Quantitative Research Methods in Chaos and Complexity: From Probability to Post Hoc Regression Analyses

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dc.contributor.authorGilstrap, Donald L.
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.date.accessioned2018-11-20T15:47:20Z
dc.date.available2018-11-20T15:47:20Z
dc.date.issued2013
dc.description.abstractIn 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.en_US
dc.description.urihttps://journals.library.ualberta.ca/complicity/index.php/complicity/article/view/20400
dc.format.mimetypeapplication/pdf
dc.identifier.citationGilstrap, Donald L. (2013): Quantitative Research Methods in Chaos and Complexity: From Probability to Post Hoc Regression Analyses. Complicity: An International Journal of Complexity and Education, 10 (1/2).
dc.identifier.urihttp://ir.ua.edu/handle/123456789/4961
dc.subjectcomplexity theory
dc.subjectchaos theory
dc.subject.lcshEducation—Research
dc.titleQuantitative Research Methods in Chaos and Complexity: From Probability to Post Hoc Regression Analysesen_US
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