Analysis of Complex Survey Data

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Analysis of Complex Survey Data - Course Details

Delve into the course contents and find out about the faculty members.

Single Course Price:

800.00 EUR (tax exempt)

 

Instructor:

Stefan Zins, PhD (Institute for Employment Research)

Video lecture:

Stefan Zins, PhD (Institute for Employment Research)

 

Course Dates

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Book this course here!

In order to book the course with alumni conditions, please get in touch with Manon Pfeifer directly.

Course Description

Short Course Description
Analysis of Complex Sample Data covers the following topics: the development and handling of selection and other compensatory weights for survey data analysis; the effects of stratification and clustering on survey estimation and inference; alternative variance estimation procedures for estimated survey statistics; methods and computer software that take into account the effects of complex sample designs on survey estimation and inference; and methods for handling missing data, including weighting adjustment.

Prerequisites
The prerequisites include one or more graduate courses in statistics covering techniques through OLS and logistic regression, a course in applied sampling methods, or permission of the instructor. The course is presented at a moderately advanced statistical level. Although the course will review the fundamentals of statistical analysis methods for survey data and provide detailed examples on the use of statistical software, it will be assumed that the students are familiar with statistical methods, including multiple regression and logistic regression. The initial lectures in the course syllabus will review the various complex features of sample designs and how they influence 2 estimation and inference based on survey data. The course syllabus and level of instruction also assume that students are familiar with basic sampling procedures, including simple random sampling, stratification, cluster sampling and multi-stage sample designs. Students who do not have graduate-level training in sampling techniques should expect to devote additional time during the first weeks of the course to supplemental readings on this topic.

Course Objectives
By the end of the course, students will…

  • understand the importance of accounting for the effects of complex sample designs on estimation and inference.
  • be able to identify how sample design elements impact estimation and inference
  • be able to estimate sampling error using:
  • direct estimators
  • linearization techniques
  • replication methods
  • be able to account for complex sample designs in:
  • descriptive analysis for continuous variables
  • categorical data analysis
  • linear regression
  • logistic regression
  • be able to use standard statistical software to account for the effects of complex sample designs.

Course Composition
This is a 4 ECTS course, which runs for 8 weeks. The content of the course is broken down into 8 units:

  1. Survey estimation and inference for complex sample designs (Part 1)
  2. Survey estimation and inference for complex sample designs (Part 2)
  3. Sampling error estimation for complex samples
  4. Descriptive analysis for continuous variables (Part 1)
  5. Descriptive analysis for continuous variables (Part 2)
  6. Analysis of categorical data from complex samples
  7. Linear regression for complex sample survey data
  8. Logistic regression for complex survey data

Learning and Teaching Methods
In this course, you are responsible for watching video recorded lectures and reading the required literature for each unit and then “attending” mandatory weekly one-hour online meetings where students have the chance to discuss the materials from a unit with the instructor. In addition, students are encouraged to post questions about the materials covered in the videos and readings of the week in the forum before the meetings. Just like in an on-site course, homework will be assigned and graded and there will be a final project at the end of the course.

Grading
Grading will be based on three criteria:

  • Weekly questions submitted / class preparation / class participation (worth 20%)
  • Completion of four (4) homework assignments (worth 40% total)
  • Final course project (worth 40%)

 

ZFU Certification and Online Dispute Resolution

ZFU Certification

The Mannheim Master of Applied Data Science & Measurement program is certified according to the regulations of the ZFU (Staatliche Zentralstelle für Fernunterricht).

 

Online Dispute Resolution

Online dispute resolution according to Art. 14 Sect. 1 ODR-VO: The European Commission provides a platform for online dispute resolution (ODR). You can find more information under http://ec.europa.eu/consumers/odr/

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