Exploring Individual Differences in Response to Intervention

描述（由申请人提供）：阅读困难有负面后果，往往与行为问题有关，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，儿童带入课堂的个人特征和历史，以及由此延伸的干预项目，对识字学习产生了互动影响。鉴于需要更好地理解和改善扫盲干预范式的结果，本项目建议探讨儿童特质、家庭环境和家庭风险状况的个体差异如何调节干预的反应。为此，该项目创新性地提出将三个现有的干预项目联合收割机结合起来，以创建一个比任何单独的干预措施更有代表性和更强大的数据汇集源，并利用已收集数据的知识库。该项目的三个目标将有助于实现使用生物生态模型测量个体差异对干预的反应的总体目标。目的1探索儿童的特质特征，或认知和心理社会结果。目标2探索家庭环境，如家庭识字的做法和父母的信仰。目的3探讨各种学习障碍的家庭危险状况和干预困难。通过使用综合数据分析的新方法，可以组合来自每个项目的原始数据，并控制跨站点的异质性。此外，对于儿童的个体差异方面，目前没有在现有的干预计划，我们建议招募原来的参与者和他们的家庭到本项目的问卷部分。这种原始数据的汇集，以及通过调查问卷收集的新数据，将允许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，作为多层次模型中的调节因子，预测儿童对干预的反应，定义为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，这对学童和后代公民具有重大的公共卫生影响。

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.