Abstract
The study proposed a method for extending the Bollen-Stine bootstrap of model fit to structural equation models with missing data. Matrix algebra difficulties associated with an incomplete data matrix are circumvented by applying the Bollen-Stine transformation to each case (or group of cases sharing a common pattern of missing data) using reduced arrays that contain elements corresponding to the observed variables. A SAS macro program is provided for the purposes of implementing this procedure, and its' performance was assessed in a simulation that varied distribution shape, sample size, and the missing data rate. Compared to the unadjusted fit statistic, which produced dramatically inflated Type I error rates, the bootstrap yielded model rejection rates quite close to the nominal 5% level, although rejection rates were conservative under small sample conditions.
Original language | English (US) |
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Pages (from-to) | 359-377 |
Number of pages | 19 |
Journal | Multivariate Behavioral Research |
Volume | 37 |
Issue number | 3 |
DOIs | |
State | Published - 2002 |
ASJC Scopus subject areas
- Statistics and Probability
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)