Essentials of SmartPLS 4: For Research and Data Analysis

PLS-SEM is a composite-based approach to SEM that aims at maximizing the explained variance of dependent constructs in the path model. Researchers and practitioners use PLS-SEM, especially when they conduct studies on success factors and the sources of competitive advantage.

Compared to other SEM techniques, PLS-SEM allows researchers to estimate very complex models with many constructs and indicator variables. Furthermore, PLS-SEM allows for estimating reflective and formative constructs and generally offers much flexibility in terms of data requirements. The goal of PLS-SEM is the explanation of variances (prediction-oriented character of the methodology) rather than explaining covariances (theory testing via covariance-based SEM, CB-SEM). The application of the PLS-SEM method is of high interest if the assumptions of CB-SEM are violated and the proposed cause-and-effect relationships are not sufficiently explored.

The course will include the following aspects:

• Why is data analysis required?

• Basic concepts related to SEM

• Data analysis using SPSS and SmartPLS

• Measurement model analysis

• Structural model analysis

• Mediator/Moderator analysis

• Advanced issues in SmartPLS

• Explanation in APA style

• Real exercise from Fiverr projects

• Getting started with Fiverr gigs

• Tips and tricks and many more

The course features include but are not limited to:

• Competitive edge for pursuing PhD and Master's

• Required tool for scholarly publication

• High acceptance rate by top business journals

• Easy explanation of the statistical analysis

• Focus on both theory and practice

• Become a global data analyst and freelancer

• Excellent carrier opportunity

• Huge demand in local and global markets

Software package: SmartPLS 4, SPSS 25.0

Who should attend?

Research scholars, faculty members, university students, and individuals who are engaged in or interested in contemporary statistical techniques

Prerequisite: Basic knowledge of multivariate techniques such as factor analysis and multiple regression.

Thanks and Regards

Shahedul Hasan

Research Assistant & Independent Researcher.

BBA & MBA, University of Dhaka.

Ex-Lecturer, East Delta University.

Data Analysis Instructor, Instructory and Udemy.

Top Rated Freelancer, Upwork.

Editorial Review Board Member, Virtual Economics & Transnational Marketing Journal.

Editor, Global Journal of Entrepreneurship, Innovation and Leadership.

Editorial Assistant, Journal for the Study of Cooperative and Experiential Education.