University of Oregon, 5262 University of Oregon, Eugene, OR 97403, United States.
Southern Methodist University, United States.
J Sch Psychol. 2017 Apr;61:55-74. doi: 10.1016/j.jsp.2016.12.001. Epub 2017 Jan 19.
Emergent reading skills are crucial to the development of fluency and comprehension, and as such, assessing kindergarten entry skills is critical to inform educational decisions. However, skills that are assessed too early are likely to yield many zero scores, as most students do not yet have the experience or ability to perform the task. Although these floor effects typically lessen across time to show near-normal distributions, growth models cannot accommodate repeated measures with different distributions. The purposes of this paper are to (a) introduce a two-step sampling weight approach to growth mixture modeling that addresses distributions changing over time, and (b) apply the approach to a sample of 1911 kindergarten students universally screened on an emergent reading skill (letter sound fluency) across the year. Results distinguish between students that begin at zero and make meaningful gains and those who begin at zero and do not. We discuss the methodological implications of our approach and the practical implications for growth modeling and early identification.
应急阅读技能对流畅性和理解能力的发展至关重要，因此，评估幼儿园入学技能对于做出教育决策至关重要。然而，评估过早的技能可能会导致很多零分，因为大多数学生还没有经验或能力来完成任务。尽管这些地板效应通常随着时间的推移而减轻，以显示接近正态分布，但增长模型不能适应具有不同分布的重复测量。本文的目的是：(a)介绍一种两步抽样权重方法，用于增长混合建模，以解决随时间变化的分布；(b)将该方法应用于 1911 名在整个学年普遍接受一项应急阅读技能（字母发音流畅性）筛查的幼儿园学生样本。结果区分了从零点开始并取得有意义进步的学生和从零点开始但没有进步的学生。我们讨论了我们方法的方法学意义以及对增长建模和早期识别的实际意义。
