Duncan S C, Duncan T E
Multivariate Behav Res. 1994 Oct 1;29(4):313-38. doi: 10.1207/s15327906mbr2904_1.
Longitudinal data sets typically suffer from attrition and other forms of missing data. When this common problem occurs, several researchers have demonstrated that correct maximum likelihood estimation with missing data can be obtained under mild assumptions concerning the missing data mechanism. With reasonable substantive theory, a mixture of cross-sectional and longitudinal methods developed within multiple-group structural equation modeling can provide a strong basis for inference about developmental change. Using an approach to the analysis of missing data, the present study investigated developmental trends in adolescent (N = 759) alcohol, marijuana, and cigarette use across a 5-year period using multiple-group latent growth modeling. An associative model revealed that common developmental trends existed for all three substances. Age and gender were included in the model as predictors of initial status and developmental change. Findings discuss the utility of latent variable structural equation modeling techniques and missing data approaches in the study of developmental change.
纵向数据集通常存在数据缺失和其他形式的缺失数据问题。当出现这个常见问题时，一些研究人员已经证明，在关于缺失数据机制的温和假设下，可以获得对缺失数据的正确最大似然估计。基于合理的实质性理论，在多组结构方程模型中开发的横截面和纵向方法的混合可以为推断发展变化提供有力的基础。本研究采用一种缺失数据分析方法，使用多组潜在增长模型，对759名青少年在5年期间的酒精、大麻和香烟使用情况的发展趋势进行了调查。一个关联模型显示，这三种物质存在共同的发展趋势。年龄和性别作为初始状态和发展变化的预测因素被纳入模型。研究结果讨论了潜在变量结构方程建模技术和缺失数据方法在发展变化研究中的效用。
