DRE 1011 Psychometrics and Multivariate Analysis

DRE 1011 Psychometrics and Multivariate Analysis

Course code: 
DRE 1011
Course coordinator: 
Ulf Henning Olsson
Course name in Norwegian: 
Psychometrics and Multivariate Analysis
Product category: 
PhD Economics courses
2021 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

The course is a thorough examination of the basics of multivariate statistics and structural equation modeling (SEM).  SEM is a very general and flexible analysis approach that combines path analysis and confirmatory factor analysis with the ability to model complex hypothesis relevant for researchers in sociology, psychology, marketing, strategy, education and economics. 

Learning outcomes - Knowledge

The aim of the course is to give the students training in areas of advanced multivariate statistics, by teaching them some of the most popular computer based methods applied in social science statistics. The course will also focus on basic statistical theory as well as on critical use of statistics in research. There will also be discussions and presentations of some of the latest research within parametric methods and non-normality.

Learning outcomes - Skills

After undertaking this course, the student should be able to apply several multivariate statistical techniques, use modern statistical software and apply these on their research projects. They should also have acquired enough basic knowledge so that they on their own can extend their statistical "tool box".

General Competence


Course content

Module 1

  • Univariate Regression analysis and Analysis of Variance
  • Classification: Logistic Regression/Probit Regression and Discriminant analysis
  • ​Multivariate Regression analysis
  • EFA (Exploratory factor analysis) and PCA (Principal Component Analysis)

Module 2

  • CFA (Confirmatory factor analysis)
  • MTMM models
  • SEM (Structural Equation Modeling)
  • Multigroup Analysis
  • ​Longitudinal SEM
Teaching and learning activities


Software tools
Software defined under the section "Teaching and learning activities".
Additional information

Main software is R/lavaan (and MPlus).


Admission to a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.

External candidates are kindly asked to attach confirmation of admission to a PhD programme when signing up for a course with the doctoral administration. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on courses does not permit registration for courses, handing in exams or gaining credits for the course. Course certificates or conformation letters will not be issued for sitting in on courses


Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Required prerequisite knowledge


Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
DRE 10111
Grading scale:
Grading rules:
Internal and external examiner
Examination when next scheduled course
100No2 Month(s)Individual The paper should be original work, and be written specifically for this course.
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Individual
Duration:2 Month(s)
Comment:The paper should be original work, and be written specifically for this course.
Exam code:DRE 10111
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
Group work / Assignments
80 Hour(s)
Specified learning activities (including reading).
Student's own work with learning resources
50 Hour(s)
Autonomous student learning (including exam preparation).
30 Hour(s)
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.