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ERIC Number: EJ1074501
Record Type: Journal
Publication Date: 2013
Abstractor: As Provided
Reference Count: 39
Quantitative Research Methods in Chaos and Complexity: From Probability to Post Hoc Regression Analyses
Gilstrap, Donald L.
Complicity: An International Journal of Complexity and Education, v10 n1-2 p57-70 2013
In addition to qualitative methods presented in chaos and complexity theories in educational research, this article addresses quantitative methods that may show potential for future research studies. Although much in the social and behavioral sciences literature has focused on computer simulations, this article explores current chaos and complexity methods that have the potential to bridge the divide between qualitative and quantitative, as well as theoretical and applied, human research studies. These methods include multiple linear regression, nonlinear regression, stochastics, Monte Carlo methods, Markov Chains, and Lyapunov exponents. A postulate for post hoc regression analysis is then presented as an example of an emergent, recursive, and iterative quantitative method when dealing with interaction effects and collinearity among variables. This postulate also highlights the power of both qualitative and quantitative chaos and complexity theories in order to observe and describe both the micro and macro levels of systemic emergence.
Descriptors: Educational Research, Social Science Research, Behavioral Science Research, Statistical Analysis, Research Methodology, Probability, Regression (Statistics), Monte Carlo Methods, Markov Processes, Mathematical Concepts, Physics, Difficulty Level
University of Alberta. 347 Education South, Edmonton, Alberta T6G 2G5, Canada. e-mail: firstname.lastname@example.org; Web site: https://ejournals.library.ualberta.ca/index.php/complicity
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Authoring Institution: N/A