A comparison of learning disabled college students' achievement from WRAT and PIAT grade,standard, and subtest scores

Although there have been a number of studies that compared the test results of the Wide Range Achievement Test (WRAT) and the Peabody Individual Achievement Test (PIAT), none had been accomplished with a group of college-aged learning disabled students. Indeed, few researchers had investigated populations over 16 years of age, and none had compared grade scores and standard scores with appropriate subtest scores as derived from these two instruments. With the recent emphasis on postsecondary education for the learning disabled, it is deemed critical that researchers should develop a data base for study of this population. The current research demonstrated that although both tests purport to measure academic achievement in reading, spelling, and arthmetic, in reality, significantly different scores are derived when WRAT (1965, 1978) norms are compared to PIAT (1970) norms as stated in grade scores and standard scores. These significant differences in the grade and standard scores clearly indicate that the WRAT and the PIAT, particularly in regard to arthmetic and to a lesser degree to reading, are not interchangeable instruments of academic achievement.     

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.