The survey instrument was developed through a factor analysis based upon the original 42 VCCS Management by Objectives goals as identified and developed by the VCCS Task Force on Management by Objectives. The reliability was established through a test/retest within the Virginia Community College System which produced a correlation coefficient of .98 between the two administrations of the instrument. Face validity was established based upon the development of the Management by Objectives goals specifically for the Virginia Community College System institutions by the VCCS Task Force on Management by Objectives. The findings of this study indicated that no statistically significant difference existed for either of the eight null hypotheses tested utilizing the SAS REGR analysis of variance procedure. The program produced overall multivariate analyses of variance for all but one comparison. Univariate analyses of variance were concomitantly produced per goal statement for each comparison. Statistical tests of ‘significance were conducted utilizing the .01 level of significance. Based upon the results of the data analysis, it was concluded that goal consonance existed between and among college and campus administrative groups (college-wide/central office and campus administrators) of the five Virginia multi-campus community colleges. / Ed. D.

Organizations have recognized usability engineering as a needed step in the development process to ensure the success of any product. As is the case in all competitive settings areas for improvement are scouted and always welcomed. In the case of usability engineering a lot of time, money, equipment, and other resources are spent to gather usability data to identify and resolve usability problems in order to improve their product. The usability data gained from the expenditure of resources is often only applied to the development effort at hand and not reused across projects and across different development groups within the organization. More over, the usability data are often used at a level that forces the organization to only apply the data to that specific development effort. However, if usability data can be abstracted from the specific development effort and analyzed in relation to the process that created and identified the data; the data can then be used and applied over multiple development efforts. The User Action Framework (UAF) is a hierarchical framework of usability concepts that ensures consistency through completeness and precision. The UAF by its nature classifies usability problems at a high level. This high level classification affords usability engineers to not only apply the knowledge gained to the current development effort but to apply the knowledge across multiple development efforts. This author presents a mechanism and a process to allow usability engineers to find insights in their usability data to identify both strengths and weaknesses in their process. In return usability practitioners and companies can increase their return on investment by extending the usefulness of usability data over multiple development efforts. / Master of Science

No

No

No

The exploration of complex systems is a fundamental pursuit in various scientific disciplines, includingphysics, biology, finance and engineering. The inherent complexity and dynamics within these systemspose significant challenges for traditional analytical methods. In recent years, the emergence of Topological Data Analysis (TDA) has provided a promising framework for uncovering hidden structures andpatterns in dynamic data sets. This thesis investigates the application of Topological Data Analysis to analyze system dynamics,aiming to enhance our understanding of their behavior. Through a detailed review of existing literature,we examine the theoretical foundations of TDA and its relevance to discrete and continuous processes.We discuss conceptual underpinnings of persistent homology, a key technique in TDA, and its potentialfor capturing essential features of system dynamics. By applying TDA to two distinct models, thestochastic ODE and the discrete logistic equation, we demonstrate its effectiveness in revealing underlyingstructures that traditional methods might overlook, thereby offering new insights into the analysis ofstochastic and discrete dynamical systems.

No description available.

Topological Data Analysis is a quickly expanding field but one particular subfield, multidimensional persistence, has hit a dead end. Although TDA is a very active field, it has been proven that the one-dimensional persistence used in persistent homology cannot be generalized to higher dimensions. With this in mind, progress can still be made in the accuracy of approximating it. The central challenge lies in the multiple persistence parameters. Using more than one parameter at a time creates a multi-filtration of the data which cannot be totally ordered in the way that a single filtration can. The goal of this thesis is to contribute to the development of persistence heat maps by replacing the persistent betti number function (PBN) defined by Xia and Wei in 2015 with a new persistence summary function, the accumulated persistence function (APF) defined by Biscio and Moller in 2016. The PBN function fails to capture persistence in most cases and thus their heat maps lack important information. The APF, on the other hand, does capture persistence that can be seen in their heat maps. A heat map is a way to visually describe three dimensions with two spatial dimensions and color. In two-dimensional persistence heat maps, the two chosen parameters lie on the x- and y- axes. These persistence parameters define a complex on the data, and its topology is represented by the color. We use the method of heat maps introduced by Xia and Wei. We acquired an R script from Matthew Pietrosanu to generate our own heat maps with the second parameter being curvature threshold. We also use the accumulated persistence function introduced by Biscio and Moller, who provided an R script to compute the APF on a data set. We then wrote new code, building from the existing codes, to create a modified heat map. In all the examples in this thesis, we show both the old PBN and the new APF heat maps to illustrate their differences and similarities. We study the two-dimensional heat maps with respect to curvature applied to two types of parameterized knots, Lissajous knots and torus knots. We also show how both heat maps can be used to compare and contrast data sets. This research is important because the persistence heat map acts as a guide for finding topologically significant features as the data changes with respect to two parameters. Improving the accuracy of the heat map ultimately improves the efficiency of data analysis. Two-dimensional persistence has practical applications in analyses of data coming from proteins and DNA. The unfolding of proteins offers a second parameter of configuration over time, while tangled DNA may have a second parameter of curvature. The concluding argument of this thesis is that using the accumulated persistence function in conjunction with the persistent betti number function provides a more accurate representation of two-dimensional persistence than the PBN heat map alone.

No description available.

Functional data analysis (FDA) studies data in the form of measurements over a domain as whole entities. Our first focus is on the post-hoc analysis with pairwise and contrast comparisons of the popular functional ANOVA model comparing groups of functional data. Existing contrast tests assume independent functional observations within group. In reality, this assumption may not be satisfactory since functional data are often collected continually overtime on a subject. In this work, we introduce a new linear contrast test that accounts for time dependency among functional group members. For a significant contrast test, it can be beneficial to identify the region of significant difference. In the second part, we propose a non-parametric regression procedure to obtain a locally sparse estimate of functional contrast. Our work is motivated by a biomedical study using Raman spectroscopy to monitor hemodialysis treatment near real-time. With contrast test and sparse estimation, practitioners can monitor the progress of the hemodialysis within session and identify important chemicals for dialysis adequacy monitoring. In the third part, we propose a functional data model for degradation analysis of functional data. Motivated by degradation analysis application of rechargeable Li-ion batteries, we combine state-of-the-art functional linear models to produce fully functional prediction for curves on heterogenous domains. Simulation studies and data analysis demonstrate the advantage of the proposed method in predicting degradation measure than existing method using aggregation method. / Doctor of Philosophy / Functional data analysis (FDA) studies complex data structure in the form of curves and shapes. Our work is motivated by two applications concerning data from Raman spectroscopy and battery degradation study. Raman spectra of a liquid sample are curves with measurements over a domain of wavelengths that can identify chemical composition and whose values signify the constituent concentrations in the sample. We first propose a statistical procedure to test the significance of a functional contrast formed by spectra collected at beginning and at later time points during a dialysis session. Then a follow-up procedure is developed to produce a sparse representation of the contrast functional contrast with clearly identified zero and nonzero regions. The use of this method on contrast formed by Raman spectra of used dialysate collected at different time points during hemodialysis sessions can be adapted for evaluating the treatment efficacy in real time. In a third project, we apply state-of-the-art methodologies from FDA to a degradation study of rechargeable Li-ion batteries. Our proposed methods produce fully functional prediction of voltage discharge curves allowing flexibility in monitoring battery health.