Online Faculty Development Programme on Structural Equation Modelling Using AMOS

Expert: Prof. Manoj Kumar Mishra

Assistant Professor, Faculty of Management, Marwadi University, Rajkot, Gujarat, India

Data Analysis-EDP from IIM Jammu

Prof. Mishra is currently serving as an assistant professor in the Faculty of Management at Marwadi University, Rajkot, Gujarat, with over 16 years of academic experience. His PhD topic “Green Entrepreneurial Intention among Higher Education Students in Delhi/NCR.”  Prof. Mishra has published more than forty research papers in reputed national and international journals, including Scopus-indexed (Q1, Q2) and ABDC-listed journals. He has conducted more than 100 workshops and faculty development programs (FDPs) across India on research methodology and data analysis. He is proficient in multiple statistical and analytical tools such as SPSS, AMOS, Jamovi, Smart-PLS, Power BI, Tableau, Meta-Essentials, NVivo, R-Studio, fsQCA, EViews, and STATA.

Programme Objectives

• To enable an understanding of the basic to advanced level of Regression and Model building
• To furnish a hands-on session on SPPS and AMOS for Regression analysis and ANN
• 
  

Pedagogy

The FDP will be highly experimental and interactive with lectures, discussions, presentations, and hands-on sessions using SPPS and AMOS software. Participants will be provided with the relevant industry example data sets for the purpose of analysis to facilitate experiential learning. 

Programme Schedule:

Day 1: Structural Equation Modelling (SEM) using AMOS – Foundation, Model Fit, and Introduction to Nonlinear Thinking: Model Preparation & Specification, Model Fit Testing & Improvement, Validity, Reliability & Interpretation, Why Linearity is Not Enough – Introduction to Nonlinearity

Day 2: Advanced Mediation & Moderation using PROCESS-MACRO with Nonlinear Extensions: Mediation Analysis (Linear), Moderation Analysis (Linear), Moderated Mediation (Conditional Process Analysis) : PROCESS Model 7, PROCESS Model 14 , PROCESS Model 58 

Day 3: Artificial Neural Networks (ANN) for Nonlinearity – Modeling, Comparison, and Reporting Why ANN for Nonlinearity in Social Science / Management Research, ANN Architecture & Design, Sensitivity analysis and variable importance (connection weights method), Resources: Sample papers (e.g., Journal of Business Research – two-stage SEM-ANN).