Analyzing Developmental Trajectories with Mixture Models: A Second Generation of Models and Software

使用混合模型分析发展轨迹：第二代模型和软件

• 批准号: 9911370
• 负责人: Daniel Nagin
• 金额: $ 24.99万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9911370&HistoricalAwards=false
• 关键词: Analyzing; Developmental; Trajectories; Mixture; Models

A developmental trajectory describes the course of a behavior over age or time. This project builds upon prior NSF funded research (SES-9511412) that developed a semi-parametric, group-based approach for identifying distinctive groups of individual trajectories within the population of interest, and also produced "canned" SAS-based software for estimation of the trajectory models. This project will extend the research in several important ways. First, the univariate trajectory models and software will be extended to include a random effect component. The software also will be expanded to incorporate a previously developed non-parametric trajectory model. Second, a next generation of joint trajectory models will be developed. Work to date has focused on developing group-based models appropriate for analyzing the developmental course of a single behavior. A key goal of this research is to generalize univariate trajectory models to allow the joint estimation of trajectories of two distinct but related behaviors, for example, physical aggression in childhood and violent delinquency in adolescence. Here again the capacity for joint trajectory estimation will be incorporated into the already available software package. Third, alternative measures for evaluating model goodness of fit will be explored. Such measures are intended to complement the Bayesian Information Criterion (BIC) as a basis for model selection.This methodological research program is motivated by substantive problems from criminology, developmental psychology, and psychiatry, such as co-morbidity and heterotypic continuity. Co-morbidity refers to the contemporaneous occurrence of two or more undesirable conditions such as physical aggression and hyperactivity during childhood. Heterotypic continuity refers to the inter-temporal manifestation of a latent individual trait in different but analogous behavioral forms. For example, a latent propensity for violence may reveal itself as kicking and biting siblings during early childhood, gang fighting during adolescence, and spouse abuse during adulthood. The form and target of the aggression is different but the constant is physical violence. Because of the changing form of the manifestation, use of the same measurement scale at different stages of life is inappropriate for capturing such a tendency. The themes of co-morbidity and heterotypic continuity will be explored in analyses of major prospective longitudinal data sets from around the world.

发展轨迹描述了行为随年龄或时间的变化过程。 该项目建立在先前NSF资助的研究（SES-9511412）的基础上，该研究开发了一种半参数的、基于组的方法，用于识别感兴趣的人群中的个体轨迹的独特组，并且还产生了用于轨迹模型估计的“罐装”基于SAS的软件。 该项目将在几个重要方面扩展研究。 首先，单变量轨迹模型和软件将被扩展以包括随机效应分量。 该软件还将得到扩展，以纳入以前开发的非参数轨迹模型。 其次，将开发下一代关节轨迹模型。 迄今为止，工作的重点是开发适合于分析单个行为发展过程的基于群体的模型。 本研究的一个主要目标是推广单变量轨迹模型，以允许联合估计两种不同但相关的行为的轨迹，例如，儿童时期的身体攻击和青少年时期的暴力犯罪。 在这里，关节轨迹估计的能力将再次纳入现有的软件包。 第三，将探讨评估模型拟合优度的替代措施。 这些措施的目的是补充贝叶斯信息标准（BIC）作为模型selection.This方法的研究计划的基础是从犯罪学，发展心理学和精神病学，如共病和异型连续性的实质性问题的动机。 共病是指儿童时期同时发生两种或两种以上不良状况，如身体攻击和多动症。 异型连续性是指一个潜在的个体特征在不同但相似的行为形式中的跨时间表现。 例如，潜在的暴力倾向可能会在幼儿期表现为踢和咬兄弟姐妹，在青春期表现为帮派争斗，在成年期表现为虐待配偶。 攻击的形式和目标各不相同，但身体暴力是不变的。由于表现形式的变化，在不同的生命阶段使用相同的测量尺度是不合适的，以捕捉这种趋势。 在分析来自世界各地的主要前瞻性纵向数据集时，将探讨共病和异型连续性的主题。