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.
Este curso faz parte do Programa de cursos integrados Survey Data Collection and Analytics
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Programa de cursos integrados Survey Data Collection and Analytics Universidade de MichiganInformações sobre o curso
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Programa - O que você aprenderá com este curso
Module 1: Sampling as a research tool
Mere randomization
Saving money using cluster sampling
Using auxiliary data to be more efficient
Avaliações
- 5 stars71,27%
- 4 stars19,14%
- 3 stars2,12%
- 2 stars1,06%
- 1 star6,38%
Principais avaliações do SAMPLING PEOPLE, NETWORKS AND RECORDS
Best course of the specialization. Great instructor.
Very useful course to get the foundation in understanding the sampling process
The MOST useful class among this survey specializations. Highly recommended.
This is a very good course and I especially liked the peer review assessement.
Sobre Programa de cursos integrados Survey Data Collection and Analytics
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