GESIS Training Courses
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Scientific Coordination

Dr.
Sabina Haveric
Tel: +49 (0221) 47694 - 166

Administrative Coordination

Laura Rüwe

Sampling, Weighting and Estimation

Lecturer(s):
Dr. Matthias Sand, Christian Bruch

Date: 26.11 - 27.11.2019 ics-file

Location: Cologne / Course language: Englisch

About the lecturer - Dr. Matthias Sand

About the lecturer - Christian Bruch

Course description

This course will cover three interrelated topics: methods of selecting complex samples, creation of analysis weights that reduce sampling variance and adjust for nonresponse, and the analysis of weighted data collected via complex samples.
The first day will start with an introduction to the framework for design based inference and some basic sampling designs will be introduced. Common features of sampling designs such as stratification, sampling of clusters and multi-stage sampling will be discussed. For each method, students will learn the relevant formulas for point estimates and variance estimates; however, the course will emphasize application over theoretical proofs of the formulas.
The second day will focus on estimation based on survey samples and the usage of survey weights to reduce sampling variance and non-response bias. Furthermore, students will learn how complex designs and estimators alter the ways in which survey data should be analyzed. Traditional methods of analysis, usually taught in introductory statistics courses, are inapplicable to such data sets.


Keywords



Learning objectives

Participants should understand what a sampling design and how to implement a complex sampling design.
The participants should gain an understanding that the sampling design matters when the conducted an analysis of sample data, i.e. a statistical test.
Finally participants should have learned how survey weights work and how they influence the properties of estimators.


Prerequisites

Participants should have had an introductory level of knowledge about statistical inference, e.g. they should be familiar with the terms estimator, sampling variance, and statistical test.
Participants should have a beginner's level knowledge of R, which is used for the exercises of the course.


Recommended readings

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