Applied Statistics for Educational Researchers (DUH165)

This course consists of an introduction to R with a focus on latent variable modeling. We will cover confirmatory factor analysis and structural equation modeling, together with relevant psychometric theory. You will learn how to import, visualize, describe, and analyze real-world datasets, and to conduct reproducible analysis with quarto.

Course description for study year 2024-2025


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This PhD course will introduce educational researchers to SEM and psychometrics and enable the successful candidate to apply those analyses in their own research using R software.

Learning outcome

By completion of this course, the PhD candidate will have gained the following:


    • of measurement theory
    • a good understanding of multiple regression and factor analysis
    • a good understanding of SEM
    • understanding the importance of reproducible research
    • deeper understanding of statistical inference


    • running latent variable models in R
    • preparing results of such analyses for publication

General competences:

    • being able to choose and apply the right analyses for the given data
    • developing advanced strategies for further research

Required prerequisite knowledge


Recommended prerequisites

The students are expected to

  • have R and RStudio already installed
  • formulate a research question in their PhD project, and acquire a relevant dataset that might help answer the question


Form of assessment Weight Duration Marks Aid
Paper 1/1 Passed / Not Passed

Evaluation will be based on the active participation and analyses performed in group work, presented in a brief paper.Coursework requirements: Active participation in lectures and seminars at the workshop. Self-study. The students’ workload will be approximately 150 hours of work.

Coursework requirements

80 % attendance
At least 80 % attendance in lectures and seminares.

Course teacher(s)

Course coordinator:

Njål Foldnes

Study Program Director:

Hein Berdinesen

Course teacher:

Ulrich Dettweiler

Method of work

In this week-long seminar, we will introduce CFA and SEM and demonstrate their flexibility. For instance, we study longitudinal data by specifying growth curve and cross-lagged models. Teaching sessions will alternate between lectures and group-work discussions in order to learn theoretical concepts, and more hands-on experimentation writing R scripts.

Overlapping courses

Course Reduction (SP)
Advanced Statistics for Educational Researchers: Analyzing Structural Equation Models and Latent Growth Curves w/ MPlus (DSP165_1) 5

Open for

International and local students enrolled in a doctoral program. Max. 25 participants.

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


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