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Modeling Intraindividual Variability With Repeated Measures Data

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advanced repeated measures techniques
analysis
Antisocial Behavior
Baseline ASI
behavioral data statistics
Category=JMAL
Category=JMH
Category=PBT
Category=PBWH
components
Cov Parm Subject Estimate Standard
curve
Data Set
effects
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Fixed Effects Component
Growth Model
Hierarchical Model
Individual Growth Models
latent
latent curve analysis
Latent Curve Model
level
Linear Mixed Model
linear mixed models
Linear Model
longitudinal data analysis
Missing Data
Multilevel Model
multilevel modeling
Partial Autocorrelation Function
Partner Gender
Person Period Data Set
random
Repeated Measures
Repeated Measures Data
Residual Intercepts
Sample Autocorrelation Function
time series regression
Unconditional Growth Model
Unconditional Linear Growth Model
Unconditional Means Model
units
upper
variance
Variance Components

Product details

  • ISBN 9780805831252
  • Weight: 700g
  • Dimensions: 152 x 229mm
  • Publication Date: 01 Oct 2001
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable.

It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a "user-friendly" style such that even the "novice" data analyst can easily apply the techniques.

Each chapter features:

  • a minimum discussion of mathematical detail;
  • an empirical example applying the technique; and
  • a discussion of the software related to that technique.

Content highlights include analysis of mixed, multi-level, structural equation, and categorical data models. It is ideal for researchers, professionals, and students working with repeated measures data from the social and behavioral sciences, business, or biological sciences.
About
Scott L. Hershberger, D.S. Moskowitz