What is path analysis?

Path analysis is a subset of structural equation modeling that examines the direct and indirect relationships between observed variables.

What are the key metrics for evaluating model fit?

Key metrics for model fit include chi-square tests, RMSEA, CFI, TLI, SRMR, and others. These help determine how well a proposed model explains the observed data.

How does the chi-square test evaluate model fit?

The chi-square test compares observed data with the expected data from the hypothesized model. A significant result (p < .05) typically indicates a poor model fit.

Path Analysis and Model Fit: Understanding Key Metrics

Path analysis is a key aspect of structural equation modeling (SEM) used to evaluate the direct and indirect relationships between variables in a model. This article explores the essential metrics, such as the chi-square test, RMSEA, CFI, TLI, and more, to understand how well a model fits the data and how these indicators help determine model reliability.

Path Analysis and Model Fit

Path analysis is a subset of structural equation modeling (SEM) that focuses on observed variables and their linear relationships. Unlike SEM, it does not typically involve latent variables. Evaluating model fit is a critical step in path analysis, and several statistical metrics are available for assessing this fit.

Understanding the fit of the proposed model is crucial for ensuring the relationships between variables are plausible. Researchers rely on various indices to ensure accuracy and robustness in the model.

Chi-Square Test of Model Fit

The chi-square (χ²) test is one of the most commonly used methods to assess model fit. It compares observed data with expected data based on the hypothesized model. A significant chi-square result (p < .05) indicates a poor fit. However, this test is sensitive to sample size and can often reject models with large samples, even when the discrepancies are minimal.

Due to this limitation, while useful, the chi-square test is not relied upon exclusively in model fit evaluation.

Root Mean Square Error of Approximation (RMSEA)

RMSEA is a preferred fit index as it accounts for model complexity and sample size. RMSEA values below 0.05 suggest a good model fit, while values between 0.05 and 0.08 represent reasonable fit. Models with values above 0.10 typically indicate poor fit.

Unlike the chi-square test, RMSEA adjusts for model complexity, making it suitable for larger models or samples.

Comparative Fit Index (CFI)

The Comparative Fit Index (CFI) compares the fit of the target model to a null model. CFI values range from 0 to 1, with higher values indicating better fit. A CFI of 0.90 or higher is often considered acceptable, and a CFI of 0.95 or higher suggests excellent fit.

This index is less sensitive to sample size, making it an effective alternative to the chi-square test for model fit assessment.

Tucker-Lewis Index (TLI)

The Tucker-Lewis Index (TLI), or Non-Normed Fit Index (NNFI), also compares the fit of the target model to a null model. The TLI penalizes models for unnecessary complexity, with values above 0.90 indicating a good fit. Models with values closer to 1 balance complexity and fit.

The TLI is particularly valuable when comparing models with different numbers of parameters, as it rewards parsimonious models.

Standardized Root Mean Square Residual (SRMR)

The SRMR measures the discrepancy between observed and predicted correlations. An SRMR value below 0.08 indicates a good fit, while values closer to zero reflect a better model fit.

The SRMR is an absolute fit index, making it a popular choice due to its straightforward interpretation in path models.

Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)

The AIC and BIC are used to compare competing models, rather than assessing a single model's fit. Lower AIC and BIC values indicate better-fitting models, especially when balancing goodness-of-fit and parsimony.

These indices are useful for model selection but do not provide absolute thresholds, unlike RMSEA or the chi-square test.

Goodness of Fit Index (GFI)

The Goodness of Fit Index (GFI) measures the proportion of variance and covariance explained by the model. Values closer to 1 indicate better fit, with 0.90 or higher generally considered acceptable.

Although GFI has fallen out of favor due to its sensitivity to sample size, it still provides useful information about model fit in smaller models.

Interpretation of Fit Indices

Evaluating a model's fit requires considering multiple indices, as no single metric provides a complete picture. A well-fitting model should ideally have a non-significant chi-square, a CFI and TLI above 0.90, an RMSEA below 0.08, and an SRMR below 0.08.

However, fit indices are not strict cut-offs. They should be evaluated in conjunction with the model's theoretical basis, parameter estimates, and statistical significance.

Conclusion

Understanding the key metrics in path analysis and model fit is essential for assessing the adequacy of a proposed model. By considering metrics such as the chi-square test, RMSEA, CFI, TLI, and others, researchers can evaluate the plausibility and robustness of their models.