The Ability for Posterior Predictive Checking to Identify Model Misspecification in Bayesian Growth Mixture Modeling

Proper model specification is an issue for researchers, regardless of the estimation framework being utilized. Typically, indexes are used to compare the fit of one model to the fit of an alternate model. These indexes only provide an indication of relative fit and do not necessarily point toward proper model specification. There is a procedure in the Bayesian framework called posterior predictive checking that is designed theoretically to detect model misspecification for observed data. However, the performance of the posterior predictive check procedure has thus far not been directly examined under different conditions of mixture model misspecification. This article addresses this task and aims to provide additional insight into whether or not posterior predictive checks can detect model misspecification within the context of Bayesian growth mixture modeling. Results indicate that this procedure can only identify mixture model misspecification under very extreme cases of misspecification.     

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders

The factor mixture model (FMM) uses a hybrid of both categorical and continuous latent variables. The FMM is a good model for the underlying structure of psychopathology because the use of both categorical and continuous latent variables allows the structure to be simultaneously categorical and dimensional. This is useful because both diagnostic class membership and the range of severity within and across diagnostic classes can be modeled concurrently. Although the conceptualization of the FMM has been explained in the literature, the use of the FMM is still not prevalent. One reason is that there is little research about how such models should be applied in practice and, once a well-fitting model is obtained, how it should be interpreted. In this article, the FMM is explored by studying a real data example on conduct disorder. By exploring this example, this article aims to explain the different formulations of the FMM, the various steps in building a FMM, and how to decide between an FMM and alternative models.     

Disentangling Shape from Level Effects in Person-Centered Analyses: An Illustration Based on University Teachers’ Multidimensional Profiles of Effectiveness

This study compares alternative ways of disentangling the effects of level (the tendency for a person to be high, medium, or low across all factors) and shape (the tendency for a person to have a distinct pattern of factors on which they are high, medium, or low) in profile analyses. This issue is particularly relevant to performance appraisals where it is often useful to identify specific strengths and weaknesses over and above a person global performance, but also to person-centered analyses more generally where the observation of qualitative (shape) differences between profiles is often used as justification for the added value of profiles. Substantively, this study illustrates these issues in the identification of profiles of teachers based on multidimensional students’ ratings of their effectiveness, using an archival data set of 31,951 class-average ratings based on the Students’ Evaluations of Educational Quality (SEEQ) instrument collected over a 13-year period. The results show the superiority of a factor mixture operationalization of teaching effectiveness in which a global effectiveness factor was used to control for unnecessary level effects in the profiles.     

Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.     

基于Matlab的有限元结构内力分析

介绍了Matlab语言的特点,详细介绍了用MATLAB语言编制结构内力分析的有限元方法,并通过实例对平面框架结构进行了内力和位移分析.     

论儒家伦理与现代奥林匹克运动

儒家伦理与现代奥林匹克运动之间，因异质因素而相互冲突，因同构因素而相互融合。在一定条件下，异质因素整合到奥林匹克运行的机制中去，遏制了奥林匹克运动发展中的消极影响，促进奥林匹克运动乃至整个社会生活的健康发展。     

三种饰钴方式对镍电极性能的影响

研究了用物理掺杂、化学镀、包覆等三种方式饰钴后的镍电极的性能.通过比较充放电曲线、循环伏安以及扫描电镜测试结果发现:以包覆钴形式对镍电极进行修饰的效果较好.     

词法简化概说

纵观英语的历史，词法简化的趋势是显而易见的。从宏观上看，语音的演变和民族的融合对词法的简化所起的作用是巨大的。但可以预测，现存的屈折形式在将来简化的可能性是微乎其微的。     

有限群为π-幂零群的特征

本文给出了有限群G为π—幂零群的一个充要条件,并利用导群给出了群G为π—幂零群的一个充分条件.     

基于FDTD方法研究一维孔径菲涅耳衍射

应用时域有限差分FDTD方法研究一维孔径菲涅耳衍射花样的基本特征．首先简介了FDTD的基本原理和实现方法,接着从理论上分析了一维孔径菲涅耳衍射花样的规律,最后给出应用FDTD方法的数值仿真结果,该结果与理论分析相吻合．