Alfonso Palmer

Occupational safety and health in Spain

Occupational Health and Safety in Spain has improved considerably over the last decade, most likely due to a new concept where an overall concept of safety culture is defined. Important changes in industrial safety, hygiene, and psychosocial factors present an optimistic panorama for the future of Spain. Despite this general improvement, according to the European Convergence Program, Spanish statistics still offer far from good safety results. In fact, according to 1997 official statistics, Spain had the highest incidence rate for nonfatal occupational accidents of all European Union (EU) countries, and occupied third place for fatal accidents. This paper summarizes the organizational structure of the Spanish National System of Health & Safety at Work, its effective health and safety laws, and statistics on the Spanish work environment obtained from III Spanish National Survey on Work Conditions (1997). The researchers hope that the findings of this work will have an impact on Spanish industry that will subsequently bring about improvements in work conditions and develop assessment and intervention models in occupational health and safety, from a theoretical position integrating environmental, human, and organizational factors.

Quantification of the influence of friends and antisocial behaviour in adolescent consumption of cannabis using the ZINB model and data mining

Cannabis is the most consumed illegal drug in Europe and its repercussions are more important when taken up at an early age. The aim of this study is to analyse and quantify the predictive value of different personal, family and environmental variables on the consumption of cannabis in adolescence. The sample is made up of 9284 adolescents (47.1% boys and 52.9% girls) with an average age of 15.59 years (SE=1.17). The ZINB model highlights, as factors that increase the number of joints consumed per week, consumption by the peer group, nights out during the week, gender, the production of forbidden behaviour and the use of other substances, whereas the risk factors for the consumption of cannabis are consumption by friends, ease of access, production of forbidden behaviour and the use of other substances. Association rules highlight the relationship between cannabis consumption, ease of access, production of forbidden behaviour and tobacco consumption. Finally, decision trees enable us to predict cannabis consumption as well as the number of joints an adolescent will consume per week based on the production of forbidden behaviour, consumption of other substances and number of friends who consume cannabis. The results of this work have practical implications concerning the prevention of cannabis consumption in an adolescent population.

Dimensionality Reduction in Data Mining Using Artificial Neural Networks

The use of classic dimension reduction techniques can be considered customary practice within the context of data mining (DM). Nevertheless, although artificial neural networks (ANNs) are one of the most important DM techniques, specific ANN architectures for dimensionality reduction, such as the principal components analysis ANN (PCA-ANN) and the linear auto-associative ANN (LA-ANN), are used on far fewer occasions. In this study, categorical principal component analysis (CATPCA) and the two ANN procedures are studied and compared searching for uniqueness in an applied context relative to personality variables and drug consumption. A sample of 7,030 adolescents completed a personality test made up of 20 dichotomous items with a hypothesized four-factor latent model. Results point out that both ANN factor solutions converge to those obtained using CATPCA. Nevertheless, possible drawbacks of the ANN techniques lie in their relatively complex application, as well as in the need to use visual graphic analysi...

Psychometric Measurement Models and Artificial Neural Networks.

The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the use of neural networks as a dimensionality reduction tool applied to psychometrics is totally nonexistent in the literature. The work presented here is devoted to the description of Neural Networks Principal Components Analysis (NNPCA) and the comparison of its results with performance of classical PCA by means of a computer simulation study. Results indicate very important convergence between neural and classical PCA, even with simulated diffuse latent structures. NNPCA is performed under principles of neurobiologic plausibility (learning neural architecture), useful to analyze latent psychometric structures adding more psychological significance. This new tool could be considered as another complementary way to assess measurement models, together with classical approaches. The pioneer character of this work also requires the development of further empirical evidence that allows advancement in the use of the NNPCA in the study of psychometric measurement models.

Recommendations for the use of statistics in Clinical and Health Psychology

The generation of scientific knowledge in Psychology has made significant headway over the last decades, as the number of articles published in high impact journals has risen substantially. Breakthroughs in our understanding of the phenomena under study demand a better theoretical elaboration of work hypotheses, efficient application of research designs, and special rigour concerning the use of statistical methodology. Anyway, a rise in productivity does not always mean the achievement of high scientific standards. On the whole, statistical use may entail a source of negative effects on the quality of research, both due to (1) the degree of difficulty inherent to some methods to be understood and applied and (2) the commission of a series of errors and mainly the omission of key information needed to assess the adequacy of the analyses carried out. Despite the existence of noteworthy studies in the literature aimed at criticising these misuses (published specifically as improvement guides), the occurrence of statistical malpractice has to be overcome. Given the growing complexity of theories put forward in Psychology in general and in Clinical and Health Psychology in particular, the likelihood of these errors has increased. Therefore, the primary aim of this work is to provide a set of key statistical recommendations for authors to apply appropriate standards of methodological rigour, and for reviewers to be firm when it comes to demanding a series of sine qua non conditions for the publication of papers.

Data Mining: Machine Learning and Statistical Techniques

The interdisciplinary field of Data Mining (DM) arises from the confluence of statistics and machine learning (artificial intelligence). It provides a technology that helps to analyze and understand the information contained in a database, and it has been used in a large number of fields or applications. Specifically, the concept DM derives from the similarity between the search for valuable information in databases and mining valuable minerals in a mountain. The idea is that the raw material is the data to analyse, and we use a set of learning algorithms acting as diggers to search for valuable nuggets of information (Bigus, 1996). We offer an applied vision of DM techniques, in order to provide a didactic perspective of the data analysis process of these techniques. We analyze and compare the results from applying machine learning algorithms and statistical techniques, under DM methodology, in searching for knowledge models that show the structures and regularities underlying the data analysed. In this sense, some authors have pointed out that DM consists of “the analysis of (often large) observational datasets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” (Hand, Mannila & Smyth, 2001), or, more simply, “the search for valuable information in large volumes of data” (Weiss & Indurkhya, 1998), or “the discovery of interesting, unexpected or valuable structures in large databases” (Hand, 2007). Other authors define DM as “the exploration and analysis of large quantities of data in order to discover meaningful patterns and rules” (Berry & Linoff, 2004). These definitions make it clear that DM is an appropriate process for detecting relationships and patterns in large databases (although we point out that it can also be applied in relatively small databases). In this sense, the concept of Knowledge Discovery in Databases (KDD) has been frequently used in the literature to define this process (Han & Kamber, 2000, 2006; Hand et al., 2001), specifying that DM is a stage of the process, and highlighting the need for a previous stage of integration and collection of data (if we start with large raw databases), and also the stage of cleaning and preparing data (data pre-processing) before building descriptive/predictive models in the DM stage (applying suitable techniques to the analysis requirements). On the other hand, several authors have used the concept of DM (instead of KDD) to refer to the complete process (Bigus, 1996; Two Crows, 1999; Paul, Guatam & Balint, 2002; Kantardzic, 2003; Ye, 2003; Larose, 2005).

Socio-cognitive and personal characteristics of young offenders: a field study

Adolescence is characterized by a prevalence of risk-taking and the challenging of social norms which appears to bear relation to personal abilities and social cognitive deficits. With the aim of understanding this relationship a comparative study was undertaken with two groups of adolescents, one belonging to the standard population and one comprising young people who have been subject to social reform. In order to evaluate the variables involved, use was made of the Questionnaire for Evaluating Problems of Adolescents (Q-PAD). The results obtained show that both groups display significant differences in all the variables presented by the Questionnaire, with the exception of those relating to bodily dissatisfaction and conflict. Succinctly, it emerged that the young offenders presented emotional and interpersonal problems and were at risk of psychological disturbance. They demonstrated uncertainty about the future, liability to substance abuse and issues of self-esteem. These results suggest the need for programmes of prevention and intervention which specifically take into account these variables.

Can Attitudes toward Statistics and Statistics Anxiety Explain Students’ Performance? // ¿Pueden las actitudes hacia la estadística y la ansiedad estadística explicar el rendimiento de los estudiantes?

The aim of this study was to investigate the relationships between math background, trait anxiety, test anxiety, statistics anxiety, attitudes toward statistics and statistics performance in a sample of 472 university students enrolled in statistics courses of Health Sciences majors. A Structural Equation Modeling (SEM) approach showed the attitudes as the stronger direct predictor of performance, and played a full mediating role on the relationship between statistics anxiety and performance. Contrary to hypothesized, the direct contribution of math background, trait anxiety, and test anxiety to performance was non-significant. A final model posited that performance was positively and directly affected by attitudes, and in turn attitudes were positively influenced by math background and negatively affected by anxiety. Math background also appeared as negative predictor of anxiety. Finally, test anxiety was a positively direct predictor of statistics anxiety.