Bayesian analysis of longitudinal data using growth curve models

被引:115
作者
Zhang, Zhiyong
Hamagami, Fumiaki
Wang, Lijuan
Nesselroade, John R.
Grimm, Kevin J.
机构
[1] Univ Virginia, Dept Psychol, Charlottesville, VA 22903 USA
[2] Univ Calif, Oakland, CA 94607 USA
关键词
Bayesian analysis; growth curve models; informative priors; longitudinal data; pooling data; WinBUGS;
D O I
10.1177/0165025407077764
中图分类号
B844 [发展心理学(人类心理学)];
学科分类号
040202 ;
摘要
Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth. This step-by-step example illustrates how to analyze data using both noninformative and informative priors. The results show that in addition to being an alternative to the maximum likelihood estimation (MLE) method, Bayesian methods also have unique strengths, such as the systematic incorporation of prior information from previous studies. These methods are more plausible ways to analyze small sample data compared with the MLE method.
引用
收藏
页码:374 / 383
页数:10
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