Confirmatory Factor Analysis with SPSS Amos
What you'll learn
- Confirmatory Factor Analysis
- Path Analysis
- Amos
- Mediation Analysis
- Structural Equation Models
- SEM
Requirements
- There are no prerequisites for this course.
- You should have a valid Amos license (or download the trial version, available from IBM for a 30-day time limit)
Description
In this course you will learn how to develop valid and reliable instruments to collect data.
You will learn how to measure item reliability in the context of confirmatory factor analysis.
You will be able to assess the validity of your constructs (by measuring convergent and discriminant validities).
The course contains very detailed exercises, that are solved step by step, so that you can understand how to apply each step to your own models.
Who this course is for:
- Researchers and students from all areas of study for which statistics and assessment instruments play an important role.
Course content
- Preview04:02
- 03:36CFA - Part 02 Measurment Model for a Construct
- 03:24CFA - Part 03 - Relations between PA, CFA and SEM
- 05:40CFA - Part 04 Measures of Construct Validity
Instructor
![Luís Cunha, PhD](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.4 Instructor Rating
- 142 Reviews
- 1,353 Students
- 4 Courses
I did undergraduate and postgraduate studies both in Psychology and in Computer Science.
I taught Psychology / Neuropsychology courses at University for many years, and I got the level of Advanced Specialist in Neuropsychology by OPP (the official entity that regulates psychologists in Portugal)
Afterwards, I graduated in Computer Science and I did a PhD in Information Science, in which I had to apply structural equation modelling (using software such as Amos and R).
Today, I teach Object Oriented Programming, System Analysis, Software Engineering, Interface Design, and Multivariate Statistics.
My main area of work is Applied Statistics, Research Methods, and Machine Learning (with a particular focus on Neural Networks / Deep Learning).