普通高校体育隐性课程开发及实施效果研究

采用文献资料、实验、问卷调查等方法,对中国计量学院2009级大学生体育隐性课程教学进行了实验研究,研究结果表明：体育隐性课程的开发与实施符合高校体育课程改革与发展趋势,是实现高校体育课程教学目标的有效手段之一,也是改善大学生体质健康状况,促进在校大学生心理健康发展的有效途径之一。     

Preliminary Detection of Relations Among Dynamic Processes With Two-Occasion Data

Most novel analytic methods for longitudinal data are applicable to studies spanning three time-points of data at a minimum, whereas methods for two-occasion data have garnered comparatively little attention. Here, we address this limitation by introducing the two-wave latent change score (2W-LCS) model, a technique appropriate for preliminary detection of relations among dynamic processes with two-occasion data. The 2W-LCS model is well suited for the investigation of hypotheses in which changes in a construct are posited as predictors of changes in another construct. In an empirical illustration using data of elderly Hispanics from the Health and Retirement Study, we demonstrate how the 2W-LCS model provides the best match to theories rooted in changes, and highlight the advantages of this approach over other modeling alternatives (i.e., Little, Preacher, Selig, & Card, 2007; Selig & Preacher, 2009).     

市场经济与人的全面发展

市场经济与人的全面发展相互影响，一方面，市场经济的本质在客观上要求人实现全面发展，并为人的全面发展的实现提供动力和物质条件，同时又在一定程度上制约人的全面发展；另一方面，人在市场经济中为实现自身利益的最大化，最大限度地发挥自己的潜能，力求实现人的全面发展，人的全面发展是建设有序，良性运行的市场经济的基础和保证。     

Analyzing Different Types of Moderated Method Effects in Confirmatory Factor Models for Structurally Different Methods

An extension of two confirmatory factor models for multitrait-multimethod measurement designs with structurally different methods to the analysis of latent interaction effects is presented: the nonlinear latent difference (NL-LD) model and the nonlinear correlated trait–correlated method-minus-one (NL-CTC[M – 1]) model. Both models are compared with regard to (a) the psychometric definition of the latent variables, (b) the capabilities of explaining latent method effects, and (c) the analysis of latent interaction effects. Using the latent moderated structural equation approach, we show how moderated method effects can be examined in the NL-CTC(M – 1) model. This fine-grained analysis of method effects is not feasible using the classical NL-LD model. We propose an extended version of the NL-LD model, which recovers the results of the NL-CTC(M – 1) model. The different versions of the nonlinear multimethod models are illustrated using real data from a multirater study. Finally, the advantages and challenges of incorporating latent interaction effects in complex CFA–MTMM models are discussed.     

Understanding the growth of ESL paragraph writing skills and its relationships with linguistic features

This study sought to examine the development of paragraph writing skills of 116 English as a second language university students over the course of 12 weeks and the relationship between the linguistic features of students’ written texts as measured by Coh-Metrix – a computational system for estimating textual features such as cohesion and coherence – and the scores assigned by human raters. The raters’ reliability was investigated using many-facet Rasch measurement (MFRM); the growth of students’ paragraph writing skills was explored using a factor-of-curves latent growth model (LGM); and the relationships between changes in linguistic features and writing scores across time were examined by path modelling. MFRM analysis indicates that despite several misfits, students’ and raters’ performances and scale’s functionality conformed to the expectations of MFRM, thus providing evidence of psychometric validity for the assessments. LGM shows that students’ paragraph writing skills develop steadily during the course. The Coh-Metrix indices have more predictive power before and after the course than during it, suggesting that Coh-Metrix may struggle to discriminate between some ability levels. Whether a Coh-Metrix index gains or loses predictive power over time is argued to be partly a function of whether raters maintain or lose sensitivity to the linguistic feature measured by that index in their own assessment as the course progresses.     

开发右脑潜能促进智力发展

多年来 ,人们对人脑的研究发现 ,要想提高和保持大脑机能 ,提高一个人的智力水平 ,就必须做到左右大脑的均衡发展 .鉴于目前存在的对右脑的开发不够重视且很薄弱的这一现实 ,提供一些行之有效的开发右脑的方法就显得十分重要 .我们希望通过这些方法的实施 ,以提高并保持人们的大脑机能的可塑性 .     

Mostly Harmless Direct Effects: A Comparison of Different Latent Markov Modeling Approaches

We evaluate the performance of the most common estimators of latent Markov (LM) models with covariates in the presence of direct effects of the covariates on the indicators of the LM model. In LM modeling it is common practice not to model such direct effects, ignoring the consequences that might have on the overall model fit and the parameters of interest. However, in the general literature about latent variable modeling it is well known that unmodeled direct effects can severely bias the parameter estimates of the model at hand. We evaluate how the presence of direct effects in?uences the bias and efficiency of the 3 most common estimators of LM models, the 1-step, 2-step, and 3-step approaches. Furthermore, we propose amendments (that were thus far not used in the context of LM modeling) to the 2- and 3-step approaches that make it possible to account for direct effects and eliminate bias as a consequence. This is done by modeling the (possible) direct effects in the first step of the stepwise estimation procedures. We evaluate the proposed estimators through an extensive simulation study, and illustrate them via a real data application. Our results show, first, that the augmented 2-step and 3-step approaches are unbiased and efficient estimators of LM models with direct effects. Second, ignoring the direct effects leads to biased estimates with all existing estimators, the 1-step approach being the most sensitive.     

试论我国高等教育大众化进程中的潜在风险

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过程中，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才识，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。     

Representing the Twittersphere: Archiving a representative sample of Twitter data under resource constraints

The rising popularity of social media posts, most notably Twitter posts, as a data source for social science research poses significant problems with regard to access to representative, high-quality data for analysis. Cheap, publicly available data such as that obtained from Twitter's public application programming interfaces is often of low quality, while high-quality data is expensive both financially and computationally. Moreover, data is often available only in real-time, making post-hoc analysis difficult or impossible. We propose and test a methodology for inexpensively creating an archive of Twitter data through population sampling, yielding a database that is highly representative of the targeted user population (in this test case, the entire population of Japanese-language Twitter users). Comparing the tweet volume, keywords, and topics found in our sample data set with the ground truth of Twitter's full data feed confirmed a very high degree of representativeness in the sample. We conclude that this approach yields a data set that is suitable for a wide range of post-hoc analyses, while remaining cost effective and accessible to a wide range of researchers.     

The Poor Fit of Model Fit for Selecting Number of Factors in Exploratory Factor Analysis for Scale Evaluation

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the ubiquity of correlated residuals and imperfect model specification. Our research focuses on a scale evaluation context and the performance of four standard model fit indices: root mean square error of approximate (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker–Lewis index (TLI), and two equivalence test-based model fit indices: RMSEAt and CFIt. We use Monte Carlo simulation to generate and analyze data based on a substantive example using the positive and negative affective schedule (N = 1,000). We systematically vary the number and magnitude of correlated residuals as well as nonspecific misspecification, to evaluate the impact on model fit indices in fitting a two-factor exploratory factor analysis. Our results show that all fit indices, except SRMR, are overly sensitive to correlated residuals and nonspecific error, resulting in solutions that are overfactored. SRMR performed well, consistently selecting the correct number of factors; however, previous research suggests it does not perform well with categorical data. In general, we do not recommend using model fit indices to select number of factors in a scale evaluation framework.