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. This course will also give insight into survey development.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

PHD104

Version

1

Credits (ECTS)

5

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Offered by
Time table

View course schedule

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-model) and Measurement invariance will also be explained and demonstrated.

Statistical software must be installed before the start of the course (info about this will be updated on Canvas).

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 relevant issues in 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 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.

Course teacher(s)

Course coordinator:

Espen Olsen

Course teacher:

Espen Olsen

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 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).

Open for

PhD candidates enrolled in PhD programmes at the University of Stavanger or accredited universities/university colleges in Norway or abroad. Single Course Admission to PhD-Courses.

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.

Literature

Search for literature in Leganto