A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness,Kurtosis, and Covariance Matrix

We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empirically that popular test statistics in covariance analysis tend to reject true models more often under the IG transform than under the VM transform. This implies that overly optimistic evaluations of estimators and fit statistics in covariance structure analysis may be tempered by including the IG transform for nonnormal data generation. We provide an implementation of the IG transform in the R environment.     

Accounting for Parcel-Allocation Variability in Practice: Combining Sources of Uncertainty and Choosing the Number of Allocations

Item parceling remains widely used under conditions that can lead to parcel-allocation variability in results. Hence, researchers may be interested in quantifying and accounting for parcel-allocation variability within sample. To do so in practice, three key issues need to be addressed. First, how can we combine sources of uncertainty arising from sampling variability and parcel-allocation variability when drawing inferences about parameters in structural equation models? Second, on what basis can we choose the number of repeated item-to-parcel allocations within sample? Third, how can we diagnose and report proportions of total variability per estimate arising due to parcel-allocation variability versus sampling variability? This article addresses these three methodological issues. Developments are illustrated using simulated and empirical examples, and software for implementing them is provided.     

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.     

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.     

The two roads of passionate goal pursuit: links with appraisal,coping, and academic achievement

Background and Objectives: In this research, we tested the role of cognitive appraisals in explaining why harmonious and obsessive passion dimensions are related to distinct forms of coping and explored if performance was impacted by these appraisal and coping processes. Design: Undergraduate students (N = 489) participated in a longitudinal study and completed three surveys throughout the course of an academic year. Methods: Participants completed assessments of both passion dimensions (Time 1), reported how they were appraising and coping with the mid-year examination period (Time 2), and provided consent to obtain their final grade in Introductory Psychology (Time 3). The hypothesized model was tested using structural equation modeling. Results: Harmonious and obsessive passion dimensions were linked with approach and avoidant coping responses, respectively. Cognitive appraisals, particularly appraisals of challenge and uncontrollability, played an indirect role in these relationships. In addition, both appraisals and coping responses had an indirect effect in the relationship between passion dimensions and final grade. Conclusions: These results identify cognitive appraisal as a reason why passion dimensions are linked with distinct coping tendencies and demonstrate the role of appraisal and coping processes in the journey to passionate goal attainment.     

Promoting fan engagement: An exploration of the fundamental motives for sport consumption amongst premier league football spectators

The aim of this study was to examine whether fan engagement could be predicted by the motivational dimensions of sport consumption. A sample of 806 football sport fans (African = 85%, male = 67.4%, part of an organised supporters club = 57.6%) were participants. They completed the Fan Engagement Scale (FES), the Motivation Scale for Sport Consumption (MSSC) and a biographical questionnaire. Structural equation modelling was applied to evaluate the postulated regressive paths between the motivational dimensions for sports consumption and fan engagement. The motivational dimensions for sport consumption were clustered into personal and game-related factors. Both these clusters of factors were predictive of fan engagement. Customer engagement (CE) theory appears to explain the findings.     

Hijab in the American workplace: visibility and discrimination

While the initial purpose of this article was to outline online collective action (OCA) towards promoting the rights of hijab-wearing Muslim women in the workplace, it became apparent that there were substantial impediments to this solidarity. Thus, the bulk of it takes up these obstacles, arguing that American hijabis face structural discrimination and Muslims in the United States are subjected to heightened discrimination when they are highly visible to the mainstream culture. This article reviews two cases of workplace discrimination against hijabis and the reactions to these incidents on the Huffington Post, Los Angeles Times, and hijab-oriented blogs. Findings indicate that intolerance of Muslims is prevalent across various political affiliations and apprehension associated with hijabi rights could be related to the perceived decline of white privilege. The analysis also demonstrates that there may be impediments to solidarity with hijabi rights within Muslim communities due to disagreements on the Qur’anic mandate of modesty, divergent perspectives on how Muslims can best promote their rights and ensure their safety, and misunderstandings related to the rights guaranteed under reasonable accommodation. Despite the various obstacles to solidarity with hijabis, the article traces various forms of OCA at local, national and international levels that were encountered on social media platforms.     

词素的结构对称效应:结构对称汉语成语认知特点的进一步研究

运用词素启动作业,进一步考察了结构对称性汉语成语的识别过程.结果发现(1)无论是用成语的前二个字还是后二个字启动,在成语识别过程中都存在明显的词素结构对称效应,即结构对称成语中的词素启动效应明显大于非对称成语中的词素启动效应.(2)成语的结构对称性、熟悉度和启动词素位置之间存在显著的三阶相互作用.简单效应的分析表明无论成语前二个字还是后二个字启动,词素的结构对称效应均只在低熟悉度成语的识别过程中显著;在前二个字启动时,非对称成语的识别还存在熟悉度效应.     

A multitrait-multimethod model with minimal assumptions

A new model of confirmatory factor analysis (CFA) for multitrait-multimethod (MTMM) data sets is presented. It is shown that this model can be defined by only three assumptions in the framework of classical psychometric test theory (CTT). All other properties of the model, particularly the uncorrelated-ness of the trait with the method factors are logical consequences of the definition of the model. In the model proposed there are as many trait factors as different traits considered, but the number of method factors is one fewer than the number of methods included in an MTMM study. The covariance structure implied by this model is derived, and it is shown that this model is identified even under conditions under which other CFA-MTMM models are not. The model is illustrated by two empirical applications. Furthermore, its advantages and limitations are discussed with respect to previously developed CFA models for MTMM data.     

Robust mean and covariance structure analysis through iteratively reweighted least squares

Robust schemes in regression are adapted to mean and covariance structure analysis, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is properly weighted according to its distance, based on first and second order moments, from the structural model. A simple weighting function is adopted because of its flexibility with changing dimensions. The weight matrix is obtained from an adaptive way of using residuals. Test statistic and standard error estimators are given, based on iteratively reweighted least squares. The method reduces to a standard distribution-free methodology if all cases are equally weighted. Examples demonstrate the value of the robust procedure.The authors acknowledge the constructive comments of three referees and the Editor that lead to an improved version of the paper. This work was supported by National Institute on Drug Abuse Grants DA01070 and DA00017 and by the University of North Texas Faculty Research Grant Program.