Measurement Error Models

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Measurement Error Models - Course Details

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

Single Course Price:

400.00 EUR (tax exempt)

 

Instructor:

Laura Boeschoten (Tilburg University)

Video lecture:

Daniel Oberski, PhD (Utrecht University)

 

Course Dates

To see all courses in the upcoming term click here.

 

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
Surveys reflect the opinions or facts researchers are after only partly – the other part will be measurement error, which can seriously bias analyses of interest. To remove such biases it is essential to estimate the extent of measurement error in survey variables, which is precisely the goal of statistical measurement error modeling. In this course, we will discuss how measurement error can be defined, how its presence can be detected using specialized data collection designs and models, and how to perform error-corrected statistical analyses of substantive interest.

Prerequisites

  • Knowledge of basic statistics including regression analysis;
  • Ability to run an R script, for example from RStudio; a cursory understanding of R;
  • In-depth knowledge of R or latent variable models is NOT required.

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

  • define measurement error conceptually, including the concepts of reliability and validity;
  • explain the different approaches to estimating measurement error and their respective advantages and drawbacks;
  • interpret the results of statistical models used to estimate measurement error in the absence of a gold standard;
  • perform regression analyses from which the influence of measurement error has been removed and interpret the results.

Course Composition
This is a 2 ECTS course, which runs for 4 weeks. The content of the course is broken down into 4 units:

  1. What is measurement error?
  2. Estimating measurement error in continuous survey variables
  3. Estimating measurement error in categorical survey variables
  4. Correcting regression analyses for the effects of measurement error

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 weekly forum on the course page before the meetings. Just like in an on-site course, homework will be assigned and graded and there will be a final exam at the end of the course.

Grading
Grading will be based on:

  • 3 online quizzes (worth 30% total)
  • Participation in discussion during the weekly online meetings and submission of questions to the weekly forum demonstrating understanding of the required readings and video lectures (worth 10%)
  • A final open-book online exam (worth 60%)

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/

MANNHEIM BUSINESS SCHOOL (MBS)

Located in the heart of the German and European economy, Mannheim Business School (MBS), the umbrella organization for management education at the University of Mannheim, is considered to be one of the leading institutions of its kind in Germany and is continuously ranked as Germany’s #1.

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