Robustness of Parameter Estimation to Assumptions of Normality in the Multidimensional Graded Response Model

A central assumption that is implicit in estimating item parameters in item response theory (IRT) models is the normality of the latent trait distribution, whereas a similar assumption made in categorical confirmatory factor analysis (CCFA) models is the multivariate normality of the latent response variables. Violation of the normality assumption can lead to biased parameter estimates. Although previous studies have focused primarily on unidimensional IRT models, this study extended the literature by considering a multidimensional IRT model for polytomous responses, namely the multidimensional graded response model. Moreover, this study is one of few studies that specifically compared the performance of full-information maximum likelihood (FIML) estimation versus robust weighted least squares (WLS) estimation when the normality assumption is violated. The research also manipulated the number of nonnormal latent trait dimensions. Results showed that FIML consistently outperformed WLS when there were one or multiple skewed latent trait distributions. More interestingly, the bias of the discrimination parameters was non-ignorable only when the corresponding factor was skewed. Having other skewed factors did not further exacerbate the bias, whereas biases of boundary parameters increased as more nonnormal factors were added. The item parameter standard errors recovered well with both estimation algorithms regardless of the number of nonnormal dimensions.     

Impact of personality traits and personal values on curriculum choice of young adults

In the present study, an attempt was made to investigate the role of personality and personal values in the curriculum choice. Four hundred students (200 males and 200 females) ranging in age between 18- and 25-years-old participated in the study. Eysenck Personality Questionnaire – Revised (EPQ-R) and The Aspiration Index (AI) were used to measure the personality traits and personal values respectively. Regression analyses indicate that individuals scoring high on extraversion and having high intrinsic value orientation (Importance) are more likely to choose arts/humanities (A/H) academic discipline. While individuals with high scores on psychoticism, neuroticism and having extrinsic value orientation (Importance) tend to opt for business/technical (B/T) academic stream. Findings are explained in terms of the Self-determination theory and the changing choices leading to the possible shift in the value system. Gender differences in personality and values; and their impact on academic choice have also been studied. Extraversion plays the strongest role in the choice of A/H academic choice irrespective of gender. Females are guided by intrinsic values while choosing A/H streams while males are influenced by extrinsic values while opting for B/T streams.     

Deterrence of drug driving: The impact of the ACT drug driving legislation and detection techniques

Drug driving is a significant road safety concern rendering the implementation of roadside drug testing in all Australian jurisdictions. The current research sought to examine the impact of recently introduced roadside oral fluid screening in the Australian Capital Territory (ACT). Specifically, the study sought to examine drivers’ awareness, perceptions and perceived deterrent impact of these operations and the degree to which they influence likelihood of future drug driving. A total of 801 male and female motorists aged 17–88 years of age completed a phone interview assessing demographics (e.g., driving and drug taking history), awareness and perceived effectiveness of roadside drug testing, and constructs central to both Classical Deterrence Theory (i.e., certainty, severity, swiftness) and reconceptualised deterrence theory (direct and vicarious experiences of both punishment and punishment avoidance) frameworks. Overall, despite an apparent decline in drug driving behaviour since the introduction of roadside testing, a large proportion of driver’s possessed a poor awareness of these operations and did not perceive a high certainty of apprehension. Age, punishment avoidance and vicarious punishment avoidance were found to predict future likelihood of drug driving, whilst Classical Deterrence Theory variables did not. Contrary to expectations and previous studies, few significant differences were found with regards to gender. Findings are interpreted in light of the recency of roadside drug testing in the ACT and the need for future studies to examine the impact of such operations. Further recommendations for augmenting the deterrence of drug driving are discussed.     

An Alternate Method of Deriving Priorities and Related Inferences for Group Decision Making in Analytic Hierarchy Process

Some work has been carried out in the past on statistically deriving priorities in Analytic Hierarchy Process (AHP). In AHP, the aggregated worths of the alternatives, when compared with respect to several criteria, are estimated in a hierarchical comparisons model introduced by Saaty. In this setup, statistical models are used for Saaty's method of scaling in paired comparisons experiments in any level of the hierarchy. At the end, the final priority weights of the alternatives and related inferences are developed with appropriate statistical methods. Existing statistical methods in the literature assume independence of the entries of the paired comparison matrix. However, these entries are highly dependent among themselves. In this article, we propose a statistical method that allows for the dependence among the entries of the pairwise comparisons matrix. The proposed method is then illustrated with a numerical example. Copyright © 2011 John Wiley & Sons, Ltd.     

Relationship Between Balance Recovery From a Forward Fall and Lower-Limb Rate of Torque Development

Abstract

The authors examined the relationship between the maximum recoverable lean angle via the tether-release method with early- or late-phase rate of torque development (RTD) and maximum torque of lower-limb muscle groups in 56 young healthy adults. Maximal isometric torque and RTD at the hip, knee, and ankle were recorded. The RTD at 50-ms intervals up to 250?ms from force onset was calculated. The results of a stepwise multiple regression analysis, early RTD for hip flexion, and knee flexion were chosen as predictive variables for the maximum recoverable lean angle. The present study suggests that some of the early RTD in the lower limb muscles, but not the maximum isometric torque, can predict the maximum recoverable lean angle.     

Motives and Likelihood of Bribery: An Experimental Study of Managers in Taiwan

Many studies of bribery acknowledge the important role of bribe-givers, but their true motives remain unclear. We propose that the likelihood of bribery depends on the willingness of an organization to affiliate with local parties or to be successful in a host country, or to have power over local parties. We further argue that different opportunities, either pervasive or arbitrary, facilitate different types of motives that affect the likelihood of bribery. In addition, we investigate the effect of perceived fairness on the likelihood of bribery. We employ a 3 (motives: affiliation vs. achievement vs. power)?×?2 (opportunities: pervasiveness vs. arbitrariness)?×?2 (perceived fairness: high vs. low) factorial design in experimental settings among Executive MBA students in southern Taiwan. Our findings indicate that, when companies perceive a higher level of distributive fairness, high-achieving organizations are more likely to offer a bribe when the condition is pervasive. When they have a powerful motive, arbitrariness engenders a higher likelihood of bribery. When they perceive less distributive fairness, there are no significant differences between motive and opportunity.     

潜变量交互效应结构方程：分布分析方法

对于潜变量交互效应结构方程分析,目前应用较多的是乘积指标方法.分布分析方法国内还罕有应用,包括潜调节结构方程（LMS）方法和准极大似然（QML）方法.该研究以乘积指标方法的模型假设为参照,介绍了分布分析方法的模型假设.并简要叙述了LMS方法及其Mplus程序,QML方法及其QML程序.综合现有研究结果,总结出LMS和QML方法、无约束和约束方法的特点,从中可以看出各方法的优缺点,推荐了不同条件下合适的分析方法.     

Conditional estimation of exponential random graph models from snowball sampling designs

A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their network partners are then traced and asked to nominate their network partners, and so on. We assume an exponential random graph model (ERGM) of a particular parametric form and outline a conditional maximum likelihood estimation procedure for obtaining estimates of ERGM parameters. This procedure is intended to complement the likelihood approach developed by  Handcock and Gile (2010) by providing a practical means of estimation when the size of the complete network is unknown and/or the complete network is very large. We report the outcome of a simulation study with a known model designed to assess the impact of initial sample size, population size, and number of sampling waves on properties of the estimates. We conclude with a discussion of the potential applications and further developments of the approach.     

Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation

Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer &; Graham, 2002 Schafer, J.L., &; Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Graham, 2009 Graham, J.W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Enders, 2010 Enders, C.K. (2010). Applied missing data analysis. New York, NY: Guilford Press. [Google Scholar]). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program.