Multivariate Data Analysis in R for Research Scholars
Multivariate Data An
Unlock and Explore the Secrets of Data: Master multivariate Statistical Techniques and Logistic Regression. The course contains detailed videos on following
1) How to implement Structural Equation Modeling (SEM) more importantly including higher order constructs and mediation with latent variables. Basically, SEM is a combination of CFA and Linear Regression. Creation of Higher order (or hierarchical component models) has an inherited complexity which is also compounded by mediation/moderation methods. Still I have tried to keep it as simple as possible.
2) Implementing confirmatory factor analysis (CFA), explanation of basic concepts and terms in CFA and how to validate questionnaire through CFA including metrics like Composite Reliability, Discriminant validity and Average variance extracted. The mathematical equations are also explained to a level, which help in understanding the relation between Factors and Observed variables and various other terms used in CFA.
3) Executing EFA and interpreting the results from the point of view of documenting in Research Reports/ Thesis. All the prerequisites have been explained too.
All the sessions start from where the scholar has collected the primary data through a questionnaire.
The lectures may not have in depth details of all statistical terms, nevertheless, they provide sufficient awareness, which can help researchers document their thesis.
Lavaan library has been used in R Studio for implementation of all the above methods
Again, if you are looking on implementation SEM, EFA and CFA in R, with a point of view of research scholar or research supervisor, this is exactly for you.
Additionally, This course also contains lectures on Logistic Regression (supervised machine learning technique), from the basic concepts to its implementation to interpretation of results and checking effectiveness of LR model.
alysis in R for Research Scholars, Study Structural Equation Modeling, Confirmatory Factor Analysis, Exploratory Factor Analysis and Logistic Regression.
