Terri Lynch-Caris

Establishing reference values for cervical spine range of motion in pre-pubescent children

Medical professionals, physical therapists, product designers, and computational models all use cervical spine range of motion reference values. To support these functions, researchers have collected a plethora of data to determine the normal range of motion of the cervical spine of adult subjects. However, little to no data exists for subjects under the age of 14. This study utilized the cervical range of motion device, referenced with respect to the Frankfort Plane, to measure the active cervical spine range of motion in all three cardinal planes of the human body, for 106 subjects whose ages ranged from 8 to 10 years. The active range of motion for flexion, extension, lateral extension, and rotation was calculated as 66+/-13 degrees , 85+/-14 degrees , 58+/-8 degrees , and 77+/-7 degrees , respectively, using linear statistics. The observed data significantly differed from the published American Medical Association guidelines for adults but fell within the range of the reference values for 10 year olds. Stratifying and analyzing the range of motion data with respect to gender yielded no significant effect. Appendix A analyzes the data using angular statistics, and produces virtually identical results as those from linear statistics.

Undergraduate course in environmental design and manufacturing

This paper is a progress report on the development of an undergraduate course in environmental design and manufacturing. The importance of the topic is clear from National Academy of Engineering statements regarding the need to incorporate content on sustainable technology. The multidisciplinary team includes academic faculty from engineering, science, liberal studies, and business in collaboration with industry partners. The teams approach is to create a case-based course by adapting existing educational modules from Ford Motor Company. The group has received National Science Foundation funding for this project (DUE-0511322), and the project is currently in its beginning phases.

Quantifying R2 bias in the presence of measurement error

Measurement error (ME) is the difference between the true unknown value of a variable and the data assigned to that variable during the measuring process. The multiple correlation coefficient quantifies the strength of the relationship between the dependent and independent variable(s) in regression modeling. In this paper, we show that ME in the dependent variable results in a negative bias in the multiple correlation coefficient, making the relationship appear weaker than it should. The adjusted R 2 provides regression modelers an unbiased estimate of the multiple correlation coefficient. However, due to the ME induced bias in the multiple correlation coefficient, the otherwise unbiased adjusted R 2 under-estimates the variance explained by a regression model. This paper proposes two statistics for estimating the multiple correlation coefficient, both of which take into account the ME in the dependent variable. The first statistic uses all unbiased estimators, but may produce values outside the [0,1] interval. The second statistic requires modeling a single data set, created by including descriptive variables on the subjects used in a gage study. Based on sums of squares, the statistic has the properties of an R 2: it measures the proportion of variance explained; has values restricted to the [0,1] interval; and the endpoints indicate no variance explained and all variance explained respectively. We demonstrate the methodology using data from a study of cervical spine range of motion in children.