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Multivariate Data Analysis in R for Research Scholars
Rating: 4.7 out of 5(10 ratings)
411 students

Multivariate Data Analysis in R for Research Scholars

Study Structural Equation Modeling, Confirmatory Factor Analysis, Exploratory Factor Analysis and Logistic Regression
Created byDr Kapil Jaiswal
Last updated 8/2024
English

What you'll learn

  • Running Confirmatory Factor Analysis in R with library Lavaan
  • Executing Exploratory Factor Analysis in R with library Lavaan
  • Creating SEM model with Higher Order Constructs
  • Mediation Analysis in SEM
  • Concepts related to and creation of Logistic Regression Model

Course content

2 sections • 6 lectures • 1h 43m total length

  • Confirmatory Factor Analysis13:43

    This lecture contains basic concepts of CFA and its implementation and interpretation of results in R Studio

  • Exploratory Factor Analysis17:18

    This lecture starts with a brief difference between CFA and EFA and then explains EFA implementation and interpretation of results in R Studio.

    The same data file as was used in CFA and same presentation file can be referred to, for this lecture as well

  • SEM with Mediation and higher order constructs21:13

    You will learn how to use Lavaan library in R to create SEM model ( which is a combination of Linear regression and CFA) , creation of hierarchical constructs through R programming, mediation and interpretation of results like direct and indirect impact of mediating variables and how to use them in your research report.

Requirements

  • Basics of Research Methodology, Questionnaire Validity Concepts, R Studio,
  • Should have at least gone through concepts of SEM, CFA and EFA, once

Description

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.

Who this course is for:

  • Research Scholars
  • Anyone pursuing Data Analysis and interested in implementing SEM, CFA and EFA in R studio

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