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Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences
Paperback
Series: Quantitative Methodology
DatabasesGeneral MathematicsGeneral Psychology
ISBN13: 9780415817097
Publisher: Routledge
Published: Aug 27 2013
Pages: 496
Weight: 1.46
Height: 0.90 Width: 6.07 Depth: 8.98
Language: English
This book reviews the latest techniques in exploratory data mining (EDM) for the analysis of data in the social and behavioral sciences to help researchers assess the predictive value of different combinations of variables in large data sets. Methodological findings and conceptual models that explain reliable EDM techniques for predicting and understanding various risk mechanisms are integrated throughout. Numerous examples illustrate the use of these techniques in practice. Contributors provide insight through hands-on experiences with their own use of EDM techniques in various settings. Readers are also introduced to the most popular EDM software programs. A related website at http: //mephisto.unige.ch/pub/edm-book-supplement/offers color versions of the book's figures, a supplemental paper to chapter 3, and R commands for some chapters.
Editors McArdle and Ritschard taught the Exploratory Data Mining Advanced Training Institute of the American Psychological Association (APA). All contributors are top researchers from the US and Europe. Organized into two parts--methodology and applications, the techniques covered include decision, regression, and SEM tree models, growth mixture modeling, and time based categorical sequential analysis. Some of the applications of EDM (and the corresponding data) explored include:
selection to college based on risky prior academic profiles
the decline of cognitive abilities in older persons
global perceptions of stress in adulthood
predicting mortality from demographics and cognitive abilities
risk factors during pregnancy and the impact on neonatal development
Intended as a reference for researchers, methodologists, and advanced students in the social and behavioral sciences including psychology, sociology, business, econometrics, and medicine, interested in learning to apply the latest exploratory data mining techniques. Prerequisites include a basic class in statistics.
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