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.     

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.     

A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.     

A penalized likelihood method for multi-group structural equation modelling

In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis. The aim of the present work is to establish a penalized likelihood (PL) method for multi-group SEM. Our proposed method decomposes each group model parameter into a common reference component and a group-specific increment component. By penalizing the increment components, the heterogeneity of parameter values across the population can be explored since the null group-specific effects are expected to diminish. We developed an expectation-conditional maximization algorithm to optimize the PL criteria. A numerical experiment and a real data example are presented to demonstrate the potential utility of the proposed method.     

基于增长模型的非随机缺失数据处理:选择模型和极大似然方法

对含有非随机缺失数据的潜变量增长模型,为了考察基于不同假设的缺失数据处理方法:极大似然(ML)方法与DiggleKenward选择模型的优劣,通过Monte Carlo模拟研究,比较两种方法对模型中增长参数估计精度及其标准误估计的差异,并考虑样本量、非随机缺失比例和随机缺失比例的影响。结果表明,符合前提假设的Diggle-Kenward选择模型的参数估计精度普遍高于ML方法;对于标准误估计值,ML方法存在一定程度的低估,得到的置信区间覆盖比率也明显低于Diggle-Kenward选择模型。     

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.