In Florida, mean proficiency scores are reported on the Florida Comprehensive Achievement Test (FCAT) as well as recommended learning gains from the developmental scale score. Florida now has another within-year measure of growth in reading comprehension from the Florida Assessments for Instruction in Reading (FAIR). The FAIR reading comprehension screen is administered three times a year in a computer-adaptive system to students in grades 3-10. The authors' objective in this presentation is to answer the following research questions: (1) What are the correlations between FCAT and the FAIR reading comprehension scores (FCAT success probability and reading comprehension ability)?; (2) What is the value added of the FAIR reading comprehension screen to prior FCAT?; (3) Does the FAIR reading comprehension screen significantly reduce identification errors above and beyond prior FCAT?; (4) What is the value added to predicting FCAT of using Bayesian mean estimates of growth in FAIR reading comprehension compared to difference scores?; and (5) What is the value added to predicting FCAT of using Bayesian mean estimates of growth and prior FCAT scores? Findings reveal that there ways to improve on the traditional "mean proficiency" approach to accountability. The FAIR reading comprehension screen correlated highly with FCAT reading scores. Incorporating the autoregressor of prior FCAT and FAIR performance at the winter assessment into the prediction of current FCAT accounted for up to 7% unique variance. The Simple Difference approach to measuring FAIR growth accounted for up to 2-3% unique variance above and beyond the model which included the FCAT autoregressor and winter performance on the FAIR. Such improvements in FCAT prediction lead to (a) reduction in false positives in classifying student risk and (b) better placement for reading intervention. (Contains 5 tables.)