12 Apr module 09: factor analysis
- module 9: factor analysis
14/09/2020 - 15/09/2020
9:30 am - 12:45 pm
Mon 14/09: 9:30 am - 5:00 pm | online
Tue 15/09: 9:30 am - 12:45 pm | online
week 2 | register now
This module is an introduction to three related, but different statistical procedures for describing or explaining the relationship between several variables or measures: Principal Component Analysis (PCA), Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Because parsimony of description is one of the fundamental goals in science, these procedures are widely used in a multitude of domains (e.g. psychology, sociology, biology). Students will be introduced to the concepts and fundamentals of PCA, EFA and CFA, with a focus on the interpretation and practical use of these procedures rather than the mathematical formulas. The software used in this course will be R.
A brief summary of the course is as follows:
- introduction to data reduction techniques: principal component analysis versus factor analysis
- interpretation of component and factor models
- fitting and reporting results of component and factor models
- model revision and comparison
Some very basic knowledge of R is desirable (see https://cran.r-project.org/). In case you want to use your own computer, please bring your own laptop with R installed.
Dr. Mariska Barendse studied at the University of Amsterdam and obtained a research master (cum laude) in the field of educational science with a strong focus on methods and statistics. From 2010 to 2014, she was a PhD candidate at the Department of Psychometrics and Statistics of the Heymans Institute for Psychological Research at the University of Groningen and finished her PhD titled “Dimensionality assessment with factor analysis methods” in 2015. After being a statistical consultant for nine months, she worked a post-doc at Ghent University. In 2019 she was appointed as an assistant professor at the Erasmus University in Rotterdam. Her main research focus is the development and applications of statistical models using non-standard application structural educational modelling including exploratory factor analysis, multilevel modelling, and the analysis of discrete data.