The study presented here was intended to develop and provide a relatively simple method for detecting aberrant observations in confirmatory factor analysis (CFA). This method exploited a by-product of Full Information Maximum Likelihood (FIML) estimation of these models, the log-likelihood produced for each individual observation. This score, after adjusting for missing data, indexed the degree to which a model fits for a specific individual. A simulation study was run to test this index, labelled adj_lli. Data were simulated under varying levels of covariance structure, proportion of aberrant data, and proportion of missing data. Each cell had 200 samples with n = 200. Additionally, adj_lli was compared to three existing methods: Reise and Widaman's (1999) INDCHI, Yung's (1997) method for detecting outliers in mixture models, and Bollen's A, a general multivariate method (1987). Results indicated that adj_lli was effective in detecting outliers and offered some advantages over three other methods.