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Full Product Details

Author:   John J. McArdle (University of Southern California, USA) ,  Gilbert Ritschard (University of Geneva, Switzerland)
Publisher:   Taylor & Francis Ltd
Imprint:   Routledge
Dimensions:   Width: 15.20cm , Height: 2.70cm , Length: 22.90cm
Weight:   0.748kg
ISBN:  

9780415817066

ISBN 10:   0415817064
Pages:   496
Publication Date:   27 August 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Part I: Methodological Aspects J.J. McArdle, Exploratory Data Mining Using Decision Trees in the Behavioral Sciences. G. Ritschard, CHAID and Earlier Supervised Tree Methods. J.Kopf, T. Augustin, C. Strobl, The potential of model-based recursive partitioning in the social sciences –Revisiting Ockham's Razor. A.M. Brandmaier, Timo von Oertzen, J.J. McArdle, U. Lindenberger, Exploratory Data Mining with Structural Equation Model Trees. G. Ritschard, F. Losa, P.Origoni, Validating Tree Descriptions of Women’s Labor Participation with Deviance-based Criteria. G.A. Marcoulides, W.Leite, Exploratory Data Mining Algorithms for Conducting Searches in Structural Equation Modeling: A Comparison of Some Fit Criteria. K.J. Grimm, N. Ram, M. P. Shiyko, L. L. Lo, A Simulation Study of the Ability of Growth Mixture Models to Uncover Growth Heterogeneity. R. Piccarreta, C.H. Elzinga, Mining for Association between Life Course Domains. G.Ritschard, R. Bürgin, M. Studer, Exploratory Mining of Life Event Histories. Part II: ApplicationsC.A. Prescott, Clinical versus Statistical Prediction of Zygosity in Adult Twin Pairs: An Application of Classification Trees. J.J. McArdle, Dealing with Longitudinal Attrition Using Logistic Regression and Decision Tree Analyses. J.J. McArdle, Adaptive Testing of the Number Series Test Using Standard Approaches and a New Decision Tree Analysis Approach. T. S. Paskus, Using EDM to Identify Academic Risk among College Student-Athletes in the United States. S. B. Scott, B. R. Whitehead, C. S. Bergeman, and L. Pitzer, Understanding Global Perceptions of Stress in Adulthood through Tree-Based Exploratory Data Mining. P. Ghisletta, Recursive Partitioning to Study Terminal Decline in the Berlin Aging Study. Y. Zhou, K.M. Kadlec, J. J. McArdle, Predicting Mortality from Demographics and Specific Cognitive Abilities in the Hawaii Family Study of Cognition. K. F. Widaman, K.J. Grimm, Exploratory Analysis of Effects of Prenatal Risk Factors on Intelligence in Children of Mothers with Phenylketonuria.

Reviews

The combination between theoretical/methodological issues with the empirical applications is excellent. ... It offers a wide range of research examples cutting across disciplines, data types, and units of analysis. ... Readers will be able to grasp the problems presented, relate them to their own research ... and apply the tools ... to their own data sets... I am thinking about creating a course on exploratory data analysis and I can see adopting this volume for that course. - Emilio Ferrer, University of California - Davis, USA [This] book will contribute significantly in making the field of Exploratory Data Mining more accessible to many researchers in the behavioral sciences disciplines including social sciences, medicine, and business related fields. ... Suitable for an advanced level research methods course ... I would strongly recommend it. - Riyaz Sikora, University of Texas at Arlington, USA The topics selected represent some of the most cutting edge approaches that can be used now and into the future. ... There is much to recommend about this book. George Marcoulides, University of California - Riverside, USA

Data mining emerges from several tracks within quantitative methodology, and requires broad methodological background with outstanding computer skills. McArdle and Ritschard are exactly the right scholars to edit this volume, which includes fascinating and modern data mining research. - Joseph L. Rodgers, Vanderbilt University, USA The richness and volume of data available to behavioral scientists has increased dramatically, creating opportunities for new discoveries and improved prediction models. This timely and innovative volume describes and illustrates the use of new statistical strategies for probing large and complex data sets. - Rick H. Hoyle, Duke University, USA Deliberately ignoring the boundaries between separate quantitative traditions and different social and behavioural sciences, this book is an essential reading on the potential of big data to change the way we study individuals, social relationships and societies. -Francesco C. Billari, University of Oxford, UK The combination between theoretical/methodological issues with the empirical applications is excellent. ... It offers a wide range of research examples cutting across disciplines, data types, and units of analysis. ... Readers will be able to grasp the problems presented, relate them to their own research ... and apply the tools ... to their own data sets. ... I am thinking about creating a course on exploratory data analysis and I can see adopting this volume for that course. - Emilio Ferrer, University of California - Davis, USA [This] book will contribute significantly in making the field of Exploratory Data Mining more accessible to many researchers in the behavioral [and] ... social sciences, medicine, and business. ... Suitable for an advanced level research methods course...I would strongly recommend it. - Riyaz Sikora, University of Texas at Arlington, USA

Author Information

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.

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