Multivariate analysis: applied factor and regression analysis PHD104
In this course students get in-dept knowledge in multivariate statistical methods and get good insight into estimation and evaluation of different measurement and structural models. A key method in the development and validation of measurement models will be exploratory and confirmatory factor analysis. Different methods will be applied to assess the validity and reliability of measurement models. Students will learn how 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 test of mediation and moderation.
Course description for study year 2021-2022. Please note that changes may occur.
Semester tution start
Number of semesters
Language of instruction
UiS Business School, UiS Business School
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 relevant issues in multivariate analyses on an international academic level
The course covers knowledges of statistics and practical skills in statistical data analysis. The first part of the course will cover measurement issues and establishment of validity and reliability. The remaining parts of the course will focus on 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: Descriptive statistics, correlations, linear regression, Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM), validity and reliability, test of mediating and moderating effects with the use of SEM, use of control variables in models.
Statistical software: SPSS and AMOS.
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.
Form of assessment
Paper (6-10 pages)
Pass - Fail
A one-page note on a tentative topic for the course paper, outlining potential data sources and methods. This note must be submitted two weeks in advance of the course.
Presence and active participation throughout the whole week.
On request during the week; sharing models and results to the rest of the class.
Each participant will write a short paper on a self-chosen topic. This task includes picking an answerable question, finding relevant data, and conducting an appropriate quantitative analysis. In so far as possible, participants are asked to pick a topic of relevance to their ongoing research. The papers should be submitted within six weeks after the course has finished.
Method 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 and download relevant AMOS plugins on their own.
The relevant software (Amos 26.0 and SPSS 26.0, 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).
Evalutation according to UiS rules and regulations.