The Santa Clara Strength of Religious Faith Questionnaire: Confirmatory Factor Analysis

Within contemporary psychology there is increasing interest in the role of religion on behaviour and psychological functioning. Such interest can be attested to by the growth in the number of pertinent books and articles that have been published and also the development of new self-report measures of religiosity. The Santa Clara Strength of Religious Faith Questionnaire is one such recently developed measure. It is a brief self-report measure comprising 10 items that were designed to measure strength of religious faith regardless of religious denomination or affiliation. Although Plante and Boccaccini (1997a, 1997b) have provided some preliminary evidence for the reliability and validity of the scale, the factor structure of the scale has not yet been examined. The aim of the present paper was to confirm the factor structure of the scale. The hypothesised one-factor model was tested using confirmatory factor analytic methods. Data from a sample of 106 Northern Irish undergraduate university students were examined using confirmatory factor analytic methods. A one-factor model was tested and accepted on the basis of fit statistics, therefore supporting the hypothesised unidimensional structure of the scale. The present results provide further evidence that the Santa Clara Strength of Religious Faith Questionnaire is psychometrically sound and therefore it can be recommended for further use by researchers interested in the construct of strength of religious faith.     

Curvature discontinuities are cues for rapid shape analysis

Using the visual search method, we show that stimuli that contain curvature discontinuities (i.e., points where the second derivative along an image contour is not defined) are easily found among stimuli containing only smooth changes in curvature. Curved stimuli that lack curvature discontinuities, however, are difficult to find among distractors that have them. These results suggest that the visual system detects and analyzes abrupt changes in curvature in the image quickly to extract vital information about the 3-D structure of the visual environment.     

Cognitive subprocesses of mental rotation: why is a good rotator better than a poor one?

The Vanderberg-Kuse Mental Rotation Test is a standard test of mental rotation ability. Recent experiments have demonstrated that mental rotation is a complex cognitive process wherein different subprocesses (focused attention, visual scanning, perceptual decision, visual memory) play important roles in performance. We classified the population as good and poor rotators by performance of mental rotation (ns = 47: 22 men and 25 women, respectively; mean age: 20.7 yr.). To examine differences cognitive subprocesses of mental rotation of these two groups were compared. There were significant differences between poor and good rotators in performance on Raven's test and the Pieron Focused Attention test scores. The good rotators scored better because their perceptual decision-analytical intelligence (Raven) and focused attention scores were higher.     

Patterns of Natural Language Use: Disclosure, Personality, and Social Integration

When people write about their deepest thoughts and feelings about an emotionally significant event, numerous benefits in many domains (e.g., health, achievement, and well-being) result. As one step in understanding how writing achieves these effects, we have developed a computer program that provides a "fingerprint" of the words people use in writing or in natural settings. Analyses of text samples indicate that particular patterns of word use predict health and also reflect personality styles. We have also discovered that language use in the laboratory writing paradigm is associated with changes in social interactions and language use in the real world. The implications for using computer-based text analysis programs in the development of psychological theory are discussed.     

The Conception of Synthetic Entities from a Personalist Perspective

Science and Engineering Ethics - Synthetic biology opens up the possibility of producing new entities not found in nature, whose classification as organisms or machines has been debated. In this...     

Combining genetic algorithms and the finite element method to improve steel industrial processes

Most of the times the optimal control of steel industrial processes is a very complicated task because of the elevated number of parameters to adjust. For that reason, in steel plants, engineers must estimate the best values of the operational parameters of processes, and sometimes, it is also necessary to obtain the appropriate model for steel material behaviour. This article deals with three successful experiences gained from genetic algorithms and the finite element method in order to solve engineering optimisation problems. On one hand, a fully automated method for determining the best material behaviour laws is described, and on the other hand we present a common methodology to find the most appropriate settings for two cases of improvement in steel industrial processes. The study of the three reported cases allowed us to show the reliability and effectiveness of combining both techniques.