An Extension of the Effects of Praising Positive Qualifying Autoclitics on the Frequency of Reading

The Analysis of Verbal Behavior - In the current study, we reinforced tacts with positive qualifying autoclitics for reading and evaluated the subsequent effect on the allocation of reading...     

Copula-based predictions in small area estimation

Unit-level regression models are commonly used in small area estimation (SAE) to obtain an empirical best linear unbiased prediction of small area characteristics. The underlying assumptions of these models, however, may be unrealistic in some applications. Previous work developed a copula-based SAE model where the empirical Kendall's tau was used to estimate the dependence between two units from the same area. In this article, we propose a likelihood framework to estimate the intra-class dependence of the multivariate exchangeable copula for the empirical best unbiased prediction (EBUP) of small area means. One appeal of the proposed approach lies in its accommodation of both parametric and semi-parametric estimation approaches. Under each estimation method, we further propose a bootstrap approach to obtain a nearly unbiased estimator of the mean squared prediction error of the EBUP of small area means. The performance of the proposed methods is evaluated through simulation studies and also by a real data application.     

Algeria’s Trade with GAFTA Countries: A Synthetic Control Approach

Arab countries signed the Greater Arab Free Trade Area (GAFTA) agreement in 1998, while Algeria only joined in 2005. In this study, I investigate the counterfactual trade that Algeria would have had with its GAFTA trade partners had it joined the agreement in 1998. I employ synthetic control methods (SCM) in the context of the gravity approach to international trade to estimate this counterfactual outcome. The SCM creates the synthetic control unit based on a weighted average of the past characteristics and trade realizations of the countries that signed the GAFTA agreement and compares the counterfactual trade outcome against the actual outcome of the treated unit, Algeria in our case. Estimation results indicate that Algeria??s trade with nine GAFTA countries, covering more than 96?% of Algeria??s pre-treatment trade, would have improved if Algeria had signed the agreement in 1998.     

Fuzzy p-value in testing fuzzy hypotheses with crisp data

In testing statistical hypotheses, as in other statistical problems, we may be confronted with fuzzy concepts. This paper deals with the problem of testing hypotheses, when the hypotheses are fuzzy and the data are crisp. We first introduce the notion of fuzzy p-value, by applying the extension principle and then present an approach for testing fuzzy hypotheses by comparing a fuzzy p-value and a fuzzy significance level, based on a comparison of two fuzzy sets. Numerical examples are also provided to illustrate the approach.     

Spatio‐temporal modelling of disease mapping of rates

This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates. This class of models adopts spatially correlated random effects and random temporal components. Spatio‐temporal models that use conditional autoregressive smoothing across the spatial dimension and autoregressive smoothing over the temporal dimension are developed. The model also accommodates the interaction between space and time. However, the effect of seasonal factors has not been previously addressed and in some applications (e.g., health conditions), these effects may not be negligible. The authors incorporate the seasonal effects of month and possibly year as part of the proposed model and estimate model parameters through generalized estimating equations. The model provides smoothed maps of disease risk and eliminates the instability of estimates in low‐population areas while maintaining geographic resolution. They illustrate the approach using a monthly data set of the number of asthma presentations made by children to Emergency Departments (EDs) in the province of Alberta, Canada, during the period 2001–2004. The Canadian Journal of Statistics 38: 698–715; 2010 © 2010 Statistical Society of Canada     

Hierarchical Bayesian bivariate disease mapping: analysis of children and adults asthma visits to hospital

In spatial epidemiology, detecting areas with high ratio of disease is important as it may lead to identifying risk factors associated with disease. This in turn may lead to further epidemiological investigations into the nature of disease. Disease mapping studies have been widely performed with considering only one disease in the estimated models. Simultaneous modelling of different diseases can also be a valuable tool both from the epidemiological and also from the statistical point of view. In particular, when we have several measurements recorded at each spatial location, one can consider multivariate models in order to handle the dependence among the multivariate components and the spatial dependence between locations. In this paper, spatial models that use multivariate conditionally autoregressive smoothing across the spatial dimension are considered. We study the patterns of incidence ratios and identify areas with consistently high ratio estimates as areas for further investigation. A hierarchical Bayesian approach using Markov chain Monte Carlo techniques is employed to simultaneously examine spatial trends of asthma visits by children and adults to hospital in the province of Manitoba, Canada, during 2000–2010.     

Organizational Form,Ownership Structure and Corporate Performance: A Contextual Empirical Analysis of UK Companies1

This paper contributes to the literature on organizational form and performance (the performance-conduct paradigm) by examining the impact of two sets of variables that have not been examined previously: measures of environmental uncertainty and alternative forms of controlling separation of ownership from control. The paper first examines the extent to which contextual variables such as perceived environmental uncertainty and age of the organizational form moderate the relationship between organizational form and corporate performance. Next, the paper extends the analysis by examining the possible effects of ownership and control structures on performance. Measures suggested by agency theory, such as conventional ownership proxies (i.e. proportion of shares held by identifiable, homogenous groups), the proportion of non-executive board members and Fama and Jensen's concept of separation of decision management from decision control are used in the analysis. The results indicate that these measures have direct impact on corporate performance in their own right, though the main impact is indirect through interactions with organizational form variables.     

An empirical investigation of motivation and effective performance: A comparison of Arab and U.S. cultures

This study empirically examines motivational profiles within the Arab culture and compares them with the U.S. culture. The associations of needs for achievement, affiliation, and power with effectiveness (salary, promotion, and GPA) and job satisfaction in the Arab culture are investigated. The need for affiliation is negatively associated with effectiveness for managers and students, and the need for power is positively associated with effectiveness for managers. The Arab culture exhibits higher affiliation need, while the U.S. culture exhibits higher power and achievement needs. However, the two cultures exhibit similar profiles of effective motivation for managers, students, and professionals.     

The View from the Top: Senior Executives' Perceptions of Changing Management Practices in UK Companies1

There is a growing literature which suggests that the 1980s witnessed significant, even unprecedented changes in the management of organizations. This paper contributes to that literature by providing questionnaire and interview-based evidence of senior managers' perceptions of recent changes in management practice in large and medium-sized firms in the UK. The findings cover three main areas: the changing context of management practice; change initiatives in the areas of human resource management (HRM) and management levels; and the degree of success in managing change. Our findings suggest that senior managers perceive rapid and continuous change within the UK corporate sector. We conclude by suggesting that the changes reported appear to be of degree rather than a radical break with past management practices and that inconsistent means of facilitating change are seemingly being deployed.     

Phase I Analysis of Multiple Linear Regression Profiles

This article considers the Phase I analysis of data when the quality of a process or product is characterized by a multiple linear regression model. This is usually referred to as the analysis of linear profiles in the statistical quality control literature. The literature includes several approaches for the analysis of simple linear regression profiles. Little work, however, has been done in the analysis of multiple linear regression profiles. This article proposes a new approach for the analysis of Phase I multiple linear regression profiles. Using this approach, regardless of the number of explanatory variables used to describe it, the profile response is monitored using only three parameters, an intercept, a slope, and a variance. Using simulation, the performance of the proposed method is compared to that of the existing methods for monitoring multiple linear profiles data in terms of the probability of a signal. The advantage of the proposed method over the existing methods is greatly improved detection of changes in the process parameters of linear profiles with high-dimensional space. The article also proposes useful diagnostic aids based on F-statistics to help in identifying the source of profile variation and the locations of out-of-control samples. Finally, the use of multiple linear profile methods is illustrated by a data set from a calibration application at National Aeronautics and Space Administration (NASA) Langley Research Center.