Structural Equation Modeling in Economics: Advanced Training Course

Introduction

In the vanguard of quantitative economic analysis, Structural Equation Modeling (SEM) has emerged as an indispensable and highly sought-after econometric tool for unraveling intricate causal relationships within complex economic systems. This intensive 5-day training course is meticulously designed to immerse participants in the cutting-edge methodologies of SEM, empowering economists, researchers, and data scientists to move beyond conventional regression limitations and conduct rigorous, theory-driven empirical investigations. You will master the art of model specification, identification, estimation, and evaluation, gaining unparalleled proficiency in testing sophisticated economic hypotheses involving both observed and unobserved latent variables, ultimately enabling deeper insights into economic phenomena.

Duration: 5 Days

Target Audience:

  • Economists
  • Statisticians
  • Data scientists
  • Researchers and academics in economics and social sciences
  • Graduate students (Master's and Ph.D.) in quantitative fields
  • Policy analysts
  • Business analysts and managers
  • Consultants working with economic data

Objectives:

  1. To develop a comprehensive understanding of the theoretical foundations of Structural Equation Modeling.
  2. To master the process of specifying, identifying, and estimating SEMs for economic applications.
  3. To competently differentiate between measurement models and structural models.
  4. To acquire practical skills in evaluating model fit using various indices and diagnostic measures.
  5. To understand how to incorporate latent variables and address measurement error in economic models.
  6. To apply SEM techniques to analyze mediation, moderation, and complex indirect effects.
  7. To gain proficiency in using industry-standard software for SEM analysis in an economic context.
  8. To interpret and effectively communicate the results of SEM analyses for policy and research.
  9. To learn strategies for handling missing data and non-normal distributions in SEM.
  10. To critically evaluate published SEM studies and identify potential pitfalls in economic research.

Course Modules:

  1. Foundational Concepts and Introduction to Structural Equation Modeling
    • Overview of structural equation modeling as a general statistical framework.
    • Key differences between structural equation modeling and traditional regression.
    • Historical development and the importance of structural equation modeling in economics.
    • Applications of structural equation modeling in various economic sub-disciplines.
    • Conceptualizing latent variables and observed indicators in economic models.
  2. Path Analysis and Causal Inference
    • Introduction to path analysis as a building block of structural equation modeling.
    • Specifying direct, indirect, and total effects in economic models.
    • Assessing causality in observational data using structural equation modeling.
    • Model identification issues in complex path models.
    • Visualizing relationships using path diagrams.
  3. Measurement Models and Confirmatory Factor Analysis (CFA)
    • Review of classical measurement theory and its limitations.
    • Introduction to confirmatory factor analysis (CFA) within structural equation modeling.
    • Specifying and estimating measurement models for economic constructs.
    • Assessing reliability and validity (convergent, discriminant) of economic scales.
    • Testing for measurement invariance across different economic groups.
  4. Full Structural Models with Latent Variables
    • Integrating measurement and structural models in full structural equation modeling.
    • Specifying causal relationships among latent economic constructs.
    • Estimating parameters in complex structural equation models.
    • Interpreting standardized and unstandardized coefficients.
    • Practical examples of full structural equation models in economic research.
  5. Model Estimation Techniques and Assumptions
    • Overview of maximum likelihood (ML) estimation and its assumptions.
    • Generalized Least Squares (GLS) and Weighted Least Squares (WLS) estimators.
    • Robust estimation methods for non-normal data.
    • Addressing missing data using Full Information Maximum Likelihood (FIML) and multiple imputation.
    • Understanding model convergence and estimation issues.
  6. Assessing Model Fit and Respecification
    • Understanding the concept of model fit in structural equation modeling.
    • Key goodness-of-fit indices (Chi-square, RMSEA, CFI, TLI, SRMR).
    • Criteria for evaluating acceptable model fit in economic studies.
    • Strategies for model modification and respecification based on fit indices.
    • Overfitting and theoretical justification for model changes.
  7. Mediation, Moderation, and Multi-Group Analysis
    • Testing mediation effects (direct, indirect, total) in structural equation modeling.
    • Modeling moderation (interaction effects) using structural equation modeling.
    • Performing multi-group analysis to compare models across different economic populations.
    • Testing for differences in latent means and structural parameters across groups.
    • Advanced applications of mediation and moderation in economic research.
  8. Software Applications and Reporting Structural Equation Modeling Results
    • Hands-on training with leading structural equation modeling software (e.g., Amos, Mplus, R packages like lavaan).
    • Inputting data, specifying models, and running analyses in chosen software.
    • Interpreting software output and generating relevant tables and figures.
    • Best practices for reporting structural equation modeling results for publication in economic journals.
    • Critiquing published structural equation modeling studies in economics.
  9. Advanced Topics in Structural Equation Modeling
    • Introduction to longitudinal structural equation modeling and growth curve models.
    • Bayesian structural equation modeling approaches.
    • Exploring structural equation modeling with categorical variables.
    • Addressing common method bias in survey data.
    • Frontiers of structural equation modeling application in economics.
  10. Workshop and Practical Application
    • Participants bring their own research questions and datasets for analysis.
    • Guided sessions for applying learned structural equation modeling techniques to individual projects.
    • Peer review and constructive feedback on structural equation modeling models and interpretations.
    • Developing a strategic plan for integrating structural equation modeling into future economic research.

Q&A and troubleshooting specific structural equation modeling challenges

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

 

Structural Equation Modeling In Economics: Advanced Training Course in Kenya
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