Essentials Of Smartpls 4: For Research And Data Analysis
Published 8/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English
| Size: 913.39 MB[/align]
| Duration: 1h 49m
Learn Structural Equation Modelling and Path Analysis using SmartPLS 4 from Scratch
[b]What you'll learn[/b]
To introduce the basic concepts related to Multivariate Analysis and SEM.
To illustrate the basic applications of SEM in Research using SmartPLS 4.
To illustrate the procedure for preparing and creating data file using SPSS.
To provide hands on training of applying SEM using valid data set and reporting results.
[b]Requirements[/b]
Basic knowledge of multivariate techniques such as factor analysis and multiple regression.
[b]Description[/b]
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 SEMData analysis using SPSS and SmartPLSMeasurement model analysisStructural model analysisMediator/Moderator analysisAdvanced issues in SmartPLSExplanation in APA styleReal exercise from Fiverr projectsGetting started with Fiverr gigsTips and tricks and many moreThe course features include but are not limited to:Competitive edge for pursuing PhD and Master'sRequired tool for scholarly publicationHigh acceptance rate by top business journalsEasy explanation of the statistical analysisFocus on both theory and practiceBecome a global data analyst and freelancerExcellent carrier opportunityHuge demand in local and global marketsSoftware package: SmartPLS 4, SPSS 25.0Who should attend?Research scholars, faculty members, university students, and individuals who are engaged in or interested in contemporary statistical techniquesPrerequisite: Basic knowledge of multivariate techniques such as factor analysis and multiple regression.Thanks and RegardsShahedul HasanResearch Assistant and Independent ResearcherBBA and MBA, University of DhakaEx-Lecturer, East Delta UniversityData Analysis Instructor, Instructory and UdemyData Analyst & Freelancer, Fiverr
Overview
Section 1: Introduction to the Course and Instructor
Lecture 1 Course Instructor
Lecture 2 Course Objectives and Contents
Section 2: Getting Started with The Course
Lecture 3 Basic Concepts Related to SEM: Part A
Lecture 4 Basic Concepts Related to SEM: Part B
Lecture 5 Research Process and Software Package
Section 3: Steps in Conducting SEM
Lecture 6 Steps in Conducting SEM: Part A
Lecture 7 Steps in Conducting SEM: Part B
Lecture 8 Data Preparation and Preliminary Analysis using SPSS: Part A
Lecture 9 Data Preparation and Preliminary Analysis using SPSS: Part B
Section 4: Measurement Model Analysis in SmartPLS 4
Lecture 10 Assess Measurement Model Validity:Theory
Lecture 11 Construct Reliability and Convergent Validity
Lecture 12 Reporting Results of Construct Reliability and Convergent Validity
Lecture 13 Discriminant Validity
Lecture 14 Reporting Results of Discriminant Validity
Section 5: Structural Model Analysis in SmartPLS 4
Lecture 15 Assess Structural Model Validity: Part A
Lecture 16 Structural Model Analysis in SmartPLS 4
Lecture 17 Reporting Results of Structural Model Analysis
Lecture 18 Assess Structural Model Validity: Part B
Lecture 19 Reporting Results
Research Scholars,Faculty Members,University Students,PhD Researchers,Freelancing
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