Chevron Left
Volver a Sampling People, Networks and Records

Opiniones y comentarios de aprendices correspondientes a Sampling People, Networks and Records por parte de Universidad de Míchigan

4.5
estrellas
93 calificaciones
28 reseña

Acerca del Curso

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....

Principales reseñas

DK

12 de jun. de 2020

I was very impressed with the course content as well as the expert presentation. This course has empowered with relevant and practical sampling skills that I will apply in the my work

AM

9 de may. de 2020

I gained solid foundations of sampling techniques from this course. The instructor is excellent, and the course content is very comprehensive.

Filtrar por:

26 - 28 de 28 revisiones para Sampling People, Networks and Records

por Saurav J

30 de may. de 2021

good

por KESHAB K S

15 de mar. de 2021

Comprehensive Knowledge is provided in the course. Highly skilled faculty member.

por Quinn R

7 de nov. de 2020

Poorly delivered, disorganized, no clear explanations of mathematics needed, caters more to solely auditory learners (very few and mostly unhelpful visuals).