文章基本信息

标题：Hypothesized, Directly-Coded Curve Shapes in Growth Curve Analysis: An Example
本地全文：下载
作者：Patricia M. Herman ; Lee Sechrest
期刊名称：Journal of Methods and Measurement in the Social Sciences
电子版ISSN：2159-7855
出版年度：2016
卷号：6
期号：1
页码：13-29
DOI：10.2458/azu_jmmss.v3i2.16476
出版社：University of Arizona Libraries
摘要：Growth curve analysis provides important informational benefits regarding intervention outcomes over time. Rarely, however, should outcome trajectories be assumed to be linear. Instead, both the shape and the slope of the growth curve can be estimated. Non-linear growth curves are usually modeled by including either higher-order time variables or orthogonal polynomial contrast codes. Each has limitations (multicollinearity with the first, a lack of coefficient interpretability with the second, and a loss of degrees of freedom with both) and neither encourages direct testing of alternative hypothesized curve shapes. Especially in studies with relatively small samples it is likely to be useful to preserve as much information as possible at the individual level. This article presents a step-by-step example of the use and testing of hypothesized curve shapes in the estimation of growth curves using hierarchical linear modeling for a small intervention study.
关键词：growth curve analysis; non-linearity; hierarchical linear modeling; intervention outcomes