: 2.2 - Mere Randomization: A Short History
- Vidéo: 2.3 - The SRS Sampling Distribution - Part 1
- Vidéo: 2.3 - The SRS Sampling Distribution - Part 2
- Vidéo: 2.4 - Sample Size
- Vidéo: 2.5 - Margin of Error
- Vidéo: 2.6 - Sample Size and Population Size
- Reading: Notice for Auditing Learners: Assignment Submission
Noté: Random Sample of Faculty
Noté: Week 2
SEMAINE 3
Saving money using cluster sampling
10 vidéos
- Vidéo: 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 1
- Vidéo: 3.1 - Simple Complex Sampling - Choosing Entire Clusters - Part 2
- Vidéo: 3.2 - Design Effects and Intraclass Correlation - Part 1
- Vidéo: 3.2 - Design Effects and Intraclass Correlation - Part 2
- Vidéo: 3.3 - Two-Stage Sampling
- Vidéo: 3.4 - Designing for Two-Stage Sampling - Part 1
- Vidéo: 3.4 - Designing for Two-Stage Sampling - Part 2
- Vidéo: 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 1
- Vidéo: 3.5 - Dealing With the Real World - Unequal Sized Clusters - Part 2
- Vidéo: 3.6 - Sampling Fraction
Noté: Sampling Schools
Noté: Week 3
SEMAINE 4
Using auxiliary data to be more efficient
6 vidéos
- Vidéo: 4.1 - Forming Groups
- Vidéo: 4.2 - Sampling Variance
- Vidéo: 4.3 - More On Grouping
- Vidéo: 4.4 - Allocate Sample
- Vidéo: 4.5 - Other Allocations
- Vidéo: 4.6 - Weights to Combine Across Strata
Noté: Week 4
SEMAINE 5
Simplified sampling
5 vidéos
- Vidéo: 5.1 - Systematic Selection
- Vidéo: 5.2 - Intervals With Fractions - Part 1
- Vidéo: 5.2 - Intervals With Fractions - Part 2
- Vidéo: 5.3 - List Order
- Vidéo: 5.4 - Uncertainty Estimation
Noté: Credit Card Transactions
SEMAINE 6
Pulling it all together
6 vidéos, 1 lecture
- Vidéo: 6.1 - Statistical Software for Sample Selection
- Vidéo: 6.2 - Stratified Multistage Sampling
- Vidéo: 6.3 - Weights for Over/Under Sampling
- Vidéo: 6.4 - Nonresponse & Noncoverage Weighting
- Vidéo: 6.5 - Sampling Networks: Multiplicity Weighting
- Vidéo: 6.6 - Non-Probability Sampling
- Reading: Post-course Survey
Noté: Week 6 - Final Quiz
FAQ
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Coursework
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Créateurs
Université du Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Tarification
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Notation et examens
Great course
Excellent tutor. Easy to follow and useful coursework. Thanks!
Great course and excellent teaching.
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GP
A brilliant course for everybody interested in surveys and polls based on samples - whether as a producer of data who has to draw samples him- or herself or as a consumer of statistics wondering how scientists can make statements about a population by asking or examining just a few.