Multivariate analysis: applied factor and regression analysis (PHD104)

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

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:

Course teacher:

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

Course assessment

Literature