The full-information maximum likelihood (FIML) is a popular estimation method for missing data in structural equation modeling (SEM). However, previous research has shown that SEM approximate fit indices (AFIs) such as the root mean square error of approximation (RMSEA) and the comparative fit index (CFI) can be distorted relative to their complete data counterparts when they are computed following the FIML estimation. The main goal of the current paper is to propose and examine an alternative approach for computing AFIs following the FIML estimation, which we refer to as the FIML-corrected or FIML-C approach. The secondary goal of the article is to examine another existing estimation method, the two-stage (TS) approach, for computing AFIs in the presence of missing data. Both FIML-C and TS approaches remove the bias due to missing data, so that the resulting incomplete data AFIs estimate the same population values as their complete data counterparts. For both approaches, we also propose a series of small sample corrections to improve the estimates of AFIs. In two simulation studies, we found that the FIML-C and TS approaches, when implemented with small sample corrections, estimated the population-complete-data AFIs with little bias across a variety of conditions, although the FIML-C approach can fail in a small number of conditions with a high percentage of missing data and a high degree of model misspecification. In contrast, the FIML AFIs as currently computed often performed poorly. We recommend FIML-C and TS approaches for computing AFIs in SEM. (PsycInfo Database Record (c) 2023 APA, all rights reserved).