TiberioST122004.pdf (1.25 MB)
Stochastic and Deterministic Differential Equation Modeling: The Accuracy of Recovering Dynamic Model Parameters of Change
thesis
posted on 2004-12-17, 00:00 authored by Stacey S. TiberioThe analysis of change is an extensively studied and complicated topic in the social sciences. A variety of statistical methods that may conceptualize change in potentially many different ways may be used to assess change in a phenomenon over a specified time interval. Resultantly, conceptual differences in defining change will be addressed. In addition, the major advantages of utilizing continuous time differential equation models will be illustrated, and the classification criteria of such models will be discussed. Broadly speaking, differential equations may be classified according to at least five dimensions; one of these dimensions is the distinction between deterministic and stochastic models. The primary difference between these two models is whether or not random fluctuations of the process itself are explicitly accounted for in the differential equation. The accuracy of the deterministic and stochastic differential equation models in recovering simulated dynamic model parameters of change is examined over a wide variety of conditions likely to occur in practice for social science phenomena. Results are summarized in terms of practical implications and general guidelines for employing these two types of continuous time differential equation modeling techniques.
History
Date Modified
2017-06-02Research Director(s)
Steven M. Boker, Ph.D.Committee Members
Scott E. Maxwell, Ph.D. Cindy S. Bergeman, Ph. D.Degree
- Master of Arts
Degree Level
- Master's Thesis
Language
- English
Alternate Identifier
etd-12172004-133613Publisher
University of Notre DameProgram Name
- Psychology
Usage metrics
Categories
No categories selectedLicence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC