Longitudinal Models in the Behavioral and Related Sciences
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A. Scherpenzeel
Ab Mooijaart
advanced statistical methods
Albert Satorra
Alexander Gattig
Autoregressive Cross-lagged Model
behavioral data modeling
Category=JMB
Category=JNC
Christian Steglich
EDM
Eldad Davidov
Elmar Schlueter
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eq_nobargain
eq_non-fiction
eq_society-politics
Fumiaki Hamagami
Garry Dowse
General Growth Mixture Model
growth curve analysis
Growth Curve Model
Hermann Singer
Ingmar Visser
Iona Mccarthy
Jeroen K. Vermunt
Johan Oud
John J. Mcardle
Jost Reinecke
Juan Carlos Bou
Julia Simonson
Kees Van Montfort
Kevin J. Grimm
KF
Latent Basis Model
latent class analysis
Latent Curve Model
Latent Difference Score Models
Latent Growth Curve
Latent Growth Curve Model
Latent Intercepts
Latent Slope Factor
Latent State Trait Models
LC Growth Model
LC Model
LCGA
LDS
Lm Model
longitudinal data analysis applications
Maartje E. J. Raijmakers
Mark De Rooij
Measurement Occasions
Michael Schweinberger
Mixture Growth Model
MTMM Model
network evolution models
Nicholas T. Longford
Peter Schmidt
Positive Cross-lagged Effects
Random Intercepts
Shelley Blozis
Sigma Points
State Space Model
stochastic process modeling
Sy-Miin Chow
Tom Snijders
Uwe Engel
Verena D. Schmittmann
W.E. Saris
Product details
- ISBN 9780805859133
- Weight: 771g
- Dimensions: 152 x 229mm
- Publication Date: 30 Oct 2006
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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This volume reviews longitudinal models and analysis procedures for use in the behavioral and social sciences. Written by distinguished experts in the field, the book presents the most current approaches and theories, and the technical problems that may be encountered along the way. Readers will find new ideas about the use of longitudinal analysis in solving problems that arise due to the specific nature of the research design and the data available.
Longitudinal Models in the Behavioral and Related Sciences opens with the latest theoretical developments. In particular, the book addresses situations that arise due to the categorical nature of the data, issues related to state space modeling, and potential problems that may arise from network analysis and/or growth-curve data. The focus of part two is on the application of longitudinal modeling in a variety of disciplines. The book features applications such as heterogeneity on the patterns of a firm’s profit, on house prices, and on delinquent behavior; non-linearity in growth in assessing cognitive aging; measurement error issues in longitudinal research; and distance association for the analysis of change. Part two clearly demonstrates the caution that should be taken when applying longitudinal modeling as well as in the interpretation of the results. This new volume is ideal for advanced students and researchers in psychology, sociology, education, economics, management, medicine, and neuroscience.
Kees van Montfort, PhD, is a Full Professor of Quantitative Research Techniques at the Free University in Amsterdam and at the Nyenrode Business University in the Netherlands. Johan Oud is an Associate Professor in Longitudinal Data Analysis at the Behavioural Science Institute at Radboud University Nijmegen, in the Netherlands. Albert Satorra is a Full Professor of Statistics at the University of Pompeu Fabra in Barcelona, Spain.
