Structural Equation Modeling & Path Analysis Using Ibm Amos

Master Covariance Based Structural Equation Modeling (CB-SEM) and Path Analysis using IBM AMOS and SmartPLS 4

What you'll learn

To introduce the basic concepts related to Multivariate Analysis and CB-SEM.

To illustrate the basic applications of CB-SEM in Research using AMOS and SmartPLS.

To illustrate the procedure for preparing and creating data file using SPSS.

To provide hands on training of applying CB-SEM using valid data set and reporting results.

Requirements

Basic knowledge of statistics and data analysis.

Familiarity with concepts like correlation, regression, and hypothesis testing.

Description

Structural Equation Modeling (SEM), a second-generation multivariate technique, has become a widely used technique for examining complex business and social research models. SEM is expressly considered prominent in behavioral studies where the variables are latent. There are two types of SEM where covariance-based SEM (CB-SEM) is primarily used to confirm (or reject) theories (i.e., a set of systematic relationships between multiple variables that can be tested empirically).The most popular and user-friendly software package for CB-SEM is AMOS (Analysis of Moments Structures), which enables researchers to analyze the inter-relationships among constructs having multiple indicators in a model, where the inter-relationships are estimated simultaneously. AMOS is also owned by IBM, and the software is intertwined with SPSS; hence, it's also officially known as IBM SPSS AMOS. Moreover, SmartPLS 4 also offers the facility for analyzing models based on CB-SEM.This course provides a step-by-step guide to CB-SEM and Path Analysis using IBM AMOS and SmartPLS 4. Whether you're a researcher, data analyst, or student, this course will equip you with the skills to perform complex statistical analyses and interpret results confidently. By the end of this course, you'll be able to build, test, and refine models using AMOS and SmartPLS, being proficient in SEM and Path Analysis.Why this Course:Master Covariance-based Structural Equation Modeling (CB-SEM) and Path Analysis with IBM AMOS to gain a competitive edge in academic research.Equip yourself with the tools for successful scholarly publications in top-tier business journals.Learn complex statistical analyses quickly and clearly, enhancing your research impact.Develop both theoretical understanding and practical skills for data analysis.Seize excellent career opportunities in a market with high demand for SEM and Path Analysis expertise.Course Contents:Fundamental concepts related to CB-SEM.Data analysis using SPSS/AMOS/SmartPLS.CFA/Measurement model analysis.Structural model analysis/Hypothesis testing.Mediator/Moderator analysis.Advanced issues in AMOS.Explanation in APA style.Real exercise and solution.Tips and tricks and many more.What are you waiting for? Enroll now and enjoy:Thanks and RegardsShahedul HasanResearch 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.

Overview

Section 1: Welcome to Covariance-based Structural Equation Modeling

Lecture 1 Course Contents

Lecture 2 Reference Materials

Section 2: Foundations of Structural Equation Modeling

Lecture 3 Multivariate Analysis and EFA vs CFA

Lecture 4 Introduction to SEM and CB-SEM vs PLS-SEM

Lecture 5 Concept of Latent Constructs

Lecture 6 Types of Models and Reflective vs Formative Models

Section 3: AMOS Interface and Estimation Methods

Lecture 7 Overview of IBM AMOS Interface

Lecture 8 Difference between Estimation Methods

Section 4: Steps and Prerequisites in Conducting SEM

Lecture 9 Overview of Steps in Conducting SEM

Lecture 10 Define the Individual Constructs

Lecture 11 Model Identification in AMOS

Lecture 12 Assumptions and Sample Size Determination

Lecture 13 Data Preparation and Preliminary Analysis using SPSS

Lecture 14 Reporting the Results of Preliminary Analysis

Section 5: Specify the Measurement Model

Lecture 15 Specify the Measurement Model: An Overview

Lecture 16 Developing Measurement Model in AMOS

Section 6: Assess Measurement Model Reliability and Validity

Lecture 17 Assess Measurement Model Fit

Lecture 18 Assessing Model Fit in AMOS

Lecture 19 Reporting the Results of Model Fit

Lecture 20 Convergent Validity of Measurement Model

Lecture 21 Discriminant Validity of Measurement Model

Researchers and academics in social sciences, psychology, education, and business.,Data analysts and statisticians looking to expand their skills.,Graduate students seeking to enhance their research methodology.,Anyone interested in learning CB-SEM and Path Analysis using IBM AMOS and SmartPLS.