Structural model assessment, in the context of structural equation modeling (SEM), is a process used to evaluate the validity and reliability of the relationships hypothesized within a structural model. It determines how well the model represents the underlying relationships among constructs and whether the model provides meaningful insights. According to Hair et al. (2022), this assessment involves several key steps to ensure the model is robust and trustworthy.
Here's a breakdown of the core components involved in structural model assessment:
1. Assessing Collinearity
Collinearity (or multicollinearity) refers to a high degree of correlation between two or more predictor constructs in the structural model. High collinearity can distort the results of the model, making it difficult to determine the individual effect of each predictor.
- How to assess: Check Variance Inflation Factor (VIF) values. VIF values above 5, or sometimes 3, suggest potential collinearity issues.
- Solutions: Consider combining highly correlated constructs, removing one of the collinear constructs, or using more sophisticated techniques like ridge regression.
2. Evaluating Significance and Relevance of Relationships
This step focuses on determining whether the path coefficients (representing the strength and direction of relationships) in the model are statistically significant and practically meaningful.
- Significance: Examine the p-values associated with each path coefficient. A p-value below a predetermined significance level (e.g., 0.05) indicates that the relationship is statistically significant.
- Relevance: Consider the magnitude and direction of the path coefficients. Even if a relationship is statistically significant, a small path coefficient might not be practically relevant. The context of the research and the specific constructs being studied should guide the interpretation of relevance.
3. Evaluating Explanatory Power (R²)
The coefficient of determination, R², represents the amount of variance in an endogenous (dependent) construct that is explained by its predictor constructs. A higher R² value indicates that the model has greater explanatory power.
- Interpretation: R² values range from 0 to 1. The interpretation of acceptable R² values depends on the research context. In some fields, R² values of 0.2 are considered substantial, while in others, higher values are expected.
- Limitations: R² only reflects the variance explained by the model; it doesn't guarantee the model is correctly specified or free from bias.
4. Assessing Predictive Power (Q²)
Predictive relevance (Q²), also known as Stone-Geisser's Q², indicates the model's ability to predict unseen data points. It assesses how well the model generalizes beyond the data used to estimate it.
- Calculation: Q² is typically calculated using a blindfolding procedure (omission distance).
- Interpretation: A Q² value greater than zero suggests that the model has predictive relevance. Higher Q² values indicate stronger predictive power. Q² assesses out-of-sample prediction accuracy, a crucial aspect of model validation.
In Summary
Structural model assessment is a critical process for validating and refining structural equation models. By carefully evaluating collinearity, the significance and relevance of relationships, explanatory power (R²), and predictive power (Q²), researchers can ensure that their models are robust, reliable, and provide meaningful insights into the relationships among constructs.
