Bayesian Quantile Structural Equation Models

Structural equation modeling is a common multivariate technique for the assessment of the interrelationships among latent variables. Structural equation models have been extensively applied to behavioral, medical, and social sciences. Basic structural equation models consist of a measurement equation for characterizing latent variables through multiple observed variables and a mean regression-type structural equation for investigating how explanatory latent variables influence outcomes of interest. However, the conventional structural equation does not provide a comprehensive analysis of the relationship between latent variables. In this article, we introduce the quantile regression method into structural equation models to assess the conditional quantile of the outcome latent variable given the explanatory latent variables and covariates. The estimation is conducted in a Bayesian framework with Markov Chain Monte Carlo algorithm. The posterior inference is performed with the help of asymmetric Laplace distribution. A simulation shows that the proposed method performs satisfactorily. An application to a study of chronic kidney disease is presented.     

Modeling Predictors of Latent Classes in Regression Mixture Models

The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students’ academic achievement outcome. Implications of the study are discussed.     

生料粉磨配合系统研究

生料粉磨是水泥生产的主要工序之一，而球磨机与选粉机如何配合使用，将决定粉磨机的台时产量。     

群体活动与社会舆论之互动——以1905年前后的社会思潮为背景

清末两大社会群体——清朝统治集团和知识分子在1905年前后复杂的社会思潮背景中，分别在自身力所能及的范围内展开活动，力图制造、控制社会舆论。而从他们的活动与社会舆论的互动关系中，又可观察到当时社会思潮隐与显的不同面相，从而更为深入地理解政治和思想的交织与缠绕。     

Automated Latent Growth Curve Model Fitting: A Segmentation and Knot Selection Approach

Latent growth curve models are widely used in the social and behavioral sciences to study complex developmental patterns of change over time. The trajectories of these developmental patterns frequently exhibit distinct segments in the studied variables. Latent growth models with piecewise functions for repeated measurements of variables have become increasingly popular for modeling such developmental trajectories. A major problem with using piecewise models is determining the precise location of the point where the change in the process has occurred and uncovering the related number of segments. The purpose of this paper is to introduce an optimization procedure that can be used to determine both the segments and location of the knots in piecewise linear latent growth models. The procedure is illustrated using empirical data in order to detect the number of segments and change points. The results demonstrate the capabilities of the procedure for fitting latent growth curve models.     

Testing Criterion Correlations With Scale Component Measurement Errors Using Latent Variable Modeling

A latent variable modeling method for testing criterion correlations with measurement error terms in multicomponent measuring instruments is outlined. The approach is based on an application of the Benjamini–Hochberg multiple testing procedure and can be used when assumptions of validity estimation related procedures need to be examined. The method also allows studying the extent to which criterion validity coefficients might be due to the relationship between a presumed underlying latent construct evaluated by a psychometric scale and a criterion variable, or could be a consequence of the relation between measurement error in the overall scale score and the criterion. The discussed procedure is widely applicable with popular latent variable modeling software, and is illustrated using a numerical example.     

MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

MplusAutomation is a package for R that facilitates complex latent variable analyses in Mplus involving comparisons among many models and parameters. More specifically, MplusAutomation provides tools to accomplish 3 objectives: to create and manage Mplus syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of MplusAutomation is to support methodological developments in structural equation modeling using Mplus.     

汉语话语中的时间表达

时间是语句构成中一个不可或缺的要素。在连贯性的话语中，时间的表达除了具有单个语句的特征之外，还存在一个语句之间组合衔接及其相互关系的问题。从《骆驼祥子》整个本来考察，汉语话语中时间关系的表达有“潜性”和“显性”两大方法，前主要依靠语序；而后则有三种主要类型：时间对照型、时间连续型以及时间统辖型，而每一种类型都有其产生的理据与特定意义。     

潜能差异性与环境层次性在个性发展中的交互作用

有关教育和个体教育的论述，多离不开人的潜能问题。对人的潜能，人本主义进行了比较多的论述，但其论述仅停留于对潜能同一性的认识，事实上，潜能的差异性不仅构成了个体最初不同的内部状态，而且在人的一生中都持续地发挥着作用，个体就是在这种差异潜能和环境的交互作用中发展起来的。这种交互作用使个体发展和社会发展构成了交互影响的统一体，这一点应引起我们教育和管理工作的高度重视。     

Discrete Time Survival Analysis Via Latent Variable Modeling: A Note on Lagged Depression Links to Stroke in Middle and Late Life

This article is concerned with a latent variable modeling approach to discrete time survival analysis that includes both time-invariant and time-varying covariates. The approach is illustrated with data from the Health and Retirement Study, which are utilized to study further the relationship of depression to stroke in middle and late life. Employing lag-1 depression scores as time-varying covariates, in addition to a set of relevant medical and demographic variables as time-invariant covariates collected at baseline, the article addresses a particular aspect of the prominent vascular depression hypothesis representing an important area in aging research, gerontology, geriatrics, and medicine. The results indicate considerable links of immediately prior depression levels to subsequent occurrences of stroke in middle-aged and older adults. The findings complement those reported by Raykov, Gorelick, Zajacova, and Marcoulides (2017), and are consistent with that hypothesis implying depression as a potential warning sign of an impending stroke.