认知诊断模型下整体和项目拟合指标

认知诊断模型能否拟合测验数据,直接决定诊断结果的准确性。目前国内鲜有研究涉及认知诊断测验下的模型-资料拟合检验。文章将模型整体拟合指标及基于PPMC的项目拟合指标应用于认知诊断模型-资料拟合检验。模拟研究基于DINA,R-DINA和R-RUM三个诊断模型检验各拟合指标的表现。结果显示整体和项目拟合指标在识别数据产生模型时皆有较高准确率。采用整体和项目拟合指标比较了三个竞争模型与Tatsuoka带分数减法数据的拟合情况,显示R-RUM拟合最好。     

Using the Bollen-Stine Bootstrapping Method for Evaluating Approximate Fit Indices

Accepting that a model will not exactly fit any empirical data, global approximate fit indices quantify the degree of misfit. Recent research (Chen, Curran, Bollen, Kirby, & Paxton, 2008) has shown that using fixed conventional cut-points for approximate fit indices can lead to decision errors. Instead of using fixed cut points for evaluating approximate fit indices, this study focuses on the meaning of approximate fit and introduces a new method to evaluate approximate fit indices. Millsap (2012) introduced a simulation-based method to evaluate approximate fit indices. A limitation of Millsap's (2012) work was that a rather strong assumption of multivariate normality was implied in generating simulation data. In this study, the Bollen-Stine bootstrapping procedure (Bollen & Stine, 1993) is proposed to supplement the former study. When data are nonnormal, the conclusions derived from Millsap's (2012) simulation method and the Bollen-Stine method can differ. Examples are given to illustrate the use of the Bollen-Stine bootstrapping procedure for evaluating the Root Mean Squared Error of Approximation (RMSEA). Comparisons are made with the simulation method. The results are discussed, and suggestions are given for the use of proposed method.     

Sensitivity of Fit Indices to Misspecification in Growth Curve Models

This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of approximation, standardized root mean square residual, comparative fit index, and Tucker-Lewis Index. The fit indices were found to have differential sensitivity to different types of misspecification in either the mean or covariance structures with severity of misspecification controlled. No fit index was always more (or less) sensitive to misspecification in the marginal mean structure relative to those in the covariance structure. Specifying the covariance structure to be saturated can substantially improve the sensitivity of fit indices to misspecification in the marginal mean structure; this result might help identify the sources of specification error in a growth curve model. An empirical example of children's growth in math achievement (Wu, West, &; Hughes, 2008 Wu, W., West, S. G. and Hughes, J. N. 2008. Effect of retention in first grade on children's achievement trajectories over four years: A piecewise growth analysis using propensity score matching.. Journal of Educational Psychology, 100: 727–740. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) was used to illustrate the results.     

Sensitivity of Fit Indices to Model Misspecification and Model Types

The search for cut-off criteria of fit indices for model fit evaluation (e.g., Hu &; Bentler, 1999 Hu, L. and Bentler, P. M. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling., 6: 1–55. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) assumes that these fit indices are sensitive to model misspecification, but not to different types of models. If fit indices were sensitive to different types of models that are misspecified to the same degree, it would be very difficult to establish cut-off criteria that would be generally useful. The issue about SEM fit indices being sensitive to different types of models has not received sufficient attention, although there is some research suggesting that this might be the case (e.g., Kenny &; McCoach, 2003 Kenny, D. A. and McCoach, D. B. 2003. Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling., 10: 333–351. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). This study examines if fit indices are sensitive to different types of models while controlling for the severity of model misspecification. The findings show that most fit indices, including some very popular ones (e.g., RMSEA), may be sensitive to different types of models that have the same degree of specification error. The findings suggest that, for most fit indices, it would be difficult to establish cut-off criteria that would be generally useful in SEM applications.     

One Size Does Not Fit All: Managing Radical and Incremental Creativity

This research extends creativity theory by re‐conceptualizing creativity as a two‐dimensional construct (radical and incremental) and examining the differential effects of intrinsic motivation, extrinsic rewards, and supportive supervision on perceptions of creativity. We hypothesize and find two distinct types of creativity that are associated with different motivational factors. We further consider how combinations of motivational factors are linked to the different types of creativity. Finally, theoretical and managerial implications are discussed.     

Structural equations modeling: Fit Indices,sample size,and advanced topics

This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth.     

不同条件下拟合指数的表现及临界值的选择

在本模拟研究中设计了6种样本容量,6种因子载荷,和4种评分等级,并考察了正态和非正态分布两种情况。采用的错误模型为参数误置（真模型中每个因子各由5个题目来测量,错误模型中则是第一个因子由6个题测量,另两个因子各由4个和5个题来测量,即有一个因子载荷被误置）模型。结果发现（1）样本量、载荷量、评分等级数和分布形态都对GOF的取值确有影响。其中分布形态的影响最大。NNFI、IFI在不同条件下的平均值是最稳定的,其次是CFI、RMSEA和SRMR。它们都算是值得推荐的GOF,尤其是NNFI和IFI。（2）在正态分布中,当样本量≥1000时,根据NNFI、IFI、CFI、RMSEA、SRMR对模型是否拟合做出判断时有很低的两类错误率,在样本量<1000时则不理想。在偏态条件下无论选择哪个GOF两类错误率都很高。（3）采用2指数策略在很多情况下也不能显著降低两类错误率。（4）由于在数据分布非正态,或正态但样本量<1000时是难判断模型是否拟合的。因此我们提出了2界值策略。即为每个GOF确定上下两个界值。低于下界值时可判断模型是不正确的,而高于上界值时则可判断模型是正确的。GOF取值处于上下界值之间时难以判断模型是否拟合,只能说越高拟合的可能性越大。这时就要通过跨样本验证和增加样本量来确定模型是否正确     

Sensitivity of Fit Indices to Fake Perturbation of Ordinal Data: A Sample by Replacement Approach

In many psychological questionnaires the need to analyze empirical data raises the fundamental problem of possible fake or fraudulent observations in the data. This aspect is particularly relevant for researchers working on sensitive topics such as, for example, risky sexual behaviors and drug addictions. Our contribution presents a new probabilistic approach, called Sample Generation by Replacement (SGR), to address the problem of evaluating the sensitivity of 8 commonly used SEM-based fit indices (Goodness of Fit Index, GFI; Adjusted Goodness of Fit Index, AGFI; Expected Cross Validation Index, ECVI; Standardized Root-Mean-Square Residual Index, SRMR; Root-Mean-Square Error of Approximation, RMSEA; Comparative Fit Index, CFI; Nonnormed Fit Index, NNFI; and Normed Fit Index, NFI) to fake-good ordinal data. We used SGR to perform a simulation study involving 3 different SEM models, 2 sample size conditions, and 2 estimation methods: maximum likelihood (ML) and weighted least squares (WLS). Our results show that the incremental fit indices (CFI, NNFI, and NFI) are clearly more sensitive to fake perturbation than the absolute fit indices (GFI, AGFI, and ECVI). Overall, NFI turned out to be the best and most reliable fit index. We also applied SGR to real behavioral data on (non)compliance in liver transplant patients.     

An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA

The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have been proposed, but the theoretical and empirical differences between the 2 have not been explored. In this article, we investigate the behavior of these 2 corrections. We show that the virtually unknown correction due to Li and Bentler (2006) Li, L. and Bentler, P. M. 2006. “Robust statistical tests for evaluating the hypothesis of close fit of misspecified mean and covariance structural models.”. In UCLA Statistics Preprint #506. Los Angeles: University of California..  [Google Scholar], which we label the sample-corrected robust RMSEA, is a consistent estimate of the population ML RMSEA yet drastically reduces bias due to nonnormality in small samples. On the other hand, the popular correction implemented in several SEM programs, which we label the population-corrected robust RMSEA, has poor properties because it estimates a quantity that decreases with increasing nonnormality. We recommend the use of the sample-corrected RMSEA with nonnormal data and its wide implementation.     

The Influence of Number of Categories and Threshold Values on Fit Indices in Structural Equation Modeling with Ordered Categorical Data

This study examines the unscaled and scaled root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker–Lewis index (TLI) of diagonally weighted least squares (DWLS) and unweighted least squares (ULS) estimators in structural equation modeling with ordered categorical data. We show that the number of categories and threshold values for categorization can unappealingly impact the DWLS unscaled and scaled fit indices, as well as the ULS scaled fit indices in the population, given that analysis models are misspecified and that the threshold structure is saturated. Consequently, a severely misspecified model may be considered acceptable, depending on how the underlying continuous variables are categorized. The corresponding CFI and TLI are less dependent on the categorization than RMSEA but are less sensitive to model misspecification in general. In contrast, the number of categories and threshold values do not impact the ULS unscaled fit indices in the population.     

Fits About Fit

The paper argues that two traditions have dominated research on person–environment fit, the individual differences tradition and the organisational psychology tradition. I briefly review these traditions using the present set of papers as exemplars of these traditions. Then the inclusion of national cultural issues in person–environment fit research, stimulated by two papers in this issue, is introduced and I suggest that this should be the start of a new tradition. Finally, I note that there has been little conceptualisation of, and research on, the environment in person–environment research. This is especially true with regard to the role of people in making environments, and environmental effectiveness outcomes for person–environment fit. I conclude with the thought that fits over fit are healthy because fit is usefully conceptualised and operationalised from so many different interesting perspectives.     

A Person-Environment Fit Approach to Volunteerism: Volunteer Personality Fit and Culture Fit as Predictors of Affective Outcomes

This study employed a person-environment (P-E) fit approach to explaining volunteer satisfaction, affective commitment, and turnover intentions. It was hypothesized that personality fit would explain additional variance in volunteer affective outcomes above and beyond motives to volunteer. This hypothesis was supported. Personality fit but not culture fit was related to satisfaction and affective commitment. Volunteer turnover intentions were predicted by gender and by social and values motives. The implications of the results are discussed with respect to the two literature domains that were combined in this study: the functional approach to volunteerism and P-E fit theory. Functional approaches to volunteerism and paid work should be more strongly linked to each other in future research. Moreover, P-E fit theory should be extended by including conditional determinants that moderate P-E fit effects.     

Adjusting to Retirement: Considerations for Counselors

The purpose of this article is to examine retirement while focusing on issues older adults face in adjusting to retirement and to suggest implications for counseling people who are making this transition.     

Value From Regulatory Fit

Abstract— Where does value come from? I propose a new answer to this classic question. People experience regulatory fit when the manner of their engagement in an activity sustains their goal orientation or interests regarding that activity. When there is fit, people engage more strongly in what they are doing and "feel right" about it. Fit influences the strength of value experiences—how good or how bad one feels about something—independently of the pleasure and pain experiences that are associated with outcomes. It uniquely contributes to people's experience of the value of things. Fit is shown to influence judgments and decision making, attitude and behavior change, and task performance.     

调节性匹配理论述评

在决策、动机领域,Higgins（2000）提出了一种新的理论——调节性匹配理论。所谓调节性匹配（regulatoryfit）,指的是个体的自我调节定向与其行为策略之间的匹配。该理论认为,调节性匹配能增强个体在目标追求过程中的动机强度、主观评价和情绪体验,从而对个体的行为决策产生重要影响。文章围绕调节性匹配的概念、产生、效应及应用价值等问题对相关研究进行综述,并在此基础上提出了现有研究存在的问题及未来的研究方向     

Regulatory Fit and Voting1

The problem of many democracies is low voter turnout. One reason is the voting procedure, which only allows voting for a party or candidate. Introduction of voting against could bring more voters to the polls. The concept of regulatory focus ( Higgins, 1998 ) suggests that people who focus on prevention would vote more eagerly if they are given the opportunity to blackball disliked candidates. This article describes 2 studies that verify this hypothesis. In the first study, over two thirds of participants declared that they would vote more willingly if they had a “for or against” choice at the election. The second study shows that the “pro or anti” formula is especially attractive to participants with a prevention regulatory focus.     

个人—组织匹配的研究现状与展望

个人—组织匹配主要探讨个人和组织之间的相容性以及实现这种相容性的前提和结果。个人与组织可能以附助或补偿的方式形成匹配。尽管在操作指标的选择上,研究者们没有达成共识,但他们的研究结论都同样体现了个人—组织匹配在员工的组织进入、工作态度、离职意向、工作压力、亲社会行为、工作绩效和组织文化培训等方面的管理价值。展望该领域的进一步研究,自我报告的研究方法以及高水平匹配的益处是令人质疑的,匹配的评估指标也亟待明确。