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Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences

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ACO Algorithm
advanced behavioral data mining techniques
BIC Value
Category=GPS
Category=JH
Category=JHBC
Category=JMB
CFA Model
classification trees
decision
Decision Tree Analysis
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Exploratory Data Mining
forest
Full Scale IQ Score
GMM
growth mixture modeling
IQ Point
LGM
Life Event
Linear Latent Growth Curve Model
Linear Spline Model
logistic
longitudinal research methods
Low PHE Diet
Mars Model
Multivariate Adaptive Regression Splines
node
partitioning
PHE Level
Pheromone Level
PROC NLIN
psychological data interpretation
random
Random Forest
recursive
recursive partitioning
regression
Regression Tree
risk factor analysis
Tabu Algorithms
Tabu Search
terminal
Terminal Nodes
tree
Variable Importance Measure

Product details

  • ISBN 9780415817097
  • Weight: 920g
  • Dimensions: 152 x 229mm
  • Publication Date: 27 Aug 2013
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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.

The results of EDM analyses can be perilous – they are often taken as predictions with little regard for cross-validating the results. This carelessness can be catastrophic in terms of money lost or patients misdiagnosed. This book addresses these concerns and advocates for the development of checks and balances for EDM analyses. Both the promises and the perils of EDM are addressed.

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.

About
John J. McArdle is Senior Professor of Psychology at the University of Southern California where he heads the Quantitative Methods training program. Gilbert Ritschard is Professor of Statistics and project leader at the Swiss National Center of Competence in Research LIVES.