心理契约破坏研究现状与展望

心理契约破坏（psychologicalcontractbreach）是雇员对于组织未能充分履行心理契约中承诺责任的主观感知。文章阐述了心理契约破坏的概念界定、它产生的主要原因及其后果,以及相关的跨文化研究,并重点介绍了心理契约破坏对员工工作态度和行为产生影响过程中的相关权变因素（中介变量和缓冲变量）。在此基础上进一步提出未来研究应着重以下几方面：（1）心理契约破坏的心理机制及干预措施研究;（2）跨文化比较研究;（3）个体间差异的研究;（4）研究方法的改进     

Structural Social Capital,Household Income and Life Satisfaction: The Evidence from Beijing,Shanghai and Guangdong-Province,China

Although the positive associations between social capital, household income and life satisfaction are well documented, little is known about how income actually shapes the relation between structural social capital and life satisfaction. This study investigates the roles of economic condition in determining the relationships between life satisfaction and three aspects of structural social capital, namely network size, neighborhood interaction and social participation. This study uses a random sample of 6002 adults nested in 2365 families from three regions in China: Beijing, Shanghai and Guangdong-province and applies multilevel linear regression models. The results firstly show that household income and structural social capital are positively associated with life satisfaction. Further analyses on the interaction effects find that household income substantively reduces the association between a social network size and life satisfaction but increases the relationship between social participation and life satisfaction. In addition, the differences between families explain about 25 % unexplained variances of life satisfaction.     

Non-Compliance with On-Site Data Collection in Outdoor Recreation Monitoring

ABSTRACT

A wide range of methods exists for on-site visitor monitoring in parks and recreation areas. Self-registration methods have proven to be popular because of their low cost and relative ease of administration, but little is known about the extent to which the data collected from self-registration boxes are representative of the population of visitors, and the degree that bias exists as a result of non-compliance. This article examines these concerns based on research at Fulufjället National Park in Sweden. On-site registration card and follow-up mail survey data from a sample of visitors who did not voluntarily register were compared with the same kind of data for visitors who did register voluntarily. In total, 10 registration card items and 284 mail survey items were tested for variations between compliant and non-compliant visitors. Of these, one third of the card items, 12% and 3% of the survey items for Swedish and German visitors, respectively, yielded statistically significant differences. Implications for management and suggestions for further research are discussed.     

On the Model-Based Bootstrap With Missing Data: Obtaining a P-Value for a Test of Exact Fit

Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) Beran, R. and Srivastava, M. S. 1985. Bootstrap tests and confidence regions for functions of a covariance matrix. The Annals of Statistics, 13: 95–115. [Crossref], [Web of Science ®] , [Google Scholar] for general covariance structure models and applied to structural equation modeling by Bollen and Stine (1992) Bollen, K. A. and Stine, R. A. 1992. Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods and Research, 21: 205–229. [Crossref], [Web of Science ®] , [Google Scholar]. An extension of this transformation to missing data was presented by Enders (2002) Enders, C. K. 2002. Applying the Bollen-Stine bootstrap for goodness-of-fit measures to structural equation models with missing data. Multivariate Behavioral Research, 37: 359–377. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], but it is an approximate and not an exact solution, with the degree of approximation unknown. In this article, we provide several approaches to obtaining an exact solution. First, an explicit solution for the special case when the sample covariance matrix within each missing data pattern is invertible is given. Second, 2 iterative algorithms are described for obtaining an exact solution in the general case. We evaluate the rejection rates of the bootstrapped likelihood ratio statistic obtained via the new procedures in a Monte Carlo study. Our main finding is that model-based bootstrap with incomplete data performs quite well across a variety of distributional conditions, missing data mechanisms, and proportions of missing data. We illustrate our new procedures using empirical data on 26 cognitive ability measures in junior high students, published in Holzinger and Swineford (1939) Holzinger, K. J. and Swineford, F. 1939. A study in factor analysis: The stability of a bi-factor solution. Supplementary Educational Monographs, 48: 1–91.  [Google Scholar].     

Bias and Efficiency in Structural Equation Modeling: Maximum Likelihood Versus Robust Methods

In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data, in practice, can make the ML estimator (MLE) biased and inefficient. In addition to ML, robust methods have also been developed, which are designed to minimize the effects of outlying cases. But the properties of robust estimates and their standard errors (SEs) have never been systematically studied. This article studies two robust methods and compares them against the ML method with respect to bias and efficiency using a confirmatory factor model. Simulation results show that robust methods lead to results comparable with ML when data are normally distributed. When data have heavy tails or outlying cases, robust methods lead to less biased and more efficient estimators than MLEs. A formula to obtain consistent SEs for one of the robust methods is also developed. The formula-based SEs for both robust estimators match the empirical SEs very well with medium-size samples. A sample of the Cross Racial Identity Scale with a 6-factor model is used for illustration. Results also confirm conclusions of the simulation study.     

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices.     

Ridge structural equation modelling with correlation matrices for ordinal and continuous data

This paper develops a ridge procedure for structural equation modelling (SEM) with ordinal and continuous data by modelling the polychoric/polyserial/product‐moment correlation matrix R . Rather than directly fitting R , the procedure fits a structural model to R _a= R +a I by minimizing the normal distribution‐based discrepancy function, where a > 0. Statistical properties of the parameter estimates are obtained. Four statistics for overall model evaluation are proposed. Empirical results indicate that the ridge procedure for SEM with ordinal data has better convergence rate, smaller bias, smaller mean square error, and better overall model evaluation than the widely used maximum likelihood procedure.     

试论如何运用系统论原理认识细胞

运用系统论的原理分析了细胞的形态、结构与功能.细胞是生物体结构和功能的基本单位,具有多样性和统一性.细胞系统是复杂的物质系统,具有层次性、整体性、动态性及开放性等特征.应用唯物辩证法系统论的原理可以对细胞系统做出更新、更全面的诠释.     

小学生自我调节学习的特点

本文采用学业自我调节问卷,探讨了小学四、五、六年级学生自我调节学习的特点。结果表明,不同年级小学生在自我调节学习的外部调节、摄入性调节、认同调节和内部动机维度上存在差异;不同调节类型对学习成绩有影响;不同年级、不同调节水平对数学成绩有显著影响,在语文成绩上未达显著;最后,本文对于该方向的未来研究给予了展望。