دورية أكاديمية

Impact of misspecifications of the latent variance-covariance and residual matrices on the class enumeration accuracy of growth mixture models

التفاصيل البيبلوغرافية
العنوان: Impact of misspecifications of the latent variance-covariance and residual matrices on the class enumeration accuracy of growth mixture models
المؤلفون: Diallo, Thierno M., Morin, Alexandre J., Lu, Huizhong
المساهمون: School of Social Sciences and Psychology (Host institution)
بيانات النشر: U.S., Psychology Press
سنة النشر: 2016
مصطلحات موضوعية: XXXXXX - Unknown, analysis of covariance, mathematical models, statistical power analysis, growth mixture models, stat, psy
الوصف: This series of simulation studies was designed to assess the impact of misspecifications of the latent variance–covariance matrix (i.e., Ψk ) and residual structure (i.e., Θk ) on the accuracy of growth mixture models (GMMs) to identify the true number of latent classes present in the data. Study 1 relied on a homogenous (1-class) population model. Study 2 relied on a population model in which the latent variance–covariance matrix is constrained to be 0 (Ψk = 0): Study 3 relied on a population model in which the latent variance–covariance matrix was specified as invariant across classes (Ψk = Ψ): Finally, Study 4 relied on a more realistic specification of the latent variance–covariance matrix as different across classes (Ψk): In each of these studies, we assessed the class enumeration accuracy of GMMs as a function of different types of estimated model (6 models corresponding to the 3 types of population models used to simulate the data and involving the free estimation of the residual structure across latent classes or not) and 4 design conditions (within-class residual matrix, sample size, mixing ratio, class separation). Overall, our results show the advantage of relying on models involving the free estimation of the Ψk and Θk matrices within all latent classes. However, based on the observation that inadmissible solutions occur more frequently in these models than in more parsimonious models, we propose a more comprehensive sequential strategy to the estimation of GMM.
نوع الوثيقة: article in journal/newspaper
اللغة: English
العلاقة: https://doi.org/10.1080/10705511.2016.1169188Test
DOI: 10.1080/10705511.2016.1169188
الإتاحة: https://doi.org/10.1080/10705511.2016.1169188Test
حقوق: undefined
رقم الانضمام: edsbas.14C19B50
قاعدة البيانات: BASE