PhD Course: PED610: Using R and Structural Equation Modeling in Educational Research

ECTS credits:

Participation with approved assignment: 5 credits.

Participation without examination: 2 credits, provided participation on all three days.

Programme

PhD programme in Humanities and Education

Prerequisites

Students must be admitted to a relevant PhD-programme.

Learning outcomes

After completing the course, participants will:

• have knowledge of the basic functionalities of the R operating system
• be able to run, modify, and write basic R syntax to perform routine data management tasks
• have the necessary knowledge and skills to start learning R beyond the introductory level
• have knowledge of model specification, identification, and evaluation
• be able to formulate and implement basic structural equation models (SEM) using R
• be able to evaluate a basic SEM and respecify it if necessary.
• have the necessary knowledge and skills to start learning and using SEM beyond the introductory level. 

Contents

During the first day of the course, essential concepts within the program R will be introduced. R is a free software environment (https://www.r-project.org/) for statistical computing and graphics that has gained much popularity in the recent years. Familiarizing oneself with R and basic programming concepts will lead to being able to run basic R syntax and perform routine data management tasks in R.

The second day focuses on Structural Equation Modeling (SEM). SEM is a flexible multivariate statistical approach that has become increasingly popular in research in the field of the social sciences. The day will include lectures and practical exercises in specifying, identifying, estimating, and evaluating basic SEM models, such as path models, confirmatory factor analysis, and structural regression models. These models will be implemented in lavaan, a package in the statistical program R.

Participants should have installed R, RStudio Desktop, and the package lavaan before attending.

Teaching methods

Teaching will consist of a combination of lectures and practical exercises, spread over three days. The course will be taught in English.

Examination requirement

Participation in all three days of the course.

Assessment methods and criteria

An individual practical assignment: Student who wish to gain full credits for this course must submit a paper that describes a practical analysis of a SEM. Example datasets will be provided for analysis

Offered as a free-standing course

Yes

Responsible faculty

Faculty of Humanities and Education

Contact person

Esther Canrinus