Psychometric reevaluation of the scientific attitude inventory‐revised (SAI‐II)

The central purposes of this study were to review the development and evolution of the Scientific Attitude Inventory (SAI) and then reevaluate the psychometric properties of the revised form of the SAI, the Scientific Attitude Inventory II (SAI‐II). The SAI‐II was administered to a convenience sample of 543 middle and high school students from five teachers in four schools in four school districts in San Antonio, Texas, at the beginning of the 2004–2005 school year. Confirmatory factor analysis on the full data set failed to support the existence of a 12‐factor structure (as proposed by the scale developers) or a one‐factor structure. The data were then randomly divided into exploratory [exploratory factor analysis (EFA)] validation and confirmatory [confirmatory factor analysis (CFA)] cross‐validation sets. Exploratory and confirmatory models yielded a three‐factor solution that did not fit the data well [χ² (321) = 646, p < .001; RMSEA = .061 (.90 CI = .054–.068); and CFI = .81]. The three factors were labeled “Science is About Understanding and Explaining” (13 items), “Science is Rigid” (6 items), and “I Want to Be a Scientist” (8 items). The α‐coefficients for these three factors ranged from 0.59 to 0.85. Whether these identified subscales are valid will require independent investigation. In this sample, and consistent with prior publications, the SAI‐II in its current form did not have satisfactory psychometric properties and cannot be recommended for further use. © 2008 Wiley Periodicals, Inc. J Res Sci Teach 45: 600–616, 2008     

Comparison of methods for determining power generated on a rope-braked cycle ergometer during low-intensity exercise

This study compares the instrumentation and analysis techniques used when determining the power expended pedalling a rope-braked ergometer manufactured by Monark (Sweden) during a low intensity test. Power values were generated by eight subjects. The instrumentation consisted of load cells to measure the rope brake forces, a tachometer to measure the flywheel velocity and instrumented pedal cranks manufactured by Schoberer Rad Messtechnik (SRM). The subjects pedalled a rope-braked ergometer at 60 rev min^-1, against a resistance of 3 kg, for 5 minutes. Three different measurements of the mean power were recorded and these were compared with the value given by Monark. The SRM cranks provided two sets of results using different software packages supplied with the cranks. SRM standard software is used for taking measurements during training and cycle races over long time periods. An additional piece of software is provided by SRM called Ptnew, which gives readings of torque and pedal cadence over periods up to 30 seconds. Using the values supplied by Monark each subject generated 180 W of power. The mean power for the eight subjects, measured using the SRM cranks, was 170.36 W (SD 4.11) using the alternative SRM software (Ptnew) over a 30 second period and 173.68 W (SD 2.21) using the standard SRM software. From the direct measurement of the brake forces and flywheel velocity the mean power across the eight subjects was 148.90 W (SD 5.89). The SRM cranks measure the input power, whereas the direct measurement system measures the power output excluding mechanical losses. These values give a figure for the mechanical efficiency for the roped-braked ergometer of 88%. It was found that Monark overestimates the power generated by the subjects when compared with both the SRM systems and the direct measurement instrumentation.     

Predicting ground reaction forces in running using micro-sensors and neural networks

Measurement of ground reaction force (GRF) in running provides a direct indication of the loads to which the body is subjected at each foot-ground contact, and can provide an objective explanation for performance outcomes. Traditionally, the collection of three orthogonal component GRF data in running requires an athlete to complete a series of return loops along a laboratory based runway, within which a force platform is embedded, in order to collect data from a discrete footfall. The major disadvantages associated with this GRF data collection methodology include the inability to assess multiple consecutive foot contacts and the fact that measurements are typically confined to the laboratory. The objective of this research was to investigate the potential for wearable instrumentation to be employed, in conjunction with artificial neural network (ANN) and multiple linear regression (MLR) models, for the estimation of GRF in middle distance running. A modular wearable data acquisition system was developed to acquire in-shoe force (ISF) data. Matched data sets from wearable instrumentation (source data) and force plate (target data) records were collected from elite middle-distance runners under controlled laboratory conditions for the purposes of ANN and MLR model development (MD) and model validation (MV). In terms of statistical measures of prediction accuracy the MLR model was found to provide a superior level of accuracy for the prediction of the vertical and medio-lateral components of GRF and alternatively, the ANN model provided the most accurate predictions of the anterior-posterior component of GRF. The prediction accuracy of each component of GRF was found to be governed by the inherent signal variability, in which case the vertical and anterior-posterior components were more reliable and subsequently predicted significantly more accurately than the medio-lateral component. The emerging capability for obtaining continuous GRF records from wearable instrumentation has the potential to permit unprecedented quantification of training stress and competition demands in running.     

Government, Higher Education and the Industrial Ethic

The argument of this paper is that there have always been, and still remain, fundamental differences between the purposes of industry and those of higher education. Industry's concern is to make a profit, universities are concerned with open enquiry and intellectual freedom. The values of the two are incommensurable, and it is important that these conflicts of values are not obscured by ambiguity of language or by external pressures.
For more than a decade the British government has sought to increase the competitiveness of industry, and has initiated many changes in universities which it has linked to this policy. Three such developments are critically discussed: the enterprise in higher education initiative, academic audit and assessment of teaching quality and the recent White Paper on fundamental research. It is argued that the tendency of each of these is to cause the merging of business values with those of higher education. If this tendency proceeds unchecked universities will no longer be able to fulfil their vital rôle in a free society - the advancement of new and controversial ideas and the education of their students to think critically and autonomously.