Data Management and Analysis

数据管理与分析

• 批准号: 8301773
• 负责人: MARGARET RUTH BURCHINAL
• 金额: $ 42.96万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8301773
• 关键词: Address; Archives; Area; Behavioral; Child; Child Development; Cognitive; Collaborations; Communication; Communities; Computers; Data; Data Analyses; Data Collection; Data Set; Databases; Documentation; Ensure; Family; Family Process; Generations; Goals; Individual; Informal Social Control; Information Systems; Interview; Life; Measures; Methodology; Methods; Outcome; Outcome Measure; Phase; Poverty; Procedures; Process; Process Measure; Protocols documentation; Quality Control; Questionnaires; Research; Research Personnel; Research Project Grants; Resources; Rural; Services; Work; computerized data processing; cost effectiveness; data acquisition; data management; data sharing; design; early childhood; executive function; experience; innovation; person centered; programs; statistical service

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.

数据管理和分析核心的目标是继续提供数据管理 为计划项目研究人员提供各种数据采集和统计方面的服务和统计专业知识 分析活动。每个项目目标的组成部分是核心观测的管理和分析， 访谈、问卷、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和数据中心的大量资源 北卡罗来纳大学弗兰克·波特·格雷厄姆儿童发展中心 (FPG) 的分析中心 (DMAC) 和 PSD 方法学中心 (TMC)。数据管理和分析核心的具体目标是： 1) 会同执行委员会实施计划中的缺失设计。 2) 制定和维护数据管理策略，处理在通用协议中收集的数据和 高效、准确地完成各个项目。 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并实施数据分析计划 包括以变量为中心和以人为中心的方法来解决研究问题 与项目研究人员的合作。 5）建立数据归档制度，实现数据共享。 这些目标需要经验丰富的统计学家和计算机程序员的合作。 DMAC 程序员拥有定量数据管理各个方面的专业知识，从数据的输入和跟踪 数据生成分析文件和统计编程。 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合作 童年研究项目。数据管理和分析核心为 每个领域的计划项目。利用经验丰富的数据采集和分析中心 处理和分析整个项目的数据将提供高水平的质量控制和成本 效力。 中央数据采集和分析中心对于该计划项目尤其重要，因为数据 每个项目的贡献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 II 收集的家庭过程测量 项目 III 和核心社区测量是主要预测指标或结果测量 所有项目的分析。数据处理和分析的中央协调将确保一致和 及时处理这些数据 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 调查人员。 数据管理和分析核心提供的服务涉及几乎所有阶段 研究包括设计、数据收集、数据输入和分析。拥有的主要优点 数据管理和计算的集中设施是提供协调服务的能力 解决这些活动中涉及的广泛问题。通过协调所有数据处理活动 项目，减少重复工作，提高数据管理和分析的效率，以及 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， 在所有项目之间获得最大程度的沟通，以进一步理解要分析的数据，因为 以及这些分析的统计程序。此外，项目之间的协同效应得到增强 通过让相同的程序员和统计学家跨项目工作。