Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

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Oxford University Press, Mar 27, 2003 - Psychology - 644 pages
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Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit for:
Downloadable data sets
Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
Additional material for data analysis

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User Review  - tsryan - LibraryThing

I often find statistics textbooks largely incomprehensible, so it was a nice surprise to find this one, dedicated to a somewhat advanced sub-topic within statistics, to be a relatively easy read. It ... Read full review


A Framework for Investigating Change over Time
Exploring Longitudinal Data on Change
Introducing the Multilevel Model for Change
Doing Data Analysis with the Multilevel Model for Change
Treating TIME More Flexibly
Modeling Discontinuous and Nonlinear Change
Examining the Multilevel Models Error Covariance
Modeling Change Using Covariance Structure Analysis
Extending the DiscreteTime Hazard Model
DiscreteTime Hazard Model
Violations and Simple Solutions
and Simple Solutions
Simple Solution
Describing ContinuousTime Event Occurrence Data
Survivor Cumulative Hazard and KernelSmoothed Hazard
Fitting Cox Regression Models

A Framework for Investigating Event Occurrence
Describing DiscreteTime Event Occurrence Data
Functions and Median Lifetimes
Fitting Basic DiscreteTime Hazard Models
Model to Data
Model Fitting
Extending the Cox Regression Model

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