Empirical and conceptual problems with longitudinal trait-state models: Introducing a trait-state-occasion model
Empirical and conceptual problems with longitudinal trait-state models: Introducing a trait-state-occasion model
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DOI：
10.1037/1082-989x.10.1.3
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发表时间：
2005-03-01
影响因子：
7
通讯作者：
Steiger, JH
中科院分区：
文献类型：
--
作者：
Cole, DA;Martin, NC;Steiger, JH
潜在特质-状态-误差模型(TSE)和带自回归的潜在状态-特质模型(LST-AR)是检验心理结构纵向结构的创新性结构方程方法。这些模型的应用在一定程度上受到经验或概念问题的限制。在本研究中，蒙特卡罗分析表明，当N太小时，当波动太小时，当因子稳定性太大或太小时，TSE模型往往会产生不适当的解。对LST-AR模型的数学分析表明，它对随着时间的推移变得更加高度自相关的结构具有局限性。特质-状态-情境模型比TSE模型具有更少的经验问题，并且比LST-AR模型具有更广泛的适用性。
The latent trait-state-error model (TSE) and the latent state-trait model with autoregression (LST-AR) represent creative structural equation methods for examining the longitudinal structure of psychological constructs. Application of these models has been somewhat limited by empirical or conceptual problems. In the present study, Monte Carlo analysis revealed that TSE models tend to generate improper solutions when N is too small, when waves are too few, and when occasion factor stability is either too large or too small. Mathematical analysis of the LST-AR model revealed its limitation to constructs that become more highly auto-correlated over time. The trait-state-occasion model has fewer empirical problems than does the TSE model and is more broadly applicable than is the LST-AR model.