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Application of Path Analysis and Structural Equation Modeling in Nutrition and Dietetics.
Jeffrey E Harris1, Philip M Gleason2
1Department of Nutrition, West Chester University, West Chester, PA.
Journal of the Academy of Nutrition and Dietetics
|July 21, 2022
Summary
This article explains statistical techniques for building and testing structural models, including multiple regression and structural equation modeling, to understand variable relationships.
Area of Science:
- Statistics
- Quantitative Research Methods
- Psychometrics
Background:
- Understanding complex relationships between variables is crucial in many scientific disciplines.
- Existing statistical methods often require integration to model intricate theoretical frameworks.
- A need exists for a cohesive explanation of techniques used in structural modeling.
Purpose of the Study:
- To describe statistical procedures for developing and testing structural models.
- To elucidate the relationships and interrelationships among concepts and variables.
- To provide a comprehensive overview of key techniques in structural equation modeling.
Main Methods:
- Multiple Regression Analysis
- Exploratory and Confirmatory Factor Analysis
- Path Analysis
- Structural Equation Modeling (SEM)
Main Results:
- Detailed explanation of each statistical procedure and its contribution to structural modeling.
- Clarification of variable types: endogenous, exogenous, mediating, measured, and latent.
- Guidance on interpreting statistical estimates such as regression coefficients, path coefficients, factor loadings, and R-squared values.
Conclusions:
- The described statistical procedures provide a robust framework for testing complex theoretical models.
- Understanding these techniques enhances the ability to analyze and interpret multivariate data.
- The monograph serves as a foundational resource for researchers in structural modeling.
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