Exploring Individual Differences in Response to Intervention

描述(由申请人提供)：阅读困难具有负面后果，通常与行为问题相关，并可能对儿童的学业成绩、健康和福祉产生不利影响。越来越多的研究表明，儿童带进课堂的个人特征和历史，以及由此而来的干预项目，对识字学习产生了交互作用。鉴于人们呼吁更好地理解和改进扫盲干预范例的结果，本项目建议探讨儿童特征、家庭环境和家庭RIS状况的个体差异如何缓和干预的反应。为此，该项目创新性地建议将现有的三个干预项目结合起来，创建一个比任何一个单独干预更具代表性和更强大的汇集数据来源，并利用已经收集的数据的知识库。该项目的三个目标将有助于实现利用生物生态学模型衡量干预措施的个体差异的总体目标。目标1探讨儿童的特质特征，或认知和心理社会结果。目标2探讨家庭环境，如家庭识字做法和父母信仰。目的3探讨各种学习障碍的家庭危险状况及干预应对的困难。通过使用新的综合数据分析方法，可以将每个项目的原始数据组合在一起，并控制跨站点的异质性。此外，对于现有干预项目中没有的儿童个体差异方面，我们建议将原始参与者及其家庭招募到本项目的问卷部分。这种原始数据的汇集，以及通过问卷收集的新数据，将允许输入个体差异的各种来源，特别是儿童特征、家庭环境和家庭风险状况，作为多水平模型的调节变量，预测儿童对干预的反应，该干预被定义为由测试前状态控制的测试后状态。这项工作有可能导致更有效的扫盲干预措施，这对学童和未来几代公民的公共健康具有重大影响。

DESCRIPTION (provided by applicant): Reading difficulties have negative consequences that are frequently associated with behavior problems and can adversely impact children's school outcomes, health, and wellbeing. There has been a growing body of work which suggests that the individual traits and history that a child brings into a classroom, and by extension an intervention project, have an interactive effect on literacy learning. Given the call for better understanding and improving the outcomes from literacy intervention paradigms, this project proposes to explore how individual differences in child traits, family environment and familial ris status moderate response to intervention. To do so, this project innovatively proposes to combine three extant intervention projects to create a pooled source of data which is more representative and powerful than any one intervention alone, as well as capitalizing on the knowledge pool of already collected data. The three Aims of this project will serve to meet the general goal of measuring individual differences in response to intervention using the bioecological model. Aim 1 explores child trait characteristics, or cognitive and psychosocial outcomes. Aim 2 explores the family environment, such as home literacy practices and parental beliefs. Aim 3 explores the familial risk status of various learning disabilities and difficulties n response to intervention. By using the novel method of integrative data analysis, the raw data from each project can be combined and heterogeneity across sites controlled for. Additionally, for aspects of child individual differences which are not presently available in the existing intervention projects, we propose to recruit original participants and their families into a questionnaire portion of the present project. This pooling of raw data, as well as the new collection of data through a questionnaire, will allow for the various sources of individual differences, specifically child traits, family environment, and familial risk status, to be enteredas moderators in a multilevel model predicting children's response to intervention defined as post- test status controlled for by pre-test status. This work has the potential to lead to more effectiv literacy interventions, which has great public health implications for school children and future generations of citizens.