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Value-added scores claim to measure effectiveness on student growth. However, the use of these scores has not been sufficiently supported as a sound accountability index. Variables used change the classification of schools. This research investigates the effects of un-accounted for latent subpopulations at the student level with an MLGMM to account for schools’ classification. Additionally, LEP, student SES and school SES are also modeled. Results indicate that the number of schools identified for special treatment were greatly reduced when value-added scores were extracted from a MLGMM in conjunction with school SES, in comparison to a traditional MLM, particularly for homogeneous schools (very high SES and very low SES schools).