Mixture distribution latent state-trait analysis: Basic ideas and applications
Mixture distribution latent state-trait analysis: Basic ideas and applications
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DOI：
10.1037/1082-989x.12.1.80
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发表时间：
2007-03-01
影响因子：
7
通讯作者：
Nussbeck, Fridtjof W.
中科院分区：
文献类型：
--
作者：
Courvoisier, Delphine S.;Eid, Michael;Nussbeck, Fridtjof W.
扩展的潜在状态-特质模型的连续观察变量的混合物潜在状态-特质模型的变化，没有协变量，可以单独的个人不同的职业生涯的具体变化。重复测量的情绪状态（N = 501）的实证应用表明，一个模型与2个潜在的类适合的数据。较大的类（76%）由情绪高度可变的个体组成，其总体幸福感相对较低，其情绪变化受到日常麻烦和提升的影响。较小的类别（24%）代表那些相当稳定和快乐的人，他们的情绪只受日常提升的影响，而不是日常麻烦。对5组样本量和5组场合数的无协变量模型的模拟研究表明，该模型参数估计的适当性取决于观察次数（越高越好）和场合数（越高越好）。另一项模拟研究估计了Lo-Mendell-Rubin检验的I型和II型误差。
Extensions of latent state-trait models for continuous observed variables to mixture latent state-trait models with and without covariates of change are presented that can separate individuals differing in their occasion-specific variability. An empirical application to the repeated measurement of mood states (N = 501) revealed that a model with 2 latent classes fits the data well. The larger class (76%) consists of individuals whose mood is highly variable, whose general well-being is comparatively lower, and whose mood variability is influenced by daily hassles and uplifts. The smaller class (24%) represents individuals who are rather stable and happier and whose mood is influenced only by daily uplifts but not by daily hassles. A simulation study on the model without covariates with 5 sets of sample sizes and 5 sets of number of occasions revealed that the appropriateness of the parameter estimates of this model depends on number of observations (the higher the better) and number of occasions (the higher the better). Another simulation study estimated Type I and II errors of the Lo-Mendell-Rubin test.