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Regular version of the site
Bachelor 2019/2020

Sampling People, Networks and Records

Area of studies: Fundamental and Applied Linguistics
Delivered by: School of Linguistics
When: 3 year, 3 module
Mode of studies: distance learning
Language: English
ECTS credits: 3
Contact hours: 2

Course Syllabus

Abstract

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. Instructor - James M Lepkowski, Research Professor, Survey Research Center, Institute for Social Research, the University of Michigan. https://www.coursera.org/learn/sampling-methods
Learning Objectives

Learning Objectives

  • 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.
Expected Learning Outcomes

Expected Learning Outcomes

  • Knows about simple random sampling, cluster sampling, stratification, systematic selection, and stratified multistage samples.
  • Knows how to estimate and summarize the uncertainty of randomized sampling.
Course Contents

Course Contents

  • Sampling People, Networks and Records
    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.
Assessment Elements

Assessment Elements

  • non-blocking Online course
  • non-blocking Discussion with a HSE instructor
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * Discussion with a HSE instructor + 0.7 * Online course
Bibliography

Bibliography

Recommended Core Bibliography

  • Rebecca Killick. (2016). Introductory Statistics and Analytics: A Resampling Perspective. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.69FBF820

Recommended Additional Bibliography

  • James J. Heckman. (1977). Sample Selection Bias As a Specification Error (with an Application to the Estimation of Labor Supply Functions). NBER Working Papers. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.p.nbr.nberwo.0172