| Ginger Nelson Goff
UCLA
Assessing the impact of tracking on individual growth in mathematics achievement using random coefficient modeling
FINAL REPORT:
This dissertation uses data from the Longitudinal Study of American Youth (LSAY) to examine the effects of tracking on student growth in mathematics achievement from the seventh through the eleventh grade. Random coefficient growth models in a latent variable framework are used to analyze the data. School and student reported tracking variables from the seventh and eighth grades are used in different growth models. The study also looks at school reported track compared to student perception of track placement and change in tracking levels from the seventh to the eighth grade. A simulation study investigates methods for dealing with the lack of a background ability variable since tracking and ability are highly correlated.
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