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Sensitivity Analysis in Structural Equation Models: Cases and Their Influence.
Jolynn Pek1, Robert C MacCallum1
1a University of North Carolina at Chapel Hill.
Multivariate Behavioral Research
|January 8, 2016
Summary
Detecting influential cases in structural equation models (SEM) is crucial. This study introduces methods to identify "good" and "bad" cases, improving model interpretation and reliability.
Area of Science:
- Statistics
- Social Sciences
- Psychometrics
Background:
- Outlier and influential case detection is standard in linear regression.
- Case diagnostics in structural equation models (SEM) are less understood and applied.
- Case diagnostics reveal data subset uncertainties and highlight unusual data points.
Purpose of the Study:
- To present and illustrate measures of case influence for SEM.
- To emphasize the practical application and interpretation of case diagnostics in SEM.
- To highlight the distinction between outliers and influential cases, and introduce "good" and "bad" cases.
Main Methods:
- Application of several case influence measures within SEM.
- Empirical illustration using a common factor model (verbal/visual ability).
- Empirical illustration using a general SEM (industrialization and democracy).
Main Results:
- Cases can uniquely influence different aspects of SEM results.
- Distinction between outliers and influential cases is critical.
- Identification of "good" (improving fit) and "bad" (worsening fit) influential cases.
Conclusions:
- Detecting influential cases is vital for robust SEM.
- Recommendations for applying case influence measures in SEM are provided.
- Understanding case influence enhances the reliability of SEM findings.
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