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Normal Theory GLS Estimator for Missing Data: An Application to Item-Level Missing Data and a Comparison to Two-Stage
Victoria Savalei1, Mijke Rhemtulla2
1Department of Psychology, University of British ColumbiaVancouver, BC, Canada.
Frontiers in Psychology
|June 8, 2017
Summary
A new "full information" generalized least squares (FIGLS) estimator is introduced for structural equation models with incomplete data. This FIGLS method offers an asymptotically efficient estimation approach when data are missing at the item level but the model is at the composite level.
Area of Science:
- Statistics
- Quantitative Psychology
- Econometrics
Background:
- Structural equation models (SEMs) are widely used for analyzing complex relationships between variables.
- Maximum Likelihood (ML) and Generalized Least Squares (GLS) are common estimation methods for complete, normally distributed data.
- Full Information Maximum Likelihood (FIML) is a standard for incomplete, normally distributed data.
Purpose of the Study:
- To define and study the "full information" GLS (FIGLS) estimator for incomplete, normally distributed data.
- To address situations where FIML is not applicable, specifically when SEMs use composite variables (parcels) with item-level missing data.
Main Methods:
- Definition of the novel FIGLS estimator for incomplete data.
- Identification and study of a key application in composite-based SEMs.
- A simulation study comparing FIGLS to two-stage ML with item-level missing data.
Main Results:
- The FIGLS estimator is defined for incomplete normally distributed data.
- FIGLS is shown to be a potentially essential tool when FIML cannot be applied due to missing item-level data in composite-based SEMs.
- Simulation results comparing FIGLS and two-stage ML are presented.
Conclusions:
- The newly developed FIGLS estimator provides a viable and asymptotically efficient alternative for SEMs with item-level missing data when using composite variables.
- FIGLS expands the available estimation methods for SEMs, particularly in complex data scenarios.
- Further research and application of FIGLS are warranted in relevant statistical and social science fields.
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