The purpose of this study is to explore patterns in model-data fit related to subgroups of test takers from a large-scale writing assessment. Using data from the SAT, a calibration group was randomly selected to represent test takers who reported that English was their best language from the total population of test takers (N = 322,011). A reference scale for the items was constructed based on EBL responses. Response behaviors of test takers who reported that English was not their best language (ENBL) were examined in relationship to this reference scale. This study illustrates the use of differential subgroup analyses to identify patterns related to person misfit within subgroups, as well as subsets of items, that may affect the validity of writing scores for ENBL test takers. The methodology described here offers an approach that can be used to explore, understand, and improve the validity of scores obtained from ENBL test takers in large-scale writing assessments.