First- Versus Second-Order Latent Growth Curve Models: Some Insights From Latent State-Trait Theory

First-order latent growth curve models (FGMs) estimate change based on a single observed variable and are widely used in longitudinal research. Despite significant advantages, second-order latent growth curve models (SGMs), which use multiple indicators, are rarely used in practice, and not all aspects of these models are widely understood. In this article, our goal is to contribute to a better understanding of theoretical and practical differences between FGMs and SGMs. We define the latent variables in FGMs and SGMs explicitly on the basis of latent state–trait (LST) theory and discuss insights that arise from this approach. We show that FGMs imply a strict trait-like conception of the construct under study, whereas SGMs allow for both trait and state components. Based on a simulation study and empirical applications to the Center for Epidemiological Studies Depression Scale (Radloff, 1977 Radloff, L. S. 1977. The CES–D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1: 385–401. [Crossref], [Web of Science ®] , [Google Scholar]) we illustrate that, as an important practical consequence, FGMs yield biased reliability estimates whenever constructs contain state components, whereas reliability estimates based on SGMs were found to be accurate. Implications of the state–trait distinction for the measurement of change via latent growth curve models are discussed.     

Prediction from Latent Classes: A Demonstration of Different Approaches to Include Distal Outcomes in Mixture Models

Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight in understanding the population heterogeneity embodied by a latent class variable. The model building process regarding how to include predictors/correlates and outcomes of the latent class variables into mixture models is an area of active research. As such, new methods of including these variables continue to emerge and best practices for the application of these methods in real data settings (including simple guidelines for choosing amongst them) are still not well established. This paper focuses on one type of auxiliary variable—distal outcomes—providing an overview of the methods currently available for estimating the effects of latent class membership on subsequent distal outcomes. We illustrate the recommended methods in the software packages Mplus and Latent Gold using a latent class model to capture population heterogeneity in students’ mathematics attitudes, linking latent class membership to two distal outcomes.     

Detecting Mixtures From Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) × 3 (exogenous latent mean difference) × 3 (endogenous latent mean difference) × 3 (correlation between factors) × 3 (mixture proportions) factorial design. In addition, the efficacy of several likelihood-based statistics (Akaike's Information Criterion [AIC], Bayesian Information Ctriterion [BIC], the sample-size adjusted BIC [ssBIC], the consistent AIC [CAIC], the Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test [aVLMR]), classification-based statistics (CLC [classification likelihood information criterion], ICL-BIC [integrated classification likelihood], normalized entropy criterion [NEC], entropy), and distributional statistics (multivariate skew and kurtosis test) were examined to determine which statistics best recover the correct number of components. Results indicate that the structural parameters were recovered, but the model fit statistics were not exceedingly accurate. The ssBIC statistic was the most accurate statistic, and the CLC, ICL-BIC, and aVLMR showed limited utility. However, none of these statistics were accurate for small samples (n = 500).     

A metric measure for direct comparison of competing models in covariance structure analysis

Comparing the fit of alternative models has become a standard procedure for analyzing covariance structure analysis. Comparison of alternative models is typically accomplished by examining the fit of each model to sample data. It is argued that rather than using this indirect approach, one should do direct comparisons of the similarities and differences among competing models. It is shown that among the existing good‐ness‐of‐fit indexes, the root mean square residual (RMSR) is the only one that can be used for this purpose. However, the RMSR fails to satisfy some important statistical desiderata. Rao's Distance (RD), an alternate measure, is shown to overcome this limitation of RMSR. The preference for RD over RMSR for model comparisons is illustrated through a detailed analysis of a particular sample of multitrait‐multimethod data. A simulation study conducted to empirically investigate the sampling behavior of RD reveals that the true orderings of intermodel proximities are recovered (on average) with a fair degree of accuracy.     

An SEM Approach for the Evaluation of Intervention Effects Using Pre-Post-Post Designs

A directly applicable latent variable modeling procedure for classical item analysis is outlined. The method allows one to point and interval estimate item difficulty, item correlations, and item-total correlations for composites consisting of categorical items. The approach is readily employed in empirical research and as a by-product permits examining the latent structure of tentative versions of multiple-component measuring instruments. The discussed procedure is straightforwardly utilized with the increasingly popular latent variable modeling software Mplus, and is illustrated on a numerical example.     

A Textbook Score Card

ABSTRACT

Utilizing latent transition analysis and multidimensional scaling growth analysis, the authors studied the emerging developmental trajectories in word literacy (i.e., word-reading competence) of a group of 1,503 kindergarteners. Specifically, 3 hypotheses with respect to growth patterns in word literacy from kindergarten to Grade 2 were examined: (a) children come into kindergarten with different word literacy levels, and emerging differences would be likely to remain stable over time; (b) the differences in word literacy latent status would lead to differences in word literacy trajectories over time; and (c) students with a low growth level would lead to lower achievement in reading achievement at a later time. The results of the dynamic analyses support the hypotheses and are discussed in the context of word literacy development.     

A Study in Reading Diagnosis

ABSTRACT

The author explored the developmental courses of deep learning approach and critical thinking over a 2-year period. Latent growth curve modeling (LGM) procedures were used to test and trace the trajectories of both theoretical frameworks over time. Participants were 264 (119 women, 145 men) university undergraduates. The Deep Learning subscale of Biggs's (1987) Study Process Questionnaire and the Critical Thinking subscale of the Reflective Thinking Questionnaire (Kember et al., 2000) were administered to the participants across four waves of data collection. Results of the LGM analyses indicated the growth of change of deep learning approach increased over time, whereas critical thinking practice decreased. Further multivariate growth curve analysis revealed an interactive, dynamic association between the intercept of critical thinking and the slope of deep learning approach. This evidence supports previous research findings, indicating that critical thinking may serve as an informational source in students’ engagement in deep learning approach.     

太极拳劲力发放过程的运动学分析

太极拳劲力的发放过程是以弧线的方式在旋转,不直接对抗用力,整个用力过程在不断地动态调整,不断变化方向。这种劲力的发放能调动各肌肉协调发力,使得肌肉发力的同步性、均衡性较好。     

《中国当代文学》课程中的人文教育

文学是人文精神的载体,人文精神是文学的灵魂。当代文学课程因其现时性和形象性等特征,更容易为学生所接受,因而可利用其资源,对学生进行隐性思想道德教育、生命教育和审美教育,使他们既关注政治局势、经济形势等现象世界,又追求知识、价值、信仰等形而上的精神世界;既关注本人、本国、本民族的利益和前途,又关心整个人类的精神状态、价值范式等,最终造就他们美满的人生乃至和谐的社会。     

零度和偏离:语言本体认识的新视野

零度和偏离既是具有普遍意义的语言哲学范畴 ,又是具有经验性和现实性的操作范式 ,它们对语言本体的阐释 ,是全息的、开放的 ,它们相互依存、相互转化 ,构成一个视野开阔的语言本体认知的价值系统