DRE 1011 Psychometrics and Multivariate Analysis
DRE 1011 Psychometrics and Multivariate Analysis
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.
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.
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".
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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
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Main software is R/lavaan (and MPlus).
Enrollment in 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 enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.
Covid-19
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.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
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| Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 100 Grouping: 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 |
| Activity | Duration | Comment |
|---|---|---|
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). |
Teaching | 30 Hour(s) |
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.