An SAS Macro for Implementing the Modified Bollen-Stine Bootstrap for Missing Data: Implementing the Bootstrap Using Existing Structural Equation Modeling Software

The Bollen–Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to non-normal data. The purpose of this article is to demonstrate the use of a custom SAS macro program that can be used to implement the Bollen–Stine bootstrap with exist-ing SEM software. Although this article focuses on missing data, the macro can be used with complete data sets as well. A series of heuristic analyses are presented, along with detailed programming instructions for each of the commercial SEM soft-ware packages. Bootstrapping is a method that can be used to correct for the standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to nonnormal data. Two forms of the bootstrap have been discussed in the SEM litera-ture, the naive bootstrap and the Bollen–Stine bootstrap; the former is appropriate for generating standard error estimates, whereas the latter can be used to estimate standard errors (Yung & Bentler, 1996) and to correct for bias in the model fit sta-tistic (Bollen & Stine, 1992). Although the Bollen–Stine bootstrap has performed