Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore, if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

Feeling Better, Performing Better? Holistically-Oriented Top Performance and Well-Being (HOPE) : Performance Enhancement and Its Perceived Impacts on Musicians

Most musicians choose a career in music based on their love of the art and a desire to share it with others. However, being a performing musician is highly demanding. Despite considerable evidence of the great frequency of performance-related problems (e.g. debilitating performance anxiety) among professional musicians or aspiring musicians in the current Western classical music tradition these problems are seldom discussed openly. The existing system offers musicians very little help in learning how to build sustainable performance success into their musical career. This study it is first of its kind in Finland which addresses the issue on larger scale in a systematic way. I devised the HOPE intervention (Holistically-Oriented Top Performance and Well-Being Enhancement), in order to learn how to integrate professional peak performance and a sense of personal well-being into the lives and careers of musicians. Unlike most interventions in previous research, the HOPE intervention is explicitly holistic and aims at enhancing the whole musician, not just alleviating performance anxiety. Earlier research has not in principle focused on musicians´ psychological well-being or on their subjective perceptions. The main purpose of the study is to understand the perceived impacts of the specially devised HOPE intervention on the participants and particularly in four key areas: performing, playing or singing well-being, and overall (performing, playing or singing and well-being combined). Furthermore, it is hoped that a deeper understanding of performers´ development will be gained. The research method is interdisciplinary and mainly qualitative. The primary data consist of a series of linked questionnaires (before and after the intervention) and semi-structured follow-up interviews collected during action research-oriented HOPE intervention courses for music majors in the Sibelius Academy. With the longitudinal group called Hope 1, the core data were collected during a nine month HOPE intervention course and from follow-up interviews conducted six months later in 2003-2004. The core data of Hope 1 (nine participants) are compared with the perceived impacts on fifty-three other participants in the HOPE courses during the period since their inception, 2001-2006. The focus is particularly on participants´ subjective perceptions. Results of the study suggest that the HOPE intervention is beneficial in enhancing overall performance capacity, including music performance, and a personal sense of well-being in a music university setting. The findings indicate that within all key areas significant positive changes take place between the beginning and the end of a HOPE intervention course. The longitudinal data imply that the perceived positive changes are still ongoing six months after the HOPE intervention course is finished. The biggest change takes place within the area of performing and the smallest, in participants´ perception of their playing or singing. The main impacts include reduced feelings of stress and anxiety (an enhanced sense of well-being) as well as increased sense of direction and control in one's life. Since the results of the present research gave no other reason to believe otherwise, it is to be expected that the HOPE intervention and the results of the study can be exploited in other areas of human activity as well, especially where continuous professional top performance is a prerequisite such as in business or sports. Keywords: performance enhancement, professional top performance, subjective well-being, subjective perceptions, holism, coaching, music performance anxiety, studying music, music.

SME international performance in Latin America: The role of entrepreneurial and technological capabilities

SMEs from emerging markets in Latin America are increasingly engaging in internationalization. Nevertheless, there is limited research into how these firms achieve international performance. This study proposes and tests a conceptual model that considers managerial and technology-related capabilities and their impact on international performance of SMEs. The model uses confirmatory factor analysis (CFA) to develop the underlying multi-item constructs and structural equation modeling (SEM) to test the model with data from 233 Chilean SMEs. Specifically, the model considers the role of international entrepreneurial orientation and Internet capabilities on international market performance, taking into account the mediating effect of international entrepreneurial opportunity recognition and technology-related international networks. Results show that international entrepreneurial opportunity recognition and international networks mediate the relationship between international entrepreneurial orientation and Internet marketing capabilities on SME international performance.

Design and Performance Evaluation of EXMAN: An EXtended MANchester Data Flow Computer

Data flow computers are high-speed machines in which an instruction is executed as soon as all its operands are available. This paper describes the EXtended MANchester (EXMAN) data flow computer which incorporates three major extensions to the basic Manchester machine. As extensions we provide a multiple matching units scheme, an efficient, implementation of array data structure, and a facility to concurrently execute reentrant routines. A simulator for the EXMAN computer has been coded in the discrete event simulation language, SIMULA 67, on the DEC 1090 system. Performance analysis studies have been conducted on the simulated EXMAN computer to study the effectiveness of the proposed extensions. The performance experiments have been carried out using three sample problems: matrix multiplication, Bresenham's line drawing algorithm, and the polygon scan-conversion algorithm.

Effects of self-reported wisdom on happiness: Not much more than emotional intelligence?

Wisdom and emotional intelligence are increasingly popular topics among happiness scholars. Despite their conceptual overlap, no empirical research has examined their interrelations and incremental predictive validities. The aims of this study were (a) to investigate associations between multidimensional conceptualizations of self-reported wisdom (Ardelt in Res Aging 25(3):275-324, 2003, 2004) and emotional intelligence (Davies et al. in J Pers Soc Psychol 75:989-1015, 1998) and (b) to examine the joint effects of self-reported wisdom and emotional intelligence on dimensions of happiness (life satisfaction as well as positive and negative affect). Data were provided by two samples: 175 university students and 400 online workers. Correlations between a composite wisdom score, a composite emotional intelligence score, and happiness facets were positive and moderate in size. Regression analyses showed that the effects of composite wisdom on life satisfaction and positive affect (but not negative affect) became weaker and non-significant when composite emotional intelligence was controlled. Additional analyses including three dimensions of the self-reported wisdom (cognitive, reflective, and affective wisdom) and four dimensions of emotional intelligence (self- and others-emotions appraisal, use and regulation of emotion) revealed a more differentiated pattern of results. Implications for future research on wisdom and happiness are discussed.

Focus on opportunities as a mediator of the relationships between age, job complexity, and work performance

Focus on opportunities is a cognitive-motivational facet of occupational future time perspective that describes how many new goals, options, and possibilities individuals expect to have in their personal work-related futures. This study examined focus on opportunities as a mediator of the relationships between age and work performance and between job complexity and work performance. In addition, it was expected that job complexity buffers the negative relationship between age and focus on opportunities and weakens the negative indirect effect of age on work performance. Results of mediation, moderation, and moderated mediation analyses with data collected from 168 employees in 41 organizations (mean age = 40.22 years, SD = 10.43, range = 19-64 years) as well as 168 peers providing work performance ratings supported the assumptions. The findings suggest that future studies on the role of age for work design and performance should take employees' focus on opportunities into account.

Ambidextrous leadership and employees' self-reported innovative performance: The role of exploration and exploitation behaviors

The ambidexterity theory of leadership for innovation proposes that leaders' opening and closing behaviors positively predict employees' exploration and exploitation behaviors, respectively. The interaction of exploration and exploitation behaviors, in turn, is assumed to influence employee innovative performance, such that innovative performance is highest when both exploration and exploitation behaviors are high. The goal of this study was to provide the first empirical test of these hypotheses at the individual employee level. Results based on self-report data provided by 388 employees were consistent with ambidexterity theory, even after controlling for employee reports of their leaders' transformational and transactional leadership behaviors as well as employees' openness to experience, conscientiousness, and positive affect. The findings extend previous research on ambidexterity at the team and organizational levels and suggest a possible way for leaders to enhance employee self-reported innovative performance.

Using Big Data to manage safety-related risk in the upstream oil and gas industry: A research agenda

Despite considerable effort and a broad range of new approaches to safety management over the years, the upstream oil & gas industry has been frustrated by the sector’s stubbornly high rate of injuries and fatalities. This short communication points out, however, that the industry may be in a position to make considerable progress by applying “Big Data” analytical tools to the large volumes of safety-related data that have been collected by these organizations. Toward making this case, we examine existing safety-related information management practices in the upstream oil & gas industry, and specifically note that data in this sector often tends to be highly customized, difficult to analyze using conventional quantitative tools, and frequently ignored. We then contend that the application of new Big Data kinds of analytical techniques could potentially reveal patterns and trends that have been hidden or unknown thus far, and argue that these tools could help the upstream oil & gas sector to improve its injury and fatality statistics. Finally, we offer a research agenda toward accelerating the rate at which Big Data and new analytical capabilities could play a material role in helping the industry to improve its health and safety performance.

Nonradial directional performance measurement with undesirable outputs: An application to OECD and Non-OECD countries

The objective of this paper is to provide a more comprehensive e±ciency measure to estimate the performance of OECD and non-OECD countries. A Russell directional distance function that appropriately credits the decision-making unit not only for increase in desirable outputs but also for the decrease of undesirable outputs is derived from the proposed weighted Russell directional distance model. The method was applied to a panel of 116 countries from 1992 to 2010. This framework also decomposes the comprehensive efficiency measure into individual input/ output components' inefficiency scores that are useful for policy making. The results reveal that the OECD countries perform better than the non-OECD countries in overall, goods,labor and capital efficiencies, but worse in bad and energy efficiencies.

Quality certification and company performance: The newly developed country experience

This study investigates the impacts of ISO 9001:2008 certification on companies in Malaysia. Data were collected from CEOs and Managers through a questionnaire survey. A multivariate analysis and SPSS macro were used as statistical techniques to assess the effects of ISO 9001 certification. Results of the study indicate that ISO 9001 certified companies were having significantly greater benefits and financial performance compared to non-certified companies. However, no significant direct relationship between ISO 9001 certification and company's financial performance was found. A further investigation revealed that financial performance is actually directly related to quality and local and international business performance, which are significantly influenced by ISO 9001 certification. Therefore quality and business performances are involved in the mediational process between the financial performance of companies and ISO 9001 certification. The novelty of this research lies in the establishment of, for the first time, high level statistical relationship between ISO 9001 certification, its mediating factors and financial performance of companies.

How small business managers’ age and focus on opportunities affect business growth: A mediated moderation growth model

Research on business growth has been criticized for methodological weaknesses. We present a mediated moderation growth model as a new methodological approach. We hypothesized that small business managers' age negatively affects business growth through focus on opportunities. We sampled 201 small business managers and obtained firm performance data over 5 years, resulting in 836 observations. Growth modeling showed systematic differences in firm performance trajectories. These differences could be explained by modeling focus on opportunities as a mediator of the relationship between small business managers' age and business growth. The study illustrates how mediation models can be tested using growth modeling.

Student performance in field education placements: The findings of a 6-year Australian study of admissions data

This article reports on a 6-year study that examined the association between pre-admission variables and field placement performance in an Australian bachelor of social work program (N=463). Very few of the pre-admission variables were found to be significantly associated with performance. These findings and the role of the admissions process are discussed. In addition to the usual academic criteria, the authors urge schools to include a focus on nonacademic criteria during the admissions process and the ongoing educational program.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Finite data performance of MUSIC and minimum norm methods

In the direction of arrival (DOA) estimation problem, we encounter both finite data and insufficient knowledge of array characterization. It is therefore important to study how subspace-based methods perform in such conditions. We analyze the finite data performance of the multiple signal classification (MUSIC) and minimum norm (min. norm) methods in the presence of sensor gain and phase errors, and derive expressions for the mean square error (MSE) in the DOA estimates. These expressions are first derived assuming an arbitrary array and then simplified for the special case of an uniform linear array with isotropic sensors. When they are further simplified for the case of finite data only and sensor errors only, they reduce to the recent results given in [9-12]. Computer simulations are used to verify the closeness between the predicted and simulated values of the MSE.