Confirmatory Factor Analysis
This course introduces the fundamental principles of factor analysis. Researchers are frequently interested in studying a variety of phenomena that are not directly measureable. Commonly studied concepts such as depression, anxiety, IQ and psychopathy cannot be directly observed, but rather their existence is inferred on the basis of phenomena which can be directly observed. This leads to an inevitable question: how can we possibly measure, with any kind of accuracy, that which we cannot observe?
Factor analysis is the process by which unobservable, or ‘latent’, constructs can be accurately and reliably measured via observable, or ‘manifest’, constructs. Since researchers are usually interested in studying unobservable phenomena, factor analysis is an invaluable statistical tool for any scientist. Numerous factor analytic methods exist which can be extremely beneficial at various stages of the research process. This course will cover the major types including exploratory factor analysis, confirmatory factor analysis (CFA) and confirmatory bi-factor analysis.
The seminar uses Mplus and Amos software to demonstrate the implementation of factor analysis and is run by Dr Daniel Boduszek who has used different types of factor analysis in his research. The Quantitative Research Methods Training Unit (QRM-TU) will also invite Associate Members to lead the training.
Course outline
- Why and when Factor Analysis is needed
- Theoretical Introduction to Factor Analysis and latent constructs
- Construct Validity, Dimensionality, and Composite Reliability
- Introduction to the Mplus and/or Amos environment
- Exploratory Factor Analysis (SPSS and Mplus)
- Confirmatory Factor Analysis in Mplus and/or Amos
- Confirmatory Bifactorial Analysis in Mplus
- Testing Factorial Invariance and Composite Reliability
- Practical session
Who should attend?
The course is designed for researchers and postgraduate students who are engaged in research using large data sets. The prerequisite for taking this seminar is basic knowledge of regression analysis. Researchers from economics, public health, epidemiology, psychology, sociology, social work, medical research, education, and similar disciplines are welcome.
Why Quantitative Research Methods Training Unit?
- We know that learning statistical analysis can be a daunting and unpleasant experience at times. This is why we present complicated procedures in a simple way, avoiding jargon and confusing mathematical formulas at all costs! We teach researchers everything they need to know about factor analysis in order to finish their projects in a relaxed and friendly atmosphere where they are helped and encouraged every step of the way.
- The uniqueness of Quantitative Research Methods Training Unit is the focus on the practical application of Factor Analysis, not mathematical procedures.
- Dr Daniel Boduszek and Associate Members of QRM-TU have an extensive experience in the application of factor analytic techniques which is demonstrated by their research outputs.
- The courses is delivered by means of lectures and practical sessions. All analyses are conducted under the supervision of Dr Daniel Boduszek and three tutors (Dr Susie Kola, Dr Katie Dhingra, and Ms Kathryn Sharratt) to maximise learning outcomes (we offer a 1:5 instructor student ratio). Each course concludes with a question and answer session, and there is always the opportunity for participants to discuss their own studies with the tutors.
Location
The Quantitative Research Methods Training Unit (QRM-TU), Ramsden Building, University of Huddersfield.
Book now
To find out when the next training session will be held, please visit our online store where you can also book your place.