General Growth Mixture Analysis of Adolescents' Developmental Trajectories of Anxiety: The Impact of Untested Invariance Assumptions on Substantive Interpretations

Substantively, this study investigates potential heterogeneity in the developmental trajectories of anxiety in adolescence. Methodologically, this study demonstrates the usefulness of general growth mixture analysis (GGMA) in addressing these issues and illustrates the impact of untested invariance assumptions on substantive interpretations. This study relied on data from the Montreal Adolescent Depression Development Project (MADDP), a 4-year follow-up of more than 1,000 adolescents who completed the Beck Anxiety Inventory each year. GGMA models relying on different invariance assumptions were empirically compared. Each of these models converged on a 5-class solution, but yielded different substantive results. The model with class-varying variance–covariance matrices was retained as providing a better fit to the data. These results showed that although elevated levels of anxiety might fluctuate over time, they clearly do not represent a transient phenomenon. This model was then validated in relation to multiple predictors (mostly related to school violence) and outcomes (grade-point average, school dropout, depression, loneliness, and drug-related problems).     

Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) Bauer, D. J. 2005. A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling, 12: 513–535. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the service of more flexibly recovering characteristics of the latent aggregate regression function. This article develops and evaluates delta method and parametric bootstrap approaches for obtaining approximate confidence intervals for Bauer's semiparametric approach to modeling latent nonlinear functions. Coverage rates of these approximate point-wise confidence intervals or nonsimultaneous confidence bands are evaluated by Monte Carlo and recommendations for their use are suggested.     

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.     

基于邻接矩阵存储结构的二元关系的合成运算

关系的应用非常广泛.在数学领域对关系的研究主要集中在关系的性质上,很少有人研究关系的存储结构和算法.本文主要论述了二元关系的邻接矩阵存储结构和在此结构之上的关系的创建和合成运算.并利用C语言实现了该算法.最后分析了算法的复杂性.     

基于遍历序列重构二叉结构树的分析

针对如何由二叉树的遍历序列来唯一确定二叉树的问题,提出了用两种遍历序列唯一确定一棵二叉树的方法.理论分析证明,已知先序遍历和中序遍历或者已知后序遍历和中序遍历可以唯一确定一棵二叉树,但已知后序遍历和先序遍历就不能唯一确定了.文中还对用两种遍历序列唯一重构一棵二叉树算法进行了描述.     

再现二段式对学习其他曲式的参考价值

再现二段式是曲式学习中较早接触的一种小型曲式,对于这种曲式的再现部分、中句和从属部分(即引子、连接、尾声)的认识和把握,将对以后学习其他曲式具有重要的参考作用。本文通过对再现二段式结构特点的介绍,同时结合实例,探讨其对于学习三段式、三部曲式、回旋曲式和奏鸣曲式的重要参考价值。     

内部社会资本、二元学习与研发团队创造力

团队成员工作积极性不高与创造力不够是阻碍研发团队创新的最大问题,良好关系下的团队成员知识交流与思维碰撞对于研发团队创造力至关重要。以二元学习为中介变量,知识异质性为调节变量,构建了内部社会资本影响研发团队创造力的有调节的中介效应模型。采用结构方程模型,基于308个研发团队的数据的实证研究发现：结构资本和认知资本对研发团队创造力的直接影响显著,而关系资本对研发团队创造力的直接影响不显著;二元学习在结构资本及认知资本与研发团队创造力的关系中扮演部分中介角色,而在关系资本与研发团队创造力的关系中扮演完全中介角色;知识异质性正向调节二元学习中介效应。本研究有助于加强对团队创造力影响因素及作用机理的了解,也为提高研发团队创造力提供借鉴。