初中学生心理健康状况及其影响因素

本研究对441名初中学生进行问卷调查,结果发现：1．有7．5％的初中学生存在明显的心理健康问题。2．初中学生心理健康存在性别与年级的差异。女生比男生,初三、初二学生比初一学生有更多的心理健康问题。3．日常生活压力是影响初中学生心理健康的重要因素。4．解决问题、求助两种应付方式对学生的心理健康有积极作用,而自责、幻想、忍耐、合理化等应付方式则会危害心理健康。     

从承认理论到民主正义论——略论南希·弗雷泽的政治哲学

弗雷泽提出要以参与平等为核心重新界定批判理论的规范基础,进一步从承认理论身份模式转向地位模式;从承认的规范一元论转向包含再分配与承认的视角二元论;从局部的文化正义转向一元三维的总体的民主正义.弗雷泽的思想为弥合左派纷争,重新构建对资本主义社会的总体性批判做出了努力,反映了霍耐特承认规范一元论以后批判理论新的演进动向.     

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.