Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Structural Equation Models

Practical information
Study year 2016/2017
Time
Block 1
Programme level Full Degree Master
ECTS 7,5 ECTS
Course responsibles
  • Karl Bang Christensen (4-6d63656a4275777066306d7730666d)
  • Esben Budtz-Jørgensen (3-6b687046797b746a34717b346a71)
  • Department of Mathematical Sciences
  • Department of Public Health
Course number: NMAK16018U

Course content

The course is an introduction to latent variable models. We introduce Item Response Theory (IRT) models, focusing mainly on the Rasch model, Confirmatory Factor Analysis (CFA) models, and Structural Equation Models (SEM’s). The exercises will be a mixture of theoretical problems and data analysis. The course covers the following topics:

  • General measurement models (including Rasch model and CFA)
  • Conditional and marginal estimation
  • Model identification
  • Evaluation of model fit
  • Path analysis
  • Structural equation models
  • Measurement error in covariates
  • An introduction to implementations of the methodology (including R and SAS). 

Learning outcome

Knowledge:

At the end of the course the student will have knowledge about different types of latent variable models, and will have the knowledge to

  • Explain the assumptions underlying the models
  • Interpret the parameters of the models
  • Discuss model identification and be able to determine if two models are identical

 

Skills:

The student will acquire skills necessary for applying latent variable models to real data, decide on which model to use and which analysis to perform. The student will have the skills to utilize theoretical results in the practical analysis, including how complex models can be specified.

Competencies:

At the end of the course the students will have the competence to

  • Evaluate the fit of measurement models (including Rasch model and CFA)
  • Estimate the parameters of structural equation models
  • Use latent variable models to adjust for measurement error in covariates

Recommended prerequisites

Statistik 2 or similar knowledge of statistics. Linear algebra and the multivariate normal distribution are essential prerequisites. Some experience with the use of R or SAS is recommended.

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Education

MSc Programme in Statistics

Studyboard

Study Board of Mathematics and Computer Science

Course type

Single subject courses (day)

Duration

1 block

Schedulegroup

A
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Teaching and learning methods

4 hours of lectures and 2 hours of presentation and discussion of a weekly assignment per week for 7 weeks.

Capacity

No restrictions/ no limitations

Language

English

Workload

Category Hours
Lectures 28
Exercises 14
Preparation 164
English 206

Exam

Type of assessment

Oral examination, 30 minutes without preparation
Every week an assignment on the implementation of a solution to a statistical computing problem will be given. Students will in turn present solutions in class followed by a plenary discussion of the solutions. The student's own solutions will form the basis for his or her oral examination.

Aid

All aids allowed

Marking scale

7-point grading scale

Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Censorship form

No external censorship
Several internal examiners

Re-exam

Same as ordinary.

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