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Value-Added Models (VAMs) require consistent longitudinal data that includes student test scores coming from sequential years. However, longitudinal data is usually incomplete for several reasons, including year-to-year changes in student populations. This study explores the implications of yearly population changes on teacher VAM scores. I used the North Carolina End of Grade student data sets, created artificial sub-samples, and run separate VAMs for each sub-sample. Results of this study indicate that changes in student population could affect teacher VAM scores.