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.     

马克思主义哲学:我们时代的真理和良心——纪念马克思逝世130周年

一个伟大的哲学家、思想家逝世之后,对他的观点、思想和学说进行持续性研究在人类思想史不乏先例。但是,像马克思主义哲学这样在世界范围内引起如此广泛、深入而持久的研究却是罕见的。更重要的是,每当出现重大历史事件,每当历史处于转折关头,人们都不由自主地把目光转向马克思,并对马克思主义哲学进行新的研究。当然,我们注意到,在对马克思主义哲学不同维度、不同层     

品牌印象结构及其测量研究

品牌印象是消费者基于对品牌信息的感知和体验而在头脑中留下的品牌部分或整体信息。在文献研究和关键事件访谈的基础上,通过筛选条目、形成初步问卷、预测验和正式测验等程序,经探索性因素分析和验证性因素分析,重测信度、聚合效度、辨别效度、法则效度和效标关联效度等检验,发现全方位的品牌印象由品牌质量印象、品牌传播印象、品牌服务印象和品牌价格印象等四个维度构成。用于测量品牌印象的多维量表、各维单项目量表和单项目量表均具有较好的信度和效度。     

Right Hemispheric Dominance of Creative Insight: An Event-Related Potential Study

The purpose of this study was to investigate the hemispheric effect of creative insight. This study used high-density ERPs to record participants' brain activity while they performed an insight task. Results showed that both insight solutions and incomprehension solutions elicited a more negative ERP deflection (N320～550) than noninsight solutions in the time window of 320～550 msec. Scalp ERPs and topographical maps revealed that the insight N320～550 exhibited a relative RH dominance, whereas the incomprehension N320～550 exhibited a relative midline dominance. Dipole source analysis showed that the generator of N320～550 elicited by insight and incomprehension solutions was localized in the right middle frontal gyrus and the right parahippocampus gyrus, respectively. These distinct spatiotemporal patterns involved in insight processing and incomprehension processing suggest that the observed N320～550 might reflect the processing of set-shift and the formation of novel associations. Moreover, the spatiotemporal pattern of brain activation associated with insight solutions supported the idea that the right hemispheric dominance theory of creative thinking also applies to creative insight.     

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].     

Abstract: Local Influence and Robust Methods for Mediation Models

Mediation analysis investigates how certain variables mediate the effect of predictors on outcome variables. Existing studies of mediation models have been limited to normal theory maximum likelihood (ML) or least squares with normally distributed data. Because real data in the social and behavioral sciences are seldom normally distributed and often contain outliers, classical methods can result in biased and inefficient estimates, which lead to inaccurate or unreliable test of the meditated effect. The authors propose two approaches for better mediation analysis. One is to identify cases that strongly affect test results of mediation using local influence methods and robust methods. The other is to use robust methods for parameter estimation, and then test the mediated effect based on the robust estimates. Analytic details of both local influence and robust methods particular for mediation models were provided and one real data example was given. We first used local influence and robust methods to identify influential cases. Then, for the original data and the data with the identified influential cases removed, the mediated effect was tested using two estimation methods: normal theory ML and the robust method, crossing two tests of mediation: the Sobel (1982) Sobel, M. E. 1982. “Asymptotic confidence intervals for indirect effects in structural equation models”. In Sociological methodology, Edited by: Leinhardt, S. 290–312. Washington, DC: American Sociological Association. [Crossref] , [Google Scholar] test using information-based standard error (z _I ) and sandwich-type standard error (z _SW ). Results show that local influence and robust methods rank the influence of cases similarly, while the robust method is more objective. The widely used z _I statistic is inflated when the distribution is heavy-tailed. Compared to normal theory ML, the robust method provides estimates with smaller standard errors and more reliable test.     

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.     

干扰任务对说谎影响的研究

摘要:本文将干扰任务对记忆的影响同测谎结合起来,试图运用这一方法来对求职者的真实学历和虚假学历进行区分。实验采用模拟面试的方式,对被试在有或无干扰任务条件下进行两次提问,结果发现,四种任务条件对被试真实经历和虚假经历都具有区分度,其中双双条件的区分度是最高的。并且,被试在双双任务条件下虚假经历的得分显著低于其他三种任务条件。