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Model Conditioned Data Elasticity in Path Analysis: Assessing the "Confoundability" of Model/Data Characteristics.
Dustin A Fife1, Joseph Lee Rodgers2, Jorge L Mendoza3
1a Arthritis and Clinical Immunology Department , Oklahoma Medical Research Foundation.
Multivariate Behavioral Research
|January 7, 2016
Summary
This study introduces Model Conditioned Data Elasticity (DE), a new metric for assessing how well data fits various linear models. DE helps address issues of equivalent models and data confoundability in statistical analysis.
Area of Science:
- Statistics
- Quantitative Psychology
- Psychometrics
Background:
- Extensive research has focused on the validity of fit indices in Path Analysis and Structural Equation Modeling.
- Recent developments encourage exploring alternative criteria for model comparison, such as model complexity.
Purpose of the Study:
- To introduce and investigate Model Conditioned Data Elasticity (DE), a novel metric assessing a dataset's inherent ability to fit constrained linear models.
- To explore the utility of DE in addressing issues of equivalent models and data/model confoundability.
Main Methods:
- Development and application of the "DE package" in R for automated computer searches.
- Comparison of DE with existing concepts like equivalent models and data confoundability.
Main Results:
- Demonstration of how DE can be assessed using automated computational methods.
- Exploration of the relationship between DE, equivalent models, and data/model confoundability.
Conclusions:
- Model Conditioned Data Elasticity (DE) offers a new perspective for evaluating model fit beyond traditional fit statistics.
- DE provides a framework for understanding the inherent properties of data in relation to model fitting, contributing to the ongoing debate on fit statistics.
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