In light of the rapid advancements in quantitative research methods and data analysis, both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) have emerged as essential statistical tools for building and validating theoretical models. These methods are fundamental for understanding the internal structure of measurement instruments and testing relationships among latent variables, especially in disciplines such as social sciences, psychology, and education.

This scientific workshop aims to provide researchers and postgraduate students with an in-depth understanding of factor analysis concepts, proper application conditions, and distinctions between EFA and CFA. It also explores the theoretical and statistical foundations of each approach.

The workshop places special emphasis on the goodness-of-fit indicators used in evaluating CFA models and outlines strategies for enhancing these indicators to achieve more accurate and reliable model interpretations. It represents a valuable opportunity for those seeking to enhance the quality of their quantitative analyses and improve their proficiency in using modern statistical software in accordance with best methodological practices.

Workshop Topics

Introduction to Factor Analysis

Definition and overview of factor analysis

First-order factor analysis

Second-order factor analysis

Exploratory Factor Analysis (EFA)

Theoretical considerations in EFA

Statistical criteria in EFA

Factor extraction methods

Approaches to factor analysis

Common issues in EFA

Confirmatory Factor Analysis (CFA)

Distinguishing between CFA and EFA

Applications of CFA

Model validity testing

Goodness-of-fit testing

Goodness-of-Fit Indicators in CFA
Absolute Fit Indices:

Chi-Square (χ²) Test

Goodness-of-Fit Index (GFI)

Root Mean Square Error of Approximation (RMSEA)

Root Mean Square Residual (RMR)

Incremental/Comparative Fit Indices:

Normed Fit Index (NFI)

Comparative Fit Index (CFI)

Tucker-Lewis Index (TLI)

Parsimony Fit Indices (Economic Indicators):

Parsimonious Normed Fit Index (PNFI)

Parsimonious Comparative Fit Index (PCFI)

Parsimonious Goodness-of-Fit Index (PGFI)

Improving CFA Model Fit

Instructor:
Prof. Dr. Mohamed Ibrahim El-Khouly
Professor, Department of Economics, Zagazig University, Egypt

About the Instructor:
Prof. Dr. Mohamed El-Khouly is a distinguished professor in the Department of Economics at Zagazig University. He holds a Certified International Professional Trainer (CIPT) certification from the United States and a Diploma in Statistics from the Institute of Statistical Studies and Research, Cairo University.

He has conducted numerous in-person and online training courses in statistical analysis using various software, including SPSS, AMOS, EViews, SAS, M-Stat, Statistics, Excel, and GraphPad Prism. He is also a certified trainer at the Faculty and Leadership Development Center (FLDC), where he has delivered courses on scientific writing, research methodology, and international research publishing.

Prof. El-Khouly has participated in multiple research projects, including studies on the economic efficiency of mechanized agriculture, women's political participation, behavioral disorders in Egyptian society, and fishery resources in Egypt. He has also contributed to statistical analysis for many master's and PhD theses and scientific research in fields such as human medicine, veterinary medicine, pharmacy, science, agriculture, specific education, physical education, general education, arts, and commerce both in Egypt and internationally.

He is the author of practical books including Computer Applications and Microsoft Office Programs and Introduction to Statistics and Probability Distributions.