Training in Applied Structural Equation Modeling (SEM) using AMOS/R
Lecture 1 from our Online SEM Training Series
Structural Equation Modeling is a powerful multivariate data analysis technique that is widely used in many areas of research. It allows both confirmatory and exploratory modeling, meaning SEM is suited for both theory testing and theory development. Factor analysis, path analysis and regression all represent special cases of SEM. Structural Equation Modeling finds wide-spread application in all the major fields of study such as Economics, Social Sciences, Biology, Psychology, Education, Healthcare, and Business.
IBM® SPSS® Amos enables you to specify, estimate, assess and present models to show hypothesized relationships among variables. This software lets you build models more accurately than with standard multivariate statistics techniques. Users can choose either the graphical user interface or non-graphical, programmatic interface. SPSS Amos allows you to build attitudinal and behavioral models that reflect complex relationships.
R is a free software environment for statistical computing and graphics. It compiles on a wide variety of UNIX platforms, Windows and MacOS. The R software package is widely used among statisticians and data miners for their data analysis requirements.
This training will cover all the important concepts behind SEM with detailed emphasis on theory, application and interpretation. The main objective of this training is to help you gain better understanding about SEM and make you proficient in the use of Structural Equation Models.
At the end of this training, participants will be able to understand and apply Structural Equation Models to solve real-world challenges.
This training is will cover the following aspects related to Structural Equation Modeling:
1. Introduction to SEM
2. Different Concepts and Terminology Related to SEM
3. Exploratory Factor Analysis
4. Confirmatory Factor Analysis
5. SEM Analysis
6. Multi-Group Comparison
7. Mediation Analysis
8. Moderation Analysis
This training will have a mix of theoretical and practical sessions. Participants will be taught about all the important concepts related to Structural Equation Modeling during the theory lectures, whereas, the practical sessions will be used to supplement the theory sessions.
This is a 10 hours long one-to-one training program that will be delivered to you online via go-to-meeting. In order to attend this training you’ll need a working internet connection and a computer/laptop/tablet with AMOS/R software installed. The classes (2-hours sessions) will be held online everyday from Monday to Friday. The training will also include some supplementary readings.
Participation Certification will be provided to everyone who completes the training within the stipulated time.
Who should attend this training:-
This training program is designed for graduate and post-graduate students, researchers, scholars, faculty members and working professionals who work with data and have basic knowledge about regression analysis, factor analysis and path diagrams. This training program is open to everyone who is interested in learning about Structural Equation Modeling, irrespective of his/her academic background.
The total fee for this training is 350 USD (including taxes).
The registrations are non-transferable to any other training program or individuals. The training fee is non-refundable.
- Attend this training from anywhere.
- Flexibility in timings based on your geographical location.
- Learn from experts at 40% of the market cost.
- Unlimited e-mail support during the course.
- Free assignments and supplementary reading materials.
Registration Procedure for this course:-
- Fill the online registration form by clicking here
- Make the payment via PayPal or bank-transfer against the invoice sent to you.
- Once your registration is confirmed you can choose from the available training slots based on your time-zone and convenience.
Please feel free to contact us at firstname.lastname@example.org incase you have any questions for us.