Analysis of factors affecting drivers’ choice to engage with a mobile phone while driving in Beijing

In the current work, we examined reasons that drivers choose to engage with a mobile phone while driving in Beijing. An Internet survey was administered to collect data about talking and texting while driving. Conversations were sorted into different types. Respondents were requested to indicate the frequency of initiating a call or text, perceived risk, perceived importance and emotionality of the call or text. A structural equation model of talking and texting while driving was developed with perceived risk, perceived importance and emotionality of the call as predictors and compared to a similar model with U.S. drivers. Unlike the U.S. data, perceived risk has a significant negative impact on the choice to call or text among drivers in Beijing. Results also show that perceived importance of the call is a major factor affecting the usage of phone while driving. Even though drivers know it is dangerous and illegal, Beijing drivers choose to talk on mobile phones while driving, but they prefer not to text.     

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.     

Response to Stuart Kauffman: The Cosmic Mind and NIODA

Non-locality, indeterminacy, the meaning of the Schrödinger equation, and quantum measurements are interpretation issues in quantum mechanics that go beyond our typical view of the world through the classical physics lenses of the mechanistic determinism. In “Cosmic Mind?,” Stuart Kauffman offers an interpretation of the Schrödinger equation and quantum measurements that might support a cosmic mind. Robert John Russell in NIODA uses the indeterminacy to offer a mechanism for God to interact with nature. This response reviews these two interpretations of quantum mechanics with respect to the two-slit and EPR experiments and how these two interpretations of quantum mechanics could solve issues of interpretations.     

Diverging Paths: Understanding Racial Differences in Civic Engagement Among White,African American,and Latina/o Adolescents Using Structural Equation Modeling

There is increasing concern about the large civic engagement gap between Whites and Latina/o and African American youth. Some suggest this may be because traditional models and measures of civic engagement may not be as applicable for youth from historically marginalized groups. With an urban sample of middle and high school‐age youth (n = 903, 52% female), we used structural equation modeling to identify differences in civic pathways between youth from different racial/ethnic backgrounds. We found significant differences between groups including much stronger relationships between exposure to democratic practices and civic self‐efficacy and knowledge for African American and Latina/o youth than for White youth and a stronger relationship between civic knowledge and future civic engagement for Whites and Latina/os than for African Americans. These findings suggest that educators and researchers interested need to take into account the diversity of youths' racial experiences when examining youth civic development.     

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.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.     

公平感对社会认知的影响:经济因素的调节作用

目的及方法:基于中国综合社会调查2010年数据,采用多层结构方程模型,探究中国民众公平感对社会认知的作用,及经济因素在其中的调节效应。研究结果:第一,公平感越高,则社会冲突感越低,外在政治效能感越高,越倾向于对社会不平等进行内部归因,即对于社会的整体认知更加积极; 第二,经济因素能调节公平感与社会不平等内归因的关系,具体而言,经济水平越低,则公平感对于社会不平等的内部归因正向预测作用越大。     

USING MICROSOFT OFFICE EXCEL® 2007 TO CONDUCT GENERALIZED MATCHING ANALYSES

The generalized matching equation is a robust and empirically supported means of analyzing relations between reinforcement and behavior. Unfortunately, no simple task analysis is available to behavior analysts interested in using the matching equation to evaluate data in clinical or applied settings. This technical article presents a task analysis for the use of Microsoft Excel to analyze and plot the generalized matching equation. Using a data‐based case example and a step‐by‐step guide for completing the analysis, these instructions are intended to promote the use of quantitative analyses by researchers with little to no experience in quantitative analyses or the matching law.     

Limited information estimation and testing of discretized multivariate normal structural models

Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification. This research was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grants BSO2000-0661 and BSO2003-08507 of the Spanish Ministry of Science and Technology.     

Dimensions of fatigue in different working populations

Perceived fatigue related to work has often been measured in one dimension. The main purpose of the present study was to validate a proposed five-factor model of perceived fatigue in a new sample. 597 persons, employed in five occupations with different types of work loads, rated their fatigue after work. The ratings were subjected to analyses of linear structural equation models. The results suggest a slightly revised model for perceived fatigue, still with the five dimensions: Lack of energy, Physical exertion, Physical discomfort, Lack of motivation and Sleepiness. As expected, the rating profiles describing fatigue states differed between the five occupations. On the basis of these results, a revised version of the Swedish Occupational Fatigue Inventory (SOFI) is presented.