New approaches to studying problem behaviors: a comparison of methods for modeling longitudinal, categorical adolescent drinking data.
New approaches to studying problem behaviors: a comparison of methods for modeling longitudinal, categorical adolescent drinking data.
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DOI：
10.1037/a0014851
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发表时间：
2009-05
影响因子：
4
通讯作者：
Conger, Rand D.
中科院分区：
文献类型：
--
作者：
Feldman, Betsy J.;Masyn, Katherine E.;Conger, Rand D.
关键词：
分析问题行为轨迹可能很困难。这些数据通常是绝对的，而且往往是非常不对称的，这违反了标准正态理论统计模型的分布假设。在本文中，我们提出了几个目前可用的建模选项，所有这些选项都对观察到的分类数据做出了适当的分布假设。基于广义线性模型的三种模型：分层广义线性模型(HGLM)、增长混合模型(GMM)和潜在类增长分析(LCGA)。我们还描述了纵向潜在类分析(LLCA)，它比前三种方法需要更少的假设。最后，我们使用实际的青少年纵向酒精使用数据来说明所有模型。我们引导读者通过模型选择过程，在收敛特性、拟合和残差、简洁性和可解释性方面比较结果。计算和统计软件的进步使大多数研究人员可以很容易地获得这些类型的分析工具。对分类数据使用适当的模型将产生更准确和可靠的结果，在实际数据环境中应用这些模型可有助于在发展和预防科学领域取得实质性进展。
Analyzing problem-behavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normal-theory statistical models. In this paper, we present several currently-available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model (HGLM), a growth mixture model (GMM), and a latent class growth analysis (LCGA). We also describe a longitudinal latent class analysis (LLCA), which requires fewer assumptions than the first three. Finally, we illustrate all of the models using actual longitudinal adolescent alcohol-use data. We guide the reader through the model-selection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention.
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影响因子：
0.9
作者：
Foster, EM;Fang, GY
通讯作者：
Fang, GY
影响因子：
5.9
作者：
DONOVAN, JE;JESSOR, R
通讯作者：
JESSOR, R
影响因子：
5.9
作者：
Colder, CR;Campbell, RT;Flay, BR
通讯作者：
Flay, BR
DOI：
10.1207/s15328007sem1004_1
发表时间：
2003-01-01
影响因子：
6
作者：
Ferrer, E;McArdle, JJ
通讯作者：
McArdle, JJ
影响因子：
7
作者：
Blozis, SA
通讯作者：
Blozis, SA