Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Estimation of the probability of congestion using Monte Carlo method in OPS networks

In networks with small buffers, such as optical packet switching based networks, the convolution approach is presented as one of the most accurate method used for the connection admission control. Admission control and resource management have been addressed in other works oriented to bursty traffic and ATM. This paper focuses on heterogeneous traffic in OPS based networks. Using heterogeneous traffic and bufferless networks the enhanced convolution approach is a good solution. However, both methods (CA and ECA) present a high computational cost for high number of connections. Two new mechanisms (UMCA and ISCA) based on Monte Carlo method are proposed to overcome this drawback. Simulation results show that our proposals achieve lower computational cost compared to enhanced convolution approach with an small stochastic error in the probability estimation

Coneixements dels professionals de la salut de l'atenció primària sobre prevenció del consum de drogues

The aim of this research is to know the training of health professionals in health promotion and disease prevention, and to examine its manifestation among the actions and interventions for prevention of tobacco, alcohol or cannabis consumption. The sample includes 225 professionals. The study used a self-made design of quantitative methodology (survey study). The most important results are: the formative limitations in health education and prevention of substance use and the fact that professionals who have received specific training in substance use tap more health education as a prevention tool in their daily activities. It is also noted that 80% of professionals believe they should improve quality training, and 67% quantity, always in relation to the tobacco, alcohol and cannabis use. Generally, the overload care and the lack of time are cited as factors preventing the health education activities. Finally, the study also shows that secondary prevention activities are the most used, while community interventions are underutilized by professionals.