Integration of Stochastic Differential Equations Using Structural Equation Modeling: A Method to Facilitate Model Fitting and Pedagogy

Stochastic differential equation (SDE) models are a promising method for modeling intraindividual change and variability. Applications of SDEs in the social sciences are relatively limited, as these models present conceptual and programming challenges. This article presents a novel method for conceptualizing SDEs. This method uses structural equation modeling (SEM) conventions to simplify SDE specification, the flexibility of SEM to expand the range of SDEs that can be fit, and SEM diagram conventions to facilitate the teaching of SDE concepts. This method is a variation of latent difference scores (McArdle, 2009; McArdle & Hamagami, 2001) and the oversampling approach (Singer, 2012), and approximates the advantages of analytic methods such as the exact discrete model (Oud & Jansen, 2000) while retaining the modeling fiexibility of methods such as latent differential equation modeling (Boker, Neale, & Rausch, 2004). A simulation and empirical example are presented to illustrate that this method can be implemented on current computing hardware, produces good approximations of analytic solutions, and can flexibly accommodate novel models.     

Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration

For some time, there have been differing recommendations about how and when to include covariates in the mixture model building process. Some have advocated the inclusion of covariates after enumeration, whereas others recommend including them early on in the modeling process. These conflicting recommendations have led to inconsistent practices and unease in trusting modeling results. In an attempt to resolve this discord, we conducted a Monte Carlo simulation to examine the impact of covariate exclusion and misspecification of covariate effects on the enumeration process. We considered population and analysis models with both direct and indirect paths from the covariates to the latent class indicators. As expected, misspecified covariate effects most commonly led to the overextraction of classes. Findings suggest that the number of classes could be reliably determined using the unconditional latent class model, thus our recommendation is that class enumeration be done prior to the inclusion of covariates.     

我国高等教育大众化进程中的潜在风险探析

高等教育作为人类有目的的人力资本增值的投资活动，它给社会和受教育个人带来滞后性高经济回报的同时，也存在着潜在风险，特别是在高等教育大众化的进程中，表现得十分明显，当这种风险的“量”累积达到一定程度时，也会产生危机。借鉴国外发达国家发展的经验和教训，充分发挥我国高等教育的后发优势，成功防范和化解我国高等教育大众化进程中的潜在风险，确保我国高等教育持续协调发展，具有重要的现实意义。     

文化追寻与生命体验的双重构建--阿城《棋王》的另一种读法

本文试图从与以往研究者不同的角度进入《棋王》，通过对小说的细读和心理分析，我们可以体味到作品不仅表现了道家文化，它又是儒道互渗、互补的统一体。同时，阿城的心灵体验无意识中融入了作品，这使得《棋王》在实际上还存在着另一个潜在的主角“我”，以及另一层潜在结构。     

Multilevel Rasch Modeling: Does Misfit to the Rasch Model Impact the Regression Model?

Abstract

Multilevel Rasch models are increasingly used to estimate the relationships between test scores and student and school factors. Response data were generated to follow one-, two-, and three-parameter logistic (1PL, 2PL, 3PL) models, but the Rasch model was used to estimate the latent regression parameters. When the response functions followed 2PL or 3PL models, the proportion of variance explained in test scores by the simulated student or school predictors was estimated accurately with a Rasch model. Proportion of variance within and between schools was also estimated accurately. The regression coefficients were misestimated unless they were rescaled out of logit units. However, item-level parameters, such as DIF effects, were biased when the Rasch model was violated, similar to single-level models.     

Evaluation of Multicomponent Measuring Instrument Reliability in Complex Design Studies

A latent variable modeling approach to evaluation of scale reliability in complex design studies is outlined. The procedure is readily applicable in empirical research for the purpose of point and interval estimation of reliability of multicomponent measuring instruments in the presence of probability sampling and possible nesting within higher order units. The method can be used to aid scale construction and development efforts in large-scale studies of substantially heterogeneous populations. The described approach is illustrated with data from an international educational survey.     

医院会计信息系统安全风险的防范

医院会计信息系统是医院管理信息系统中最重要的子系统，具有完整性、复杂性和重要性三个特征。在诸多因素的影响下，会计信息系统存在着很多潜在的安全风险。因此，必须对其安全风险的防范引起足够的重视。     

The predictors of academic interest: fluid intelligence,openness, and their interaction

The importance of academic interest has been emphasised in education, both as a construct influencing achievement, and as an important learning outcome. However, psychological predictors such as personality and intelligence that characterise students who become interested in coursework are often neglected. The present study examined the influences of fluid intelligence (Gf) and Openness on academic interest in a large sample of Chinese secondary school students (N?=?836). Latent variable models found that controlling for age and gender, Openness significantly predicted interest in Chinese, mathematics, and English, whereas Gf only significantly predicted Chinese interest. Beyond the main effects, Gf and Openness also had an interaction effect on academic interest but only for Chinese. The findings represent an important contribution to understanding the psychological predictors of academic interest, although the effect sizes were relatively small.     

Longitudinal Measurement Non-Invariance with Ordered-Categorical Indicators: How are the Parameters in Second-Order Latent Linear Growth Models Affected?

Models of change typically assume longitudinal measurement invariance. Key constructs are often measured by ordered-categorical indicators (e.g., Likert scale items). If tests based on such indicators do not support longitudinal measurement invariance, it would be useful to gauge the practical significance of the detected non-invariance. The authors focus on the commonly used second-order latent growth curve model, proposing a sensitivity analysis that compares the growth parameter estimates from a model assuming the highest achieved level of measurement invariance to those from a model assuming a higher, incorrect level of measurement invariance as a measure of practical significance. A simulation study investigated the practical significance of non-invariance in different locations (loadings, thresholds, uniquenesses) in second-order latent linear growth models. The mean linear slope was affected by non-invariance in the loadings and thresholds, the intercept variance was affected by non-invariance in the uniquenesses, and the linear slope variance and intercept–slope covariance were affected by non-invariance in all three locations.     

Application of Latent Growth Curve Analysis With Categorical Responses in Social Behavioral Research

Latent growth modeling allows social behavioral researchers to investigate within-person change and between-person differences in within-person change. Typically, conventional latent growth curve models are applied to continuous variables, where the residuals are assumed to be normally distributed, whereas categorical variables (i.e., binary and ordinal variables), which do not hold to normal distribution assumptions, have rarely been used. This article describes the latent growth curve model with categorical variables, and illustrates applications using Mplus software that are applicable to social behavioral research. The illustrations use marital instability data from the Iowa Youth and Family Project. We close with recommendations for the specification and parameterization of growth models that use both logit and probit link functions.