Multivariate analysis: applied factor and regression analysis (PHD104)
In this course, students gain in-depth knowledge of multivariate statistical methods and good insight into estimating and evaluating different measurement and structural models. Exploratory and confirmatory factor analysis will be a critical method in developing and validating measurement models. Different methods will be applied to assess the validity and reliability of measurement models. Students will learn to apply measurement models in multiple regression models and with Structural Equation Modeling (SEM). Moreover, in SEM, the student will learn to test models with different types of mechanisms, such as the test of mediation and moderation. This course will also give insight into survey development and provide essential knowledge for planning research.
Course description for study year 2025-2026. Please note that changes may occur.
Course code
PHD104
Version
1
Credits (ECTS)
5
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
Content
The course covers knowledge of statistics and practical skills in statistical data analysis. The first part of the course will cover measurement issues and the establishment of validity and reliability. The remaining parts of the course will focus on the development and assessment of linear regression and structural models. Because a key goal of the course is to impart practical skills in data analysis, lectures will be mixed with seminars and applied exercises. Students will use their own datasets when possible.
Examples of statistics and methodology: Regression models, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Structural Equation Modelling (SEM), validity and reliability, test of mediating and moderating effects with the use of SEM, use of control variables in models. Exploratory SEM, longitudinal latent models (e.g., Cross-lagged panel model, CLPM) and Measurement invariance will also be explained and demonstrated.
Statistical software must be installed before the start of the course. The following software will be applied: SPSS, AMOS, and MPLUS.
Learning outcome
Knowledge, the student:
- Get the knowledge to evaluate the appropriateness of different quantitative methods and relate these to research questions
- Can develop, validate, and revise measurement concepts
- Can develop and assess causal models based on their own data
Skills, the student:
- Can develop and assess quantitative research models at the PhD-level
- Can apply appropriate quantitative methods to solve complex research questions
General competence, the student:
- Can evaluate and apply relevant quantitative methods to research projects
- Can discuss and communicate pertinent issues of multivariate analyses at an international academic level
Required prerequisite knowledge
- It is mandatory that the students have basic knowledge of quantitative methods at Master Level.
- Participants must be enrolled in a PhD programme.
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Paper (6-10 pages) | 1/1 | Passed / Not Passed |
Coursework requirements
- A one-page note on a tentative topic for the course paper outlining potential data sources and methods. This note must be submitted one week before the course.
- Presence and active participation throughout the whole week.
- On request during the week, students must share models and results with the rest of the class.
Each participant will write a short paper on a self-chosen topic. In the paper, students will investigate some empirical data and apply multivariate statistics.
Course teacher(s)
Course coordinator:
Espen OlsenCourse teacher:
Espen OlsenMethod of work
The course will consist of one intensive week of lectures and applied seminars, Monday through Friday, from 10.15 to 15.00. Participants are expected to review lecture materials on their own.
The relevant software (Amos 26.0 and SPSS 26.0, Mplus 8.3 or newer versions) should be installed and tested before starting the course. Participants will need to bring well-functioning laptops on which the software is installed. Participants who use laptops supplied by their university should install the software in advance (and contact IT for administrator rights if necessary).