Table_1_Testing the Importance of Individual Growth in Predicting State-Level Outcomes Beyond Status Measures.DOCX

The literature reports mixed findings on whether measuring individual change over time on an interim progress monitoring assessment adds value to understanding student differences in future performance on an assessment. This study examines the relations among descriptive measures of growth (simple difference and average difference) and inferential measures [ordinary least squares (OLS) and empirical Bayes] for 800,000 students in grades 4, 8, and 10 and considers how well such measures statistically explain differences in end-of-year reading comprehension after controlling for student performance on a mid-year status assessment. Student differences in their reading comprehension performance were explained by the four growth estimates (simple difference, average difference, OLS, and empirical Bayes) and differed by status variable used (i.e., performance on the fall, winter, or spring benchmark assessment). The four growth estimates examined in the study all contributed significantly to predicting end-of-year reading comprehension when initial, fall performance was used as a covariate. The simple difference growth estimate was the best predictor when controlling for mid-year (winter) status, and all but the simple difference estimate contributed significantly when controlling for final (spring) status.

现有研究文献针对“在中期进度监测评估（interim progress monitoring assessment）中测量个体随时间的变化是否有助于理解学生在后续评估中的表现差异”这一问题，得出了混杂不一的研究结论。本研究针对4、8、10年级共80万名学生，分析了两种增长描述性指标（简单差值法与平均差值法）与两种推断性指标[普通最小二乘法（ordinary least squares，OLS）与经验贝叶斯法（empirical Bayes）]之间的关联，并考察了在控制学生中期状态评估表现的前提下，上述指标能否在统计学层面有效解释年末阅读理解成绩的差异。本研究中的4种增长估计量（简单差值、平均差值、OLS与经验贝叶斯法）均可解释学生阅读理解成绩的个体差异，且解释效果会因所使用的状态变量（即秋季、冬季或春季基准评估表现）不同而存在差异。当以初始秋季评估表现作为协变量时，本研究所考察的4种增长估计量均对年末阅读理解成绩的预测具有显著贡献。当控制中期（冬季）状态变量时，简单差值增长估计量为最优预测指标；而当控制最终（春季）状态变量时，除简单差值估计量外的其余3种指标均具有显著预测贡献。