服务失败下消费者的消极情绪反应模式及企业补救对策

回顾了服务失败下消费者情绪产生的心理机制,主要有四个模型;梳理了影响消费者情绪产生的影响因素;分析了服务失败下消费者情绪反应的两种模式,进而指出服务失败后如何有效管理消费者情绪．     

Introduction to Structural Equation Modeling: Issues and Practical Considerations

Structural equation modeling (SEM) is a versatile statistical modeling tool. Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies. General steps of SEM are discussed along with important considerations in each step. Simple examples are provided to illustrate some of the ideas for beginners. In addition, several popular specialized SEM software programs are briefly discussed with regard to their features and availability. The intent of this module is to focus on foundational issues to inform readers of the potentials as well as the limitations of SEM. Interested readers are encouraged to consult additional references for advanced model types and more application examples.     

Revisiting the Model Size Effect in Structural Equation Modeling

Fitting a large structural equation modeling (SEM) model with moderate to small sample sizes results in an inflated Type I error rate for the likelihood ratio test statistic under the chi-square reference distribution, known as the model size effect. In this article, we show that the number of observed variables (p) and the number of free parameters (q) have unique effects on the Type I error rate of the likelihood ratio test statistic. In addition, the effects of p and q cannot be fully explained using degrees of freedom (df). We also evaluated the performance of 4 correctional methods for the model size effect, including Bartlett’s (1950), Swain’s (1975), and Yuan’s (2005) corrected statistics, and Yuan, Tian, and Yanagihara’s (2015) empirically corrected statistic. We found that Yuan et al.’s (2015) empirically corrected statistic generally yields the best performance in controlling the Type I error rate when fitting large SEM models.     

结构方程模型和IRT等级反应模型在人格量表项目筛选中的对比研究

为比较结构方程模型和 IRT等级反应模型在人格量表项目筛选上的作用,以《中国大学生人格量表》的7229个实际测量数据为基础,针对因素二“爽直”分别以Lisrel8.70和Multilog7.03进行结构方程模型和等级反应模型的参数估计与拟合,比较两种方法的项目筛选结果.二者统计结果均认为项目5、6、7、8拟合度不佳,在结构方程模型上表现为因子负荷较低,整体拟合指数不理想;在等级反应模型上表现为区分度参数和位置参数不理想,相关项目的特征曲线和信息曲线形态较差.但结构方程模型倾向于项目6、8更差,而等级反应模型则倾向于项目5、6更差.结构方程模型和 IRT等级反应模型对人格量表项目的统计推断结果从总体上讲是一致的,但在个别项目上略有差异.二者各有优势,可以结合使用.     

Perceived Academic Control and Failure in College students: A Three-Year Study of Scholastic Attainment

Although self-initiative is recognized as instrumental to success in college, some students do not take responsibility for their academic development and fail to make the transition from high school to college. This problem is exacerbated when bright, highly skilled students drop courses or quit college entirely. Research into this paradox of failure reveals that, although high academic control benefits learning-related emotions, cognitions, motivation, and performance, it is not sufficient to ensure optimal success. Along with academic control other factors are implicated in the paradox. In this 3-year longitudinal study, four groups of students who differed in academic control (low, high) and failure preoccupation (low, high) were tracked using broad indices of scholastic development. Overall, students higher in academic control obtained better 3-year GPAs and withdrew from fewer courses. More notable, however, high-academic-control students who were concerned about failure had better 3-year GPAs and also were less likely to withdraw from courses or quit university than the other three groups. Paradoxically, high-academic-control students who were less concerned about failure did poorly. In qualifying the assumption that more perceived control is always better, these results are interpreted following social cognition theory.     

Bayesian Data-Model Fit Assessment for Structural Equation Modeling

Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes and illustrates key features of Bayesian approaches to model diagnostics and assessing data–model fit of structural equation models, discussing their merits relative to traditional procedures.     

专业学位研究生英语自主性教学模式探索——基于结构方程建模的实证研究

专业学位研究生英语教学尚属外语教学领域的新生事物。运用结构方程建模的研究结果显示：不同类型的专业学位研究生在英语能力、学习行为和课程需求方面存在较显著的分化。西安交通大学在一项历时3年的教改实验中,采用基于自主性学习理念的立体化课程设置和教学模式,适合专业学位硕士学时较短、突出应用的特点,为专业学位研究生英语教学探索出了一条新的道路。     

结构方程模型在公共部门公众满意度测评中的应用

随着政府绩效评价工作的深入开展,公共部门公众满意度测评研究受到越来越多的关注。公众满意度测评涉及到对公众期望等较难直接测量的潜变量之间关系的分析,这对用于测评的工具提出了较高的要求,而结构方程模型正是能够满足这类要求的多元数据分析工具。由于用于私人部门的顾客满意度模型在经过了大量的调研实践和反复的数据验证基础上已经形成了较为完善的指标体系,所以公众满意度的结构方程模型可以在此基础上结合公共产品/服务的特点加以改进而形成,在运用中还要注意构建模型要有正确的理论基础等问题。     

A Structural Model of Educational Aspirations

The purpose of this study was to develop and test a model to predict educational aspirations of Canadian adolescents. Participants were a national sample of 4,034 students from grades 8–13 (2,037 males, 1,973 females). Results of a modified structural model included three sets of influences: a) a background factor comprised of parental occupation and education; b) a family involvement factor consisting of parental personal and school-based involvement with adolescents; and c) a personal factor with school marks, school and course perceptions, extracurricular reading and parental educational expectations as indicator measures. Educational aspirations was the main outcome variable. Results indicated that the personal factor had a strong direct influence on educational aspirations ( = 1.17, p < .001, R² = .76). The effects of the background and family involvement factors on educational aspirations were mediated through the personal factor. Additional analyses performed in order to test the relationships obtained in the model, revealed several significant interactions amongst the three predictor factors and educational aspirations. The findings emphasize the importance of efforts to enhance the educational aspirations of adolescents through targeted change of modifiable environmental and personal factors.     

Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables

Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist.     

Digital ITEMS Module 2: Scale Reliability in Structural Equation Modeling

In this ITEMS module, we frame the topic of scale reliability within a confirmatory factor analysis and structural equation modeling (SEM) context and address some of the limitations of Cronbach's α. This modeling approach has two major advantages: (1) it allows researchers to make explicit the relation between their items and the latent variables representing the constructs those items intend to measure, and (2) it facilitates a more principled and formal practice of scale reliability evaluation. Specifically, we begin the module by discussing key conceptual and statistical foundations of the classical test theory model and then framing it within an SEM context; we do so first with a single item and then expand this approach to a multi‐item scale. This allows us to set the stage for presenting different measurement structures that might underlie a scale and, more importantly, for assessing and comparing those structures formally within the SEM context. We then make explicit the connection between measurement model parameters and different measures of reliability, emphasizing the challenges and benefits of key measures while ultimately endorsing the flexible McDonald's ω over Cronbach's α. We then demonstrate how to estimate key measures in both a commercial software program (Mplus) and three packages within an open‐source environment (R). In closing, we make recommendations for practitioners about best practices in reliability estimation based on the ideas presented in the module.     

Testing Time-Invariance of Variable Specificity in Repeated Measure Designs Using Structural Equation Modeling

A structural equation modeling method for examining time-invariance of variable specificity in longitudinal studies with multiple measures is outlined, which is developed within a confirmatory factor-analytic framework. The approach represents a likelihood ratio test for the hypothesis of stability in the specificity part of the residual term associated with repeated administration of each measure. The procedure can be used in the search for parsimonious versions of multiwave multiple-indicator models, to test for variable specificity in them, and to examine assumptions underlying particular parameter estimation procedures in repeated measure designs. The outlined method is illustrated with empirical data.     

基于结构方程的开放大学网上学习满意度研究

如何度量网上学习的满意度水平,已成为开放大学建设过程中亟待解决的问题。本文将结构方程模型方法应用于开放大学满意度研究,构建了满意度模型,利用结构方程分析软件对学生网上学习数据进行了模型验证分析和路径分析,研究潜变量及各变量间的相关性,并依据实证分析得出研究结论,提出改进建议。     

从认知角度透视大学英语网络课程论坛交互学习的“情绪化”

良好的学业情绪对网络课程论坛的使用效果具有积极的作用。基于目前学业情绪的研究多是局限于传统课堂的师生关系,该研究借鉴Garrison等人提出的认知性存在维度来分析大学生在英语网络课程论坛中的交互,以此探究了大学生在英语网络课程论坛交互学习中所产生的学业情绪现状,探讨了其内在的原因及应对措施。     

Investigating the Multidimensionality of Need Fulfillment: A Bifactor Exploratory Structural Equation Modeling Representation

This article assesses the multidimensionality of the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) using bifactor exploratory structural equation modeling (bifactor ESEM). The first study relies on a sample of community adults (N = 2,301), and revealed the superiority of a bifactor ESEM representation, supporting the 6-factor structure of BPNSFS ratings, and the presence of a single continuum of need fulfillment relative to 2 distinct dimensions reflecting need satisfaction and frustration. These results were replicated in a second representative sample of the Hungarian adult population (N = 504), as well as across gender, and found no evidence of differential item functioning as a function of age. Relative to males, females presented higher levels of relatedness satisfaction and lower levels of competence satisfaction. Finally, autonomy frustration decreased with age, whereas competence satisfaction and frustration presented opposite curvilinear tendencies, showing that the fulfillment of this need increased sharply for younger participants, a tendency that became less pronounced with age.     

教师教学效能、应对效能与职业枯竭的结构模型研究

教师是职业枯竭的高发人群,探究高中阶段教师职业枯竭的现状与内在作用机制,进而研究应对方法,对提高我国基础教育的质量有重要意义。我们对29所高中阶段学校763名教师进行问卷调查,并建构教师教学效能、应对效能和职业枯竭的结构方程模型,发现教学效能主要以应对效能为中介作用于职业枯竭,同时直接作用于职业枯竭中的非人性化因子;应对效能区别于教学效能,直接作用于职业枯竭。提高教师的教学效能和应对效能,有利于修正教师的职业枯竭。     

五年制高职创业教育项目效果测评——基于结构方程模型的实证

当前,我国教育界已逐渐认识到创业教育的重要性,越来越多的本科院校和高职院校开设了创业教育课程,通过对创业教育项目实施效果的测评可以了解创业教育对于创业行为的推动作用。基于计划行为理论视角,使用结构方程模型的方法实证检验了某五年制高职院校创业教育项目的实施效果,结果显示:创业教育在培育个人创业意识及创业技能方面具有一定影响。     

Sample Invariance of the Structural Equation Model and the Item Response Model: A Case Study

The sample invariance of item discrimination statistics is evaluated in this case study using real data. The hypothesized superiority of the item response model (IRM) is tested against structural equation modeling (SEM) for responses to the Center for Epidemiologic Studies-Depression (CES-D) scale. Responses from 10 random samples of 500 people were drawn from a base sample of 6,621 participants across gender, age, and different health groups. Hierarchical tests of multiple-group structural equation models indicated statistically significant differences exist in item regressions across contrast groups. Although the IRM item discrimination estimates were most stable in all conditions of this case study, additional research on the precision of individual scores and possible item bias is required to support the validity of either model for scoring the CES-D. The SEM approach to examining between-group differences holds promise for any field where heterogeneous populations are assessed and important consequences arise from score interpretations.     

Selecting Path Models in SEM: A Comparison of Model Selection Criteria

Model comparison is one useful approach in applications of structural equation modeling. Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC) are commonly used for selecting an optimal model from the alternatives. We conducted a comprehensive evaluation of various model selection criteria, including AIC, BIC, and their extensions, in selecting an optimal path model under a wide range of conditions over different compositions of candidate set, distinct values of misspecified parameters, and diverse sample sizes. The chance of selecting an optimal model rose as the values of misspecified parameters and sample sizes increased. The relative performance of AIC and BIC type criteria depended on the magnitudes of the parameter misspecified. The BIC family in general outperformed AIC counterparts unless under small values of omitted parameters and sample sizes, where AIC performed better. Scaled unit information prior BIC (SPBIC) and Haughton's BIC (HBIC) demonstrated the highest accuracy ratios across most of the conditions investigated in this simulation.     

Resistance in Supervision: A Response to Perceived Threat

Supervisee resistance is discussed as a defensive response to perceived threat. When feeling threatened, counselors in supervision seek coping strategies to reduce their anxieties. Some of these strategies interfere with the process of learning and may be seen by the supervisor as resistance. Various forms of resistant behavior are enumerated, possible sources of threat that may arouse these behaviors are explored, and a step-by-step model for dealing with resistance in supervision is proposed.