Integrating person-centered and variable-centered analyses:: Growth mixture modeling with latent trajectory classes
Integrating person-centered and variable-centered analyses:: Growth mixture modeling with latent trajectory classes
复制标题
DOI：
10.1111/j.1530-0277.2000.tb02070.x
复制
发表时间：
2000-06-01
影响因子：
3.2
通讯作者：
Muthén, LK
中科院分区：
文献类型：
--
作者：
Muthén, B;Muthén, LK
背景：许多酒精研究问题需要采用以人为本方法的方法，因为兴趣是寻找异质的个体群体，例如那些容易受到酒精依赖的人，而那些不容易受到酒精依赖。以人为中心的重点也对代表发育轨迹的异质性也很有用。在酒精，药物和心理健康研究中，对异质性的认识导致了多种发育途径的理论。方法：本文简要概述了整合以人为中心和以人为本分析的新方法。讨论的方法包括潜在类别分析，潜在过渡分析，潜在类增长分析，生长混合物建模和一般生长混合物建模。这些方法以一般潜在变量建模框架呈现，该框架不仅包括连续的潜在变量，而且还包括分类的潜在变量，从而扩展了传统的潜在变量建模。回顾性：四个示例使用了国家的青年纵向调查（NLSY）数据，以说明潜在类别分析，潜在类增长分析，生长混合物建模和一般生长混合物建模。反社会行为的潜在类别分析发现了四个类别。发现了四个重型饮酒轨迹课。研究了潜在类别和背景变量与后果之间的关系。结论：以人为本和以可变为中心的分析通常被视为使用不同类型的模型和软件的不同活动。本文简要概述了整合以人为中心和以人为本分析的新方法。一般框架使得将这些模型结合起来并研究新模型，以询问具有以人为中心和可变为中心方面的研究问题。
Background: Many alcohol research questions require methods that take a person-centered approach because the interest is in finding heterogeneous groups of individuals, such as those who are susceptible to alcohol dependence and those who are not. A person-centered focus also is useful with longitudinal data to represent heterogeneity in developmental trajectories. In alcohol, drug, and mental health research the recognition of heterogeneity has led to theories of multiple developmental pathways.Methods: This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. Methods discussed include latent class analysis, latent transition analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. These methods are presented in a general latent variable modeling framework that expands traditional latent variable modeling by including not only continuous latent variables but also categorical latent variables.Results: Four examples that use the National Longitudinal Survey of Youth (NLSY) data are presented to illustrate latent class analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. Latent class analysis of antisocial behavior found four classes. Four heavy drinking trajectory classes were found. The relationship between the latent classes and background variables and consequences was studied.Conclusions: Person-centered and variable-centered analyses typically have been seen as different activities that use different types of models and software. This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. The general framework makes it possible to combine these models and to study new models serving as a stimulus for asking research questions that have both person- and variable-centered aspects.