Measurement Models in Latent Curve Analysis

潜伏曲线分析中的测量模型

• 批准号: 6976706
• 负责人: PATRICK J CURRAN
• 金额: $ 28.73万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6976706
• 关键词: adolescence (12-20); behavior prediction; behavioral /social science research tag; child psychology; clinical research; computer simulation; data collection methodology /evaluation; disease /disorder etiology; drug abuse; human data; mathematical model; model design /development; psychological models; psychometrics; substance abuse related behavior

DESCRIPTION (provided by applicant): In our first project period we focused on the systematic study of longitudinal latent curve models (LCMs) applied to continuous scale scores with a particular emphasis on challenges that commonly arise in the empirical study of substance use. Despite the many advantages of these LCMs, one limitation is that methods are currently not well developed to fit LCMs to repeated measures that are ordinally scaled, particularly in the presence of missing data. A second limitation stems from the potential violation of a strict set of required assumptions governing the structure of the measurement model of continuous or ordinally observed scale scores over time. To address these limitations, we have drawn upon our findings from the initial project period and have designed the revision of our proposed continuation project around the systematic study of measurement models in latent curve analysis. Our proposed project is organized around four specific aims. In Aim 1 we propose to study existing challenges and identify optimal strategies for fitting LCMs to ordinal manifest scale scores assessed overtime both with complete and missing data. In Aim 2 we plan to study the incorporation of latent factors with continuously scaled indicators in LCMs to allow for tests of measurement invariance and the inclusion of formal measurement models. In Aim 3 we propose extending the findings of Aim 2 to include the incorporation of latent factors with ordinally scaled indicators in LCMs. Finally, in Aim 4 we plan to study the implications of item scaling and measurement invariance across all prior aims with respect to the estimation of statistical power and optimal study design. These project goals will be pursued through the integrated use of analytical review and organization, computer simulation studies, and the analysis of data drawn from an existing longitudinal study of the parental alcoholism effects on the development of drug use in a large sample of adolescent. Taken together, we believe the proposed study has the potential for making significant unique contributions to the field of quantitative methodology and to the rigorous empirical study of developmental trajectories of substance use and abuse.

描述（由申请人提供）：在我们的第一个项目期间，我们重点关注应用于连续量表评分的纵向潜伏曲线模型（LCM）的系统研究，特别强调物质使用实证研究中常见的挑战。尽管这些 LCM 有许多优点，但一个限制是目前尚未开发出适合 LCM 的方法来适应通常按比例缩放的重复测量，特别是在存在缺失数据的情况下。第二个限制源于可能违反一组严格的所需假设，这些假设控制随着时间的推移连续或按序观察的量表分数的测量模型的结构。为了解决这些局限性，我们借鉴了最初项目期间的发现，并围绕潜在曲线分析中测量模型的系统研究设计了我们拟议的延续项目的修订版。我们提出的项目围绕四个具体目标进行组织。在目标 1 中，我们建议研究现有的挑战，并确定使 LCM 适合随时间评估的完整数据和缺失数据的顺序清单量表分数的最佳策略。在目标 2 中，我们计划研究将潜在因素与 LCM 中的连续缩放指标相结合，以便测试测量不变性并包含正式的测量模型。在目标 3 中，我们建议扩展目标 2 的发现，包括将潜在因素与 LCM 中的按序缩放指标相结合。最后，在目标 4 中，我们计划研究所有先前目标的项目缩放和测量不变性对于统计功效估计和最佳研究设计的影响。这些项目目标将通过综合使用分析审查和组织、计算机模拟研究以及对父母酗酒对青少年大样本吸毒发展影响的现有纵向研究中得出的数据进行分析来实现。总而言之，我们相信拟议的研究有可能为定量方法领域以及物质使用和滥用发展轨迹的严格实证研究做出重大独特贡献。