Statistical Core

C.数据管理和分析核心 1.目的 数据管理和分析核心的目标是继续提供数据管理 服务和统计专业知识，以计划项目调查人员在广泛的数据采集和 分析活动。每个项目的目标不可或缺的是核心观测， 访谈、问卷调查和行为数据，以及个人数据的管理和分析 项目数据管理活动利用了数据管理部门的大量资源， 弗兰克·波特·格雷厄姆儿童发展中心（FPG）的分析中心（DMAC） 方法中心（TMC）在PSD。数据管理和分析核心的具体目标是： 1)与执行委员会一起实施计划缺失的设计。 2)制定和维护数据管理策略，以处理共同方案中收集的数据， 每个项目都能高效准确地完成。 3)设计和维护项目和通用协议数据的集成数据库。 4)使用创新的纵向数据分析方法制定和实施数据分析计划 包括以变量为中心和以人为中心的方法来解决研究问题， 与项目调查人员合作。 5)建立数据共享的数据存档系统。 这些目标需要有经验的统计人员和计算机程序员的合作。所述DMAC 程序员在定量数据管理的各个方面都有专业知识，从输入和跟踪 数据分析文件的生成和统计编程。DMAC和TMC统计学家 与这些和其他研究人员就许多早期的设计和统计问题进行了合作 儿童研究项目。数据管理和分析核心为 在这些领域的每一个项目。使用经验丰富的数据采集和分析中心， 跨项目处理和分析数据将提供高水平的质量控制和成本 有效性 中央数据采集和分析中心对于本计划项目尤为重要，因为数据 每个项目的信息都有助于所有项目在解决其假设时使用的共同协议。 特别是，项目I收集的执行功能和自我调节措施，课堂观察 项目II收集的儿童认知/学业成果，以及 项目III和核心社区措施是主要的预测或结果措施， 所有项目的分析。数据处理和分析的中央协调将确保数据的一致性， 及时处理这些<$data^for-all- inyestigatgrsv仔细记录数据评分，包括 “汇总变量的可靠性和分布，将有助于其他项目使用一个项目的数据 investigators. 数据管理和分析核心所提供的服务几乎涉及 研究包括设计、数据收集、数据输入和分析。拥有一个 数据管理和计算的集中式设施是提供协调服务的能力， 解决这些活动涉及的广泛问题。通过协调数据处理活动， 减少重复工作，提高数据管理和分析的效率， 提供了对来自不同项目的数据集进行更高级别分析的机会。因此，在本发明中， 在所有项目中获得最大限度的沟通，以进一步了解要分析的数据， 以及这些分析的统计程序。此外，项目之间的协同作用得到加强 通过让相同的程序员和统计员在不同的项目中工作。

C. Data Management and Analysis Core 1. Objective The objective of the Data Management and Analysis Core is to continue to provide data management services and statistical expertise to Program Project investigators in a wide range of data acquisition and analysis activities. Integral to the goals of each project is the management and analysis of Core observational, interview, questionnaire, and behavioral data, as well as management and analysis of data from the individual projects. Data management activities draw on the considerable resources of the Data Management and Analysis Center (DMAC) at the Frank Porter Graham Child Development Center (FPG) at UNC and The Methodology Center (TMC) at PSD. Specific Aims of the Data Management and Analysis Core are to: 1) Implement the planned missing design in conjunction with the Executive Committee. 2) Develop and maintain data management strategies that process data collected in the common protocol and the individual projects efficiently and accurately. 3) Design and maintain integrated databases of project and common protocol data. 4) Develop and implement data analysis plans using innovative analytic methods for longitudinal data including variable-centered and person-centered approaches to address research questions in collaboration with project investigators. 5) To set in place a data archiving system for data sharing. These goals require the collaboration of experienced statisticians and computer programmers. The DMAC programmers have expertise in all aspects of quantitative data management, from the entry and tracking of data to the generation of analysis files and statistical programming. The DMAC and TMC statisticians collaborated with these and other investigators regarding design and statistical issues across many early childhood research projects. The Data Management and Analysis Core provides high level expertise to the Program Project in each of these areas. The use of an experienced data acquisition and analysis center to process and analyze data across the projects will provide both high levels of quality control and cost effectiveness. A central data acquisition and analysis center is especially important for this Program Project because data from each project contributes to the common protocol for use by all projects in addressing their hypotheses. In particular, executive functioning and self regulation measures collected by Project I, classroom observations and child cognitive/academic outcomes collected by Project II, and family process measures collected by Project III, and community measures from the core are primary predictor or outcome measures used in analyses by all projects. Central coordination of data processing and analysis will ensure the consistent and timely processing of these ¿data^for-all- inyestigatgrsv Careful documentation of data scoring, including the "reliability and distributions of summary variables, will facilitate the use of one project's data by other project investigators. The services provided by the Data Management and Analysis Core pertain to almost all phases of the research including design, data collection, data entry, and analysis. The main advantage of having a centralized facility for data management and computing is the ability to provide coordinated services that address the wide range of issues involved in these activities. By coordinating data processing activities for all projects, duplication of effort is reduced, efficiencies of data management and analyses are generated, and opportunities for higher level analysis across data sets from different projects are made available. Thus, maximum communication is obtained across all projects to further the understanding of data to be analyzed, as well as the statistical procedures for those analyses. Furthermore, synergy among projects is enhanced through having the same programmers and statisticians working across projects.